Table of contents

Our recent developed filter method (Phys. Rev E96(3), 033302, 2017) is applied here to investigate the energy spectrum and their corresponding wave function of one dimensional crystal. The periodic one dimensional potential is modelled by using one dimensional periodic harmonic oscillator, with variation on oscillator potential depth, quasi-potential depth, and crystal width. For energy less than the potential depth of the oscillator, the computational results reveal that the periodic harmonic oscillator produces a discrete spectrum, as the energy spectrum of a single harmonic potential. However, for energy almost equal to or greater than the depth of the potential oscillator, the periodic harmonic oscillator demonstrates the existence of pattern similar to energy band in crystal.

According to quantum mechanics, electrons do not have a fixed position in an atom, and therefore orbitals have no definite radii. However, electrons have characteristic wave functions from which the radius of their orbits can be calculated or averaged. Depending on the average method, there are three most popular expressions for orbital radius, namely the average radius, the root mean square (rms) radius, and the most probable radius. Unfortunately, for hydrogen atom, none of those three radii is equal to the classical radius, even for large principal quantum numbers called the classical regime. Here, by using energy analysis, we propose a harmonic radius and show that the results well agree with the classical radius for each principal and orbital quantum numbers.

Hypertension is an increase in blood pressure that increases to a target organ, such as stroke, coronary heart disease, right ventricular hypertrophy. Hypertension occurs if the blood pressure reaches 140 mmHg or more and diastole reaches 90 mmHg or more. According to WHO, from 50% of hypertensive patients recovering, only 25% received treatment, and only 12.5% could be treated well. Nationally, 25.8% of Indonesia's population suffers from hypertension. In this study, we modeled the risk of hypertension by considering age, heart rate, family hypertension, stress levels, and the body's future index as factors that influence the risk of hypertension. The cross-sectional survey was conducted in August 2018 at the Surabaya Hajj Hospital. Based on previous research the method used is logit and gompit logistic regression method, but the results obtained are not maximal. Therefore, in this study the researchers proposed a method for constructing hypertension risk factor modeling using a nonparametric application using a penalized spline estimator. The result of classification accuracy by using non-parametrical is 96%. Based on the result, we conclude that non-parametrical approach has better than outcome so that it can be used to modelling the risk of hypertension.

The bivariate probit regression model is a probit regression model consisting of two response variables with errors between the two variables correlate each other. The correlation between the two response variables can occur as a result of the presence of endogeneity, a condition in which a response variable becomes an exogenous variable in another response variable. Besides, the important issue that cannot be underestimated is undetectable nonlinear relationships between response variables and predictors, especially discrete or continuous predictor variables. The bivariate probit regression that does not ignore endogeneity cannot detect the nonlinear relationships between response variables and predictors, so one of the regression models that can overcome the problem is bivariate probit regression model with a semiparametric approach. The first step in semiparametric bivariate probit modeling is testing the hypothesis of exogeneity to determine whether there is a case of endogeneity or not. The exogenous test used in this study is the Lagrange Multiplier (LM) and Likelihood Ratio (LR) test. The data used in this study consisted of two binary categorical response variables, they are parity status of the mother and basic immunization giving to infants in North Kalimantan Province in 2017. The results of the exogenous test using the LM test and LR test stated that there was a significant correlation between response variables. The AIC value of the semiparametric bivariate probit model is 1301.602, while the bivariate probit model produces AIC of 1316.789, so it can be concluded that the semiparametric bivariate probit model provides better modeling results than the bivariate probit model.

Computational models are used to help us to understand the mechanisms of a complex process in nature. Before building the model, we need to know the characteristic of samples which are described in the form of measure named parameters. Usually, the value of parameters is unknown and we need to investigate those value to know the compatibility between the artificial model and real circumstances. Many optimization methods have been introduced to estimate those parameters, but some of them meet the difficulties caused by the nonlinear type of function model. Many objective functions of the estimation parameter are multimodal, high dimensional, and have many local optima, so the estimation process using traditional optimization method is not suggested. In this article, we use Grey Wolf Optimizer (GWO), as one of the metaheuristic artificial intelligence algorithm, which is inspired by leadership hierarchy and hunting behavior of a pack of wolves. GWO is applied to estimate the parameters in a model of enzymatic reaction in biodiesel synthesis. Biodiesel is renewable fuel that can solve the energy crisis and pollution. While the process of biodiesel synthesis occurs, some enzyme in the biodiesel substances react to each other and it can be modeled into ODEs (Ordinary Differential Equations) system. The kinetic parameters inside them are needed to be estimated. After the parameter are estimated, the fourth-order Runge-Kutta method is used to solve the system. The result is evaluated by analyzing the objective function which minimizes the Sum of Squared Errors (SSE). The small value of SSE and the narrow range of both parameter of model and estimation shows that GWO is effective to be the proposed method for parameter estimation and model selection problems.

Along with the increase in population and industry in many countries, the fuel oil demand also increases. Petroleum exploration on a large scale will accelerate the depletion of petroleum reserves. One alternative to meet fuel needs is the discovery of biodiesel which is renewable alternative energy. Synthesis biodiesel is carried out through an enzymatic reaction. In the enzymatic reaction model making biodiesel, there are parameters that must be estimated. The estimated parameters of the enzymatic reaction model will determine the success of the reaction. The parameter estimation of the enzymatic reaction model can be done using local optimization or global optimization algorithms, but the local optimization algorithm has a major disadvantage, the optimal value obtained is the local optimal value. Genetic algorithms are global optimization algorithms that are capable of working on high-dimensional problems. The success of genetic algorithms is determined by chromosome models, crossover operations, and mutation operations. The use of improper crossover operations often produces local optimum solutions. There are various types of crossover operation, each of which has weaknesses and advantages. This paper studies the parameters estimation of the enzymatic reaction model for biodiesel synthesis by using genetic algorithms with some crossover operation.

Three industrial revolutions are known to have been able to improve the welfare of the community. The fourth industrial revolution or industry 4.0 made many studies carried out on plans, implementations, and other actions that will affect the community. Some industries also began competing to apply industry 4.0 in their systems, including case in this study. This study was conducted in one of the port terminals that are known to be the one and only port that has implemented semi-automatic technology and environmentally friendly in the developing countries. The port operates semi-automatically, so the operating system uses computerization and minimal manpower. As a modern port, this port is equipped with advanced technology, such as automated stacking cranes, ship to shore, and grab ship un-loader, CNG trucks, combined terminal tractors, and others. Port working system is also different from the other ports where only equipment and vehicles that fuelled electricity and gas are allowed to operate in this port, so they also are known as green-ports. Based on preliminary research that had been done, even though the technology and work systems that are applied are sophisticated, but it was known that there were still some occupational accidents that should not have happened, especially in the ship to shore area. The aim of this research was conducted to identify the probability of hazards that occur based on historical data and minimize the risk of further occupational accidents using risk analysis and modelling. Statistical, mathematical, and computational approaches were carried out to obtain risk quantification and develop risk mitigation and response strategies. Thus, the results of the study are expected to help this port as a pilot green-port for other ports, so that it will have a massive positive impact on the change in a more environmentally friendly transportation system.

Cholesterol is a lipid (fat) produced by the liver and is required to build and maintain cell membranes. Cholesterol is also important for the metabolism of fat soluble vitamins. This important lipid is found in human blood. Excess cholesterol (high cholesterol) can cause health problems such as being a factor of coronary heart disease that responsible for the heart attacks, liver or kidney disease. Observation of iris pattern can detect several types of diseases, one of which is high cholesterol. The purpose of this research is to detect whether someone is exposed to high cholesterol or not, through iris images based on firefly algorithm, simulated annealing, and radial basis function. Firefly algorithm and simulated annealing are used in the unsupervised learning process in radial basis function neural networks. The stages of high cholesterol detection process are images processing namely grayscale process, thresholding, histogram equalization, segmentation, and detection process is using radial basis function neural network. The percentage success rate of the recognition pattern of iris images for detecting high cholesterol is 89%.

Acceptance of new students at public universities through the national written test is based on the total score and the capacity of the study program. This causes the study program accepts several students who have low scores on the main subject of the study program. The purpose of this study is to find the best method in predicting the probability of being accepted on the national written test and find the minimum score for each subject that must be achieved by participants to be accepted at a public university. There are two classification methods in statistics that are studied to overcome this problem, i.e. logistic regression and random forest. The results showed that the best logistic regression model had an accuracy of 97.11 percent, whereas the random forest method had an accuracy of 96.59 percent. Furthermore, the minimum score for each subject was developed based on the univariate logistic regression model.

Glaucoma is an eye disease characterized by progressive deterioration of the optic nerve head and a broad view that can cause blindness. The Population Based Survey in 2010 indicates that glaucoma was the second leading cause of blindness after cataracts, which was about 8% of 36 million sufferers of blindness worldwide. Symptoms of glaucoma that arise usually cannot be felt directly. So it is necessary to do an eye examination to find out glaucoma, one of which is to look at the size of the optic disk in the digital fundus photo. The previous studies about glaucoma identification were done by using mathematical computation approach that have still not satisfied. Therefore, in this study we propose a new method, i.e., statistical modelling approach to identify glaucoma. In statistical modelling, there are two approaches, i.e., parametrical approach, and non-parametrical approach based on penalized spline estimator. The result of classification accuracy by using parametrical and non-parametrical approaches are 73.3% and 93.33%, respectively. Based on the result, we conclude that non-parametrical approach has better outcome so that it can be used to identify glaucoma on fundus retinal image.

Acute sinusitis is an inflammation of the sinus which causes the cavity around the sinus to swells due to accumulated mucus. It makes the patient experience difficulty in breathing through the nose. Generally, it is caused by the common cold, and in most cases, the patient recovers within seven to ten days. However, persistent acute sinusitis can cause severe infections and other complications. Therefore, it requires timely detection and more accurate method of classification. Many techniques have been used to classify acute sinusitis but, in this study, the machine learning methods which includes Kernel Spherical K-Means (KSPKM) and Support Vector Machine (SVM) was applied. SPKM is the application of K-Means, in this research, it was modified by changing the inner product with kernel function to ensure linear data separation on higher dimensions for the maximization of SPKM performance. The SVM is a binary classification method that helps to create a model with good generalization ability. We used CT scan result data from RSCM, Central Jakarta. Simulations were performed with different percentage of training data. The results were compared in terms of Accuracy and Running Time. The score showed that the performance of KSPKM attained an accuracy rate of 97%, while SVM reached 90%.

Validation in statistical modeling becomes a very important part to get information on how well the model has been built. Algorithm of Continuous Ranked Probability Score (CRPS) is a validation method of goodness of fit model in statistical modeling. A model that has a small CRPS value and has a small statistical significance, then the model is declared fit for data. Conversely, if a model has a large CRPS value, then the model is declared not fit for data. Several applications of the CRPS Algorithm have been developed for unimodal distribution models. Bayesian mixture is a modeling with Bayesian approach where data has a multimodal distribution. Characteristics of multimodal distribution are owned by microarray data in Indonesia, namely data on gene expression differences for several gene IDs from Chickpea plants in Indonesia. The purpose of this study was to obtain a performance from the Continuous Ranked Probability Score (CRPS) Algorithm as a goodness of fit model method in Bayesian Mixture Model (BMM) modeling for microarray data in Indonesia in a series of activities to find new varieties of Chiekpea plants that are resistant to attack by pathogenic fungal diseases Ascochyta Rabiei. The results of this study have succeeded in establishing the Algorithm of Continuous Ranked Probability Score (CRPS) for the distribution of normal mixture for data on gene expression differences of Chickpea plants in Indonesia as a result of microarray experiments with Bayesian approaches. BMM modeling on microarray data is declared fit because it has a small average value of CRPS, which is 0.0412 to 0.385.

The spread of the influenza virus with disease resistance has been modeled by the SEIR epidemic model. The model is of the form a system of four nonlinear differential equations where its exact solution is difficult to be determined. In this paper, the model is solved by a multi-step differential transform method (MS-DTM), which is a semi-analytical method. In this case, the domain of computation is divided into a finite number of sub-intervals. At each sub-interval, we apply DTM and continuity condition to ensure the continuity solutions. To see the effectiveness of the MS-DTM, we perform some comparative study between the MS-DTM and the MATLAB ode45 routine. Our MS-DTM solutions have good agreement with those of ode45 routine. The accuracy of MS-DTM can be improved by taking a smaller step size or increase the number of terms in each subinterval.

The foreign tourist's data are count time series data that contains the discrete value. Poisson autoregressive (AR) and Negative Binomial AR are time series models used for forecasting count data. The number of foreign tourist arrivals is influenced by the series of inputs called interventions, such as the existence of bomb terror and tourism promotion. This research aims to forecast the number of foreign tourists visiting Indonesia by nationality, 2019. The number of tourist arrivals from Bahrain and Singapore represents low count data and high count data, respectively. This work employs intervention analysis on count time series model and intervention analysis on ARIMA. Intervention on Poisson AR is the best model for forecasting the number of tourist arrivals from Bahrain and Singapore to Indonesia, 2019.

Awareness of seismicity activity rates could be learned from modeling the earthquake events by utilizing the record of seismicity events data in NTB over time which is associated with count time series data. Poisson Hidden Markov Model (PHMM) has been widely applied in various fields, including earthquake event. Therefore, it would be interesting to implement PHMM on earthquake case in NTB. The data can be analyze using PHMM as we can ignore the over-dispersion and dependency relationship among data. The model is the development of Markov Model that consists of (a) observed state, which can be observed directly and (b) hidden state, which cannot be observed directly because it is hidden. Hidden state in this research is defined as seismicity activity rates classified into a low rate and high rate (2 states). The count time series data of earthquake events will be more informative when it is classified into the seismicity activity levels. This research applied earthquake event data (magnitude ≤ M4.7 and depth < 60 km) from January 2009 until September 2018, collected from USGS (United States Geological Survey). The parameter estimation method used in this research is the Bayesian method. The objective of this research is to obtain parameters of 2 state Poisson Hidden Markov Model using the Bayesian approach. Model validation measured by MAE (Mean Absolute Error). Based on the result, the average earthquake cases caused by low seismicity activity rate in NTB over time is 1 event whilst the high rate is 18 events. The probability of low seismicity activity rate influenced by the previous rate and the long run behavior (steady state) in NTB is still larger than the high rate. The achieved two-state PHMM is suitable for modeling the earthquake case in NTB with MAE values of 0.4079.

Ischemic Stroke is a condition whereby the blood supply to the brain is disrupted or reduced due to a blockage and if it is not treated immediately will cause the death of the brain. A decrease in blood flow resulting in dead brain tissue can be called an infarction. The classifications of infarction help the health sector in detecting ischemic stroke in patients. In medicine, CT scans can be used to identify Infarctions and for detecting Ischemic Stroke in patients. Therefore, studying the CT scans is crucial in helping doctors obtain functional information about the surrounding brain tissues which will be used for detecting infarction in the brain. Since it is important to pay more attention at the time of choosing the best method that gives the best results, therefore this study proposes to compare between two types of methods, Gaussian Support Vector Machine (Gaussian SVM) and Cubic Support Vector Machine (Cubic SVM). The Cubic SVM could be an efficient method for infarction classification with accurate performances as high as 80%.

The local stability of the Rosenzweig-MacArthur predator-prey system with Holling type-II functional response and stage-structure for prey is studied in this paper. It is shown that the model has three equilibrium points. The trivial equilibrium point is always unstable while two other equilibrium points, i.e., the predator extinction point and the coexistence point, are conditionally stable. When the predation process on prey increases, the number of predator increases. If the predation rate is less than or equal to the reduction rate of the predator, then the predator will go to extinct. By using the Routh-Hurwitz criterion, the local stability of the interior equilibrium point is investigated. It is also shown that the model undergoes a Hopf-bifurcation around the coexisting equilibrium point. The dynamics of the system are confirmed by some numerical simulations.

Building a probability function to get the best answer between events in many review problems is a very complex one. In this study, several events were given as a case study to find a solution, after which an analysis will be carried out when and where the event contained position and time features. So that the distance factor, travel time factor and speed factor for each event can be calculated. Then a concept comparison between Probability Network from the Bayes Theorem versus Artificial Neural Network (ANN) was made, accompanied by a review of the Propositional Logic (PL) or First Order Logic (FOL) concept compared to using the Uncertainty concept in calculating the probability of an eventin a pseudo event that has often been ignored because it is considered to have no influence and the probability is small whereas it can be the opposite. In fact, sometimes, the pseudo event is the main factor determining the real event and the probability is much greater than expected. In addition, we also give an Engineering Events Theorem that can intentionally set event-by-event syntheses to increase the probability of an event which is initially smaller than the probability of another event. After being given an injection/dummy/synthesis event, however, then the initially small probability of the event becomes very high and the time of occurrence can be accelerated or decelerated, or even minimized so that it does not occur.

Regression analysis is one of the statistical methods that study the relationship between response variables and predictor variables. Parameter estimates in classical linear regression produce regression coefficients that are thought to apply globally to the entire observation unit. But in fact, the existence of factors from the spatial aspect causes conditions between one location and another to be different. This spatial aspect allows the emergence of spatial heterogeneity. Geographically Weighted Regression (GWR) is a local development regression technique from ordinary regression using spatial data. In addition, in a study data is needed in a certain period of time involving cross-section data and time series or referred to as panel data. Geographically Weighted Panel Regression (GWPR) is a combination of GWR and panel data regression. The purpose of this study is to model Geographically Weighted Panel Regression using Fixed Effect Model (FEM) within estimators with adaptive bisquare kernel weight for data on income inequality (Gini ratio) in East Java Province from 2010 to 2014. In addition, to obtain factors that influence significant income inequality in each district/city of East Java Province. The results of this study indicate that the GWPR fixed effect model differs significantly in the panel data regression model, and the models produced for each location will be different from each other. Districts/cities in East Java Province have twenty-eight groups based on significant variables. The variables that significantly influences income inequality are the percentage of the poor, percentage of GDP regional in the category of fisheries forestry agriculture, percentage of GDP regional in the processing industry category, percentage of GDP regional gross fixed capital formation, per-capita GDP regional, and dependency ratio. In the GWPR model, the R² value is 99.953%, with Root Mean Square (RMSE) is 0.0061035. While the FEM model within estimator produces an R² value of 22.844% with RMSE is 0.1035616.

In this study, Geographically Weighted Bivariate Gamma Regression (GWBGR) model is proposed. This GWBGR model is a developer of the Bivariate Gamma Regression (BGR) model which all of the regression parameters depend on the geographical location, i.e latitude, and longitude. In these models, the response variables are correlated and follow the gamma distribution. We applied the GWBGR model to analyze Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR) in North Sumatra Province 2017. The result shows that the test of heterogeneity spatial is significant, it means MMR and IMR in North Sumatra Province depend on the geographical location. Modelling with BGR produced 6 groups based on significant variable similarities to MMR and 3 groups based on significant similarity of variables towards IMR. Based on AICc, GWBGR model is smallest than BGR model. Finally, we conclude that the GWBGR model was better than the BGR Model (global model).

Rainfall data is one of the information that can be used in flood prevention, especially low-scale rainfall data (hourly). However, the availability of hourly rainfall data is very limited. It is because not all rain stations have hourly rain gauges which are quite expensive. This research aims to obtain hourly rainfall data based on daily rainfall data with the Bartlett Lewis Rectangular Pulse (BLRP) stochastic disaggregation method. This method was applied to rainfall data at the Hydrology Laboratory of Brawijaya University Malang in 2016. There are six BLRP parameters, namely λ, ν, κ, μ_x, α, ϕ which are used to produce hourly rainfall data. To maintain the consistency of hourly rainfall data based on daily rainfall data, a proportional adjusting procedure is used. The results showed that the disaggregation method of the BLRP approach can produce good enough hourly rainfall data that is consistent with daily rainfall data, seen from the in-sample MAE value of 0.5348 and 0.3113 for out-sample MAE.

Geographically weighted regression (GWR) is a spatial data analysis method where spatially varying relationships are explored between explanatory variables and a response variable. One unresolved problem with spatially varying coefficient regression models is local collinearity in weighted explanatory variables. The consequence of local collinearity is: estimation of GWR coefficients is possible but their standard errors tend to be large. As a result, the population values of the coefficients cannot be estimated with great precision or accuracy. In this paper, we propose a recently developed method to remediate the collinearity effects in GWR models using the Locally Compensated Ridge Geographically Weighted Regression (LCR-GWR). Our focus in this study was on reviewing the estimation parameters of LCR-GWR model. And also discussed an appropriate statistic for testing significance of parameters in the model. The result showed that Parameter estimation of LCR-GWR model using weighted least square method is $\hat{\beta }({u}_{i},{v}_{i},{\lambda }_{i})={[{X}^{\ast T}W* ({u}_{i},{v}_{i}){X}^{\ast }+\lambda I({u}_{i},{v}_{i})]}^{-1}{X}^{\ast T}W* ({u}_{i},{v}_{i}){y}^{\ast }$, where the ridge parameter, λ, varies across space. The LCR-GWR is not necessarily calibrates the ridge regressions everywhere; only at locations where collinearity is likely to be an issue. And the parameter significance test using t-test, t = $t=\frac{{\hat{\beta }}_{k}({u}_{i},{v}_{i},{\lambda }_{i})}{\hat{\sigma }\sqrt{{v}_{kk}}}$.

This paper discusses regression models when the variance in count data is not equal to the mean. It happens in mortality cause of traffic accident data in jurisdiction's territory of Dharmasraya's Police Resort, where the variance is larger than the mean, which is called overdispersion. In this case we used negative binomial regression in time series with generalized linier autoregressive moving average (GLARMA) models. The parameters were estimated using maximum likelihood estimation (MLE) method and metropolis hasting algorithm at 100^th burn - in period and 150000 iteration. The prior distribution and the number of iteration in metropolis hasting algorithm had less Mean Square Error (MSE) than MLE method. Prediction for next period using model metropolis hasting algorithm.

Linearity assumption that has not been fulfilled in the path analysis should use nonparametric approach. This research uses smoothing spline nonparametric path analysis with generated data where the condition of heteroscedasticity level measured through MAPD statistic will be applied to the data. The conditions are MAPD 0.01 – 0.20; 0.21 – 0.40; 0.41 – 0.60; 0.61 – 0.80; and 0.81 – 1.00. The purpose of this research is to determine the comparison of curve estimation of spline smoothing nonparametric path function on every level of heteroscedasticity category (DM) and without considering the heteroscedasticity (TM). The research results found that relative efficiency value of DM (PWLS) estimator with TM (PLS) that is always more than 1 for every heteroscedasticity level and every observation size. Thus, it was obtained better DM estimator (PWLS approach) comparing to TM (PLS).

Poisson regression analysis shows the relationship between predictor variables and response variables that follow the Poisson distribution which has equal dispersion and average values (λ), a situation called equidispersion. However, the variance can also be greater than the average value, called overdispersion. This can be caused by excess opportunities for the emergence of zero values in the response variable or zero excess. The parameter of the overdispersed data analysis can be underestimated so that the results become biased. This bias issue can be, hopefully, overcome by the Zero Inflated Negative Binomial (ZINB) regression analysis. In the 2016 Maternal Mortality Rate data in Bojonegoro District, overdipersion was overcome by ZINB regression even though there was no significant predictor variable found affecting the response variable. ZINB regression analysis can also be applied to generated data (simulation). We had the data with average λ = (0.2, 0.4, 0.6, 0.8, 1.0, 5.0) proportion of zeros p = (0.4, 0.6, 0.8), and the number of observationsn = (200,500, 800), with each setting was repeated 100 times. From the simulation study it was found that all overdispersion events were always accompanied by zero excess events but not vice versa. The greater the value of λ then the greater the dispersion coefficient. The ZINB regression is proven to be able to overcome overdispersion in various conditions of different values of λ, p, n which can be seen from the value τ (dispersion coefficient) after ZINB regression is less than 1 in all conditions.

Dengue fever is a disease that must be watched out and early preventive measures are taken so the spread of this disease can be reduced. An early preventive dengue fever can be controlled by a mathematical model. The article proposes a model of fuzzy inference system with optimal fuzzy rule bases generated by fuzzy c-means and optimized by ordinary least square (OLS). The developing system is done by forming a data structure in the form of input-output pairs. Factors that influence the number of dengue fever cases are used as the system input and the number of dengue fever cases as the system output. Based on the input-output pairs, the fuzzy rule bases is generated by using the fuzzy c-means method. The consequent part of the rule bases is optimized by the OLS method to produce the optimal rule bases. The resulted system is able to predict the level of dengue fever in the villages with an accuracy of 90% and can be used to predict the level of dengue fever in a village by inputting factors that influence the level of dengue fever.

The purpose of this research is to identify a particular relationship pattern among predictors and response variables where the response variables are multi-response and correlated. Path analysis is one of the appropriate statistical parametric method to estimate the parameter when the curves are known, but in fact the curves is unknown. Nonparametric approach is a suitable method to estimate the unknown curve in Path Analysis. Truncated spline provides a powerful tool for estimating curve in non-parametric path model with the optimal knots will be estimated by using Generalized Cross Validation. Truncated spline estimator is obtained from Weighted Least Square (WLS) technique that depends on polynomial degree and knot.

In this paper, we model the open unemployment rate with the Kernel-Spline model. We investigate and compare the performance of model Kernel-Spline by varying the Kernel function. The performance model has been compared with five Kernel function i.e. Kernel functions Uniform, Epanechnikov, Quartic, Gaussian, and Triweight. For these models, we conducted a comparison based on actual data sets, the unemployment rate in East Java. The best model was chosen based on the Generalized Cross Validation value and the coefficient of determination criteria. The empirical results obtained have shown that Spline-Kernel model by using the Gaussian Kernel better than other models.

Multiresponse semiparametric regression model is a combination of parametric regression model and nonparametric regression model with response variables more than one and correlate. The estimate used in estimate the parameters is spline truncated. Excess spline truncated is a model that has excellent statistical and visual interpretation and can model data with changing patterns on certain sub-intervals, because spline is a kind of polynomial pieces. The data used in this study is the value of Computer Based National Examination (CBNE) Vocational High School (VHS) in the province of West Nusa Tenggara (NTB) in 2017, each subject tested on CBNE serve as response variables. Based on the significant correlation test results obtained p-value <0.05 so it can be concluded that there is correlation between the responses. The result of the multiresponse semiparametric regression model estimation is obtained by the best model with the value of MSE of 49,608; R² of 0.84 and minimum GCV value of 0.00000323 so it can be concluded that the value of CBNE VHS in NTB province satisfies goodness of fit criterions.

The purpose of this research is to know simultaneously from the influence of working capital, land area and labor. This analysis uses descriptive quantitative research methods. The data used is secondary data from the research conducted by Suryati. The data were analyzed using the Cobb-Douglas production function and estimated its parameters using Gauss Newton-Nonlinear Least Square. the optimum production function can be obtained by minimizing the function constraints, therefore the Lagrange method is used to obtain the minimum constraint function. In the results of the research it was found that the input elasticity of land area and labor affected production output. Model of Cobb-Douglas production function in onion production in Sakuru Village, Monta District, Bima Regency is $\hat{{\rm{Q}}}=5,305,000{\,{\rm{x}}}_{1}^{0.5957}{\,{\rm{x}}}_{2}^{0.0766}$. The maximum production can be achieved with a land area of 13.19 acres and a workforce of 6 people.

Prostate cancer is cancer that attacks the prostate gland, usually affecting men over 50 years. Prostate cancer is a disease that develops slowly. Based on this, rapid and precise detection is needed so that the disease can be treated immediately. This study focuses on the application Feature Selection using the Random Forest Classifier to detect prostate cancer. The Random Forest Classifier is a method of classifying data by determining the decision tree. The use of more trees will affect the accuracy to be obtained for the better. The Random Forest Classifier can classify data that has incomplete attributes and can be used to handle large sample data. Selection of features is an important process because it can affect the accuracy of classification. This method increases accuracy by about 87%. Thus, the selection of features can improve accuracy in the detection of prostate cancer.

This research concern with dynamical analysis of a SVLIT (Susceptible Vaccination Latent Infective Treatment) model. It represents the spread of tuberculosis with vaccination and saturated incident rate. The incident rate is considered because of the barriers effect due to changes in susceptible individuals behavior. This model has two equilibrium points, namely disease-free equilibrium point which always exists and an endemic equilibrium point that exists under some certain conditions. The local stability of the equilibrium points is investigated by using Routh-Hurwitz criteria. The method of next generation matrix is applied to determine the basic reproduction number R₀. It can be shown numerically that disease-free equilibrium point is local asymptotically stable when R₀ < 1, while the endemic equilibrium point exist and local asymptotically stable when R₀ > 1. Numerical simulations are given to illustrate the theoretical results.

The increase in the snack food industry in Indonesia is one of the effects of improving the current lifestyle. Along with the progress of the digitalization era, technology and marketing strategies that take advantage of the industrial revolution 4.0 demand for snacks is also felt to grow rapidly. Likewise with the growth of culinary tourism provides an opportunity for small industries in the food sector to innovate in developing their products to be attractive to tourists. Mocaf (modified cassava flour) is local flour produced from cassava through a fermentation process, in the long-term it can be used as a substitute for wheat flour. This study aims to determine the variables that affect consumer satisfaction with mocaf-based food products. Purposive sampling technique was applied as a method for determining the sample, with a sample size of 145 people. The analytical method used is structural equation model with the help of warp-PLS software using two exogenous variables namely product quality variable (X1) and price variable (X2) also satisfaction endogenous variable (Y). The results of the analysis show that product quality variables have no significant effect on customer satisfaction, while the price variable has a significant positive effect on customer satisfaction. This shows that in the consumption of mocaf-based processed products, consumers consider product prices more than product quality to influence satisfaction.

There are special cases in Bivariate Probit model where one dependent variable becomes the regressor endogenous variable for the other dependent variable. Therefore, recursive bivariate model is used to solve this case. Exclusive breastfeeding is measured to identify its characteristics so that policies can be formulated to increase the rate of exclusive breastfeeding. The variable of mother working status is endogenous. Therefore, a recursive model is used to model this case. Mother's working status affects mother's exclusive breastfeeding. Mothers who do not work increase the chances of a mother being able to give exclusive breastfeeding. The greater the age of the first pregnancy of the mother, the less the chances of mothers to not work. A mother with more child has more chance to not working. A medium number of family member affect a chance for a mother to working. A mother who has last education at the Elementary School has a high chance of exclusively breastfeeding their babies. This may be related to the ability of mothers to work with mother's education. Early Initiation of Breastfeeding (EIB) affects the opportunity for mothers to exclusively breastfeed. It proves the importance of EIB shortly after the baby is born so that the baby does not confuse the nipple and the breastfeeding process becomes easy. A husband who has last education at Senior High School give a greater chance for the mother to exclusively breastfeed. Recursive Bivariate Probit models have smaller AIC values than Bivariate Probits, which are 522.226 and 526.414. This shows that the Recursive Bivariate Probit model is better at modeling cases of exclusive breastfeeding and the status of working mothers in Surabaya city than Bivariate Probit Model.

Time series forecasting often shows behavior that is non-stationary, so it is necessary to do a forecasting method that can predict non-stationary data in order to obtain good forecast results, including Autoregressive Integrated Moving Average (ARIMA) and Multiscale autoregressive (MAR). The characteristics of this model do not include predictor variables in the model. The MAR model is a model that performs the transformation process using wavelet. The MAR Model adopts an autoregressive (AR) time series model with predictors used are wavelet and scale coefficients. The wavelet and scale coefficients are obtained by decomposing using Maximal Overlap Discrete Wavelet Transformation (MODWT). MODWT functions to decipher data based on the level of each wavelet filter. This research aims to determine the best forecasting model using the ARIMA and MAR models. Testing performed on non-stationary data, so the ARIMA and MAR models can be used in this research. This research is expected to be able to obtain the best time series model and the most suitable to be used in predicting on non-stationary data.

A study to find an analytic, and fairly friendly, solution of Bose-Einstein Integral has been conducted, by employing various common mathematical relations, mainly the Fourier Series. The result obtained was without approximation, and did not involve any divergent series. This study was carried out with motivation to obtain analytic solutions from Integral Bose-Einstein, which is very important in the study of Planck Distribution of blackbody radiation, and in Bose-Einstein statistic; and to show that the Bose-Einstein Integral can, in fact, be solved without involving complicated special functions. The Bose-Einstein Integral, surely, had been solved by large number of authors, generally, by utilizing various special functions, such as, by using the Riemann-Zeta function [1][2][6][7]. This study produced the same results as previously obtained through various ways, that is π⁴ / 15, which guarantees that the steps we conducted were correct mathematically.

Over the past few years, Twitter has significantly grown as the microblogging platform. Millions of user use this platform to share their attitudes, views, and opinion on a daily basis. This phenomenon has been used to promote people's attention towards some event, such as 2019 Indonesian Presidential Election. In this study, we investigate people's online opinions towards the event through social media. The goal of the study is to discover frequent topics amongst netizens' tweets during the election campaign. We collected tweets containing the names of the candidates, then applied topic modelling approach using Latent Dirichlet Allocation (LDA) method to cluster the topics. Based on the experiment, the tweets are clustered into ten topics with different focuses e.g., a topic discusses the candidate's position towards sensitive issues, a topic about the community supports towards one presidential candidate. Our result shows that topic modelling approach can be used to analyse people's perception in social media towards an important event.

Hepatocellular Carcinoma (HCC) is a malignant tumor that attacks the liver and can cause death. Although there have been advances in technology for the prevention, diagnosis, and treatment, the number of liver cancer patients is still increasing. The liver can still function normally even if some of its parts are not in good condition. Therefore, the symptoms of liver cancer at an early stage are difficult to detect. Early diagnosis of this disease will increase the chances of recovery. One method to diagnose Hepatocellular Carcinoma (HCC) is to check the level of alpha-fetoprotein (AFP) in the blood which is alpha-fetoprotein (AFP) is a cancer index. If the liver cancer cells continue to grow, the level of alpha-fetoprotein (AFP) will be very high. This paper presents a Possibilistic C-Means (PCM) algorithm, which used to classify the results of alpha-fetoprotein (AFP) blood tests to determine whether patients diagnosed with Hepatocellular Carcinoma (HCC) or normal patients. This method will help to get an accuracy of about 92%.

The purposes of the paper are to apply a model of Double Seasonal AutoregressiveIntegrated Moving Average with transfer function (DSARIMAX) on the number of selecta tourist visitors in Batu city, which contain multiple seasonal elements and to add a threshold element to the DSARMAX model. The DSARIMAX model produced is a feasible model but it has a low p-value because the assumption of normality of the errors is not fulfilled. Addition of the threshold to the DSARIMAX model makes the model feasible with high p-value and satisfies the normality assumption of the errors.

Drought prediction is a very challenging work due to high degree of uncertainty in the climate system. Geopotential height has been investigated as one of the dominant variables that can be used to predict drought events. This paper discussed the use of high resolution satelite based (reanalysis) data as the predictor of drought events, resulting on a high dimensional dataset. To deal with this, dimension reduction has been carried out by using Principle Component Analysis (PCA), prior to the development of the downscaling models which incorporate the past SPI (Standardized Precipitation Index) combined with the geopotential height at some specific atmosperic levels i.e. 500hPa, 850hPa, 900hPa, 975 hPa and 1000hPa. The SPI, as the drought risk measure is derived from the reduced dimension of precipitation data observed from the corresponding meteorological stations, while the geopotential height is reduced from gridded high resolution data. The downscaling process found the best model to predict the drought risk with various degree of R-squares. The outsample validation showed that predicting drought using SPI3 (three month period SPI) with geopotential at the 900hPa level as the predictor outperforms the others with R-square reaching 77%.

Twitter is one of the popular social media platforms in Indonesia. This platform has been used as a media communication and public engagement tool for many purposes, especially in political and governance domains. During the process of 2019 Indonesian Presidential Election, many people use Twitter to express their opinion/sentiment towards the election process. In this paper, we investigate the nature of people's opinion towards the Indonesian Presidential Election after the 1st debate. The goal of this study is to perform exploratory sentiment based analysis of Twitter data, and that was gathered after the 1st debate. We used lexicon sentiment analysis to calculate the sentiment of political tweets collected after the 1st debate. The identification of positive and negative opinion was automatically conducted using the available dictionary. Our result shows that sentiment of the netizen towards the 1st Presidential debate was mostly negative. In addition to this result, a predictive model was generated using CART and logistic regression to predict the netizens' sentiment. This experiment shows that the accuracy of the prediction model reaches 90%. Therefore, our study suggests that Twitter data can be used to analyse citizens' sentiment toward the Indonesian Presidential Debate and can generate a model to predict citizens' future sentiment toward the next debate.

Inventory is a substantial factor for company to ensure the continuity of its production process. In the inventory management, products usually have expiration date that should be considered in the inventory model, especially in chemical or food industry. Another factor that has an influence to the inventory model is discount factor offered by the supplier. A retailer can accept this offer in order to reduce its total inventory cost. Although buying in a large quantity can decrease purchase cost per unit and set up cost, but it can increase holding cost and expiration cost which all directly has an impact in the total inventory cost. Inventory model with expiration date and all-units discount for probabilistic demand is the focus of this paper. From the mathematical model, an optimal order quantity that minimize the total cost of inventory can be obtained. In building the mathematical model, we assume that lead time is constant and the lead time demand follows Gamma distribution. Numerical examples are given to illustrate our model and algorithm to find the optimal solution. Sensitivity analysis for parameters used in the model is also performed in order to see the impact on the optimal order quantity and total inventory cost.

Optimal control theory was used on the system of differential equations to achieve the goal of minimizing the infected population and slow down the epidemic outbreak. Necessary conditions of optimal control problem were rigorously analysed using Pontryagin's maximum principle. Three control strategies were incorporated such as human education campaign, screening and treatment of infected human and its impact were graphically observed. Runge-Kutta forward-backward sweep numerical approximation method is used to solve the optimal control system. Numerical results with education campaign levels, screening and treatment rates as controls are illustrated.

The average per capita expenditure is the cost incurred for the consumption of all household members for a month divided by the number of household members. The higher expenditure per capita, the higher level of welfare of the population. In Indonesia, the number of non-food per capita expenditures increases every year and followed by a decrease in the number of poor people. Therefore, it is necessary to investigate the effect of changes in per capita non-food expenditure on the percentage of poor people in Indonesia. To investigate it, in this paper, we use nonparametric regression model based on least square spline estimator that could accommodate the data patterns changing at certain points. The results show that for every one-million-rupiah increase in per capita non-food expenditure, if the expenditure per capita is less than 0.47 million rupiah, the percentage of poverty will decrease 68.71%, and if per capita expenditure is more than 0.47, the percentage of poverty will decrease 5.96%.

Sustainable development is one of the goals of ASEAN as a UN cooperation partner. The high level of CO₂ emissions as one indicator of the decline in environmental quality in several ASEAN member countries, requiring an appropriate policy in order to achieve sustainable development targets. Based on previous research, the relationship between CO₂ emissions, income, and health expenditure is not only simultaneous but also dynamic (changes in a variable spread to the current period and future periods). The relationship between these variables can be described in a system of dynamic simultaneous equations. The ten ASEAN country panel data for eight years (2008-2015) will be used by applying a simultaneous dynamic data panel model. GMM-System Estimator and GMM Arellano-Bond are used to estimate the parameters of dynamic models. Based on the accuracy of the sign with economic theory as well as from the standard error estimator produced, estimates from the GMM-System Estimator are considered better. The resulting estimates indicate a significant simultaneous influence between health expenditure and per capita income and between health expenditure and CO₂ emissions. Per capita income growth affects the growth of CO₂ emissions indirectly through per capita health expenditure. In addition, the lag of each variable, namely per capita income, per capita health expenditure, and CO₂ emission has a positive and significant effect that indicates a long-run multiplier effect.

The national examination as one of the standard evaluation systems of education in Zimbabwe is used for the educational developments that seek to improve the quality of education in the educational sectors. This research aims to find the best model and its factors affecting the average pass rate of the Advanced Level (A-Level) national examination in Zimbabwe. Modelling was conducted using a two-level hierarchical model with factors influencing the national examination at district in the first level and those influencing the national examination provincial level in the second level. The Bayesian approaches namely hierarchical log-logistic and normal mixture were used in the modelling. The estimation of these Bayesian approaches posterior parameters was done using Markov Chain Monte Carlo (MCMC) and the Deviance Information Criterion (DIC) value was used to select the best model. The hierarchical normal mixture was found to be the best model to explain the variability of the average pass rate percentage of the A–Level national examination and all the micro and macro variables in this study significantly influenced the A-Level national examination in Zimbabwe.

The main challenge in the development of education is improving the quality of education and equitable education. Improving the quality of education is needed in order to create high-quality human resources. Equitable education carried out also still has problems with the uneven quality of education in each region. The aim of this research is to determine the effect of public senior high school status on the admission test scores, i.e., scholastic aptitude test score and Islamic test score of state Islamic colleges in Indonesia. In this research we use a local linear estimator to estimate nonparametric regression model for longitudinal data, and then apply it to the data of admission test scores of state islamic colleges in Indonesia based on the percentage of public senior high school. The result shows that the pattern of the scholastic aptitude test and Islamic test based on the percentage of public senior high school of state Islamic colleges in western Indonesia is different from that in eastern Indonesia. The result of this research can be used by the government to make policies so that the quality of state Islamic colleges will be better by increassing the admission test scores.

Propensity score is a method used to reduce bias due to confounding factors in the estimation of the treatment impact on observational data. Propensity score is the conditional probability to get certain treatments involving the observed covariates. In general, propensity score can be calculated using two methods, they are logistic regression and Classification and Regression Tree Analysis (CART). Logistic regression model is the most common method used. In which, logistic regression model is a model used to estimate the probability of an event. In other side, collecting data by observing many subjects in different place will be influenced spatial effect. Thus, this paper will estimate propensity score using spatial logistic regression.

Cancer is one of the most common cause of death. One of the diseases that can be threaten women all over the world is ovarian cancer. Ovarian cancer is the eighth type of cancer that most women suffer from. Estimated that around 225.000 new cases are detected every year and around 140.000 people die each year from ovarian cancer. Based on WHO data, published in 2014, in Indonesia 7,6% of all cancer deaths are caused by ovarian cancer. So far there is no effective screening method for ovarian cancer. Current screening applications for high-risk women are still very controversial. There are many classification techniques has been applied for ovarian cancer prediction, for example deep learning, neuro fuzzy, neural network, and so many more. In this paper, we propose Bayesian logistic regression for ovarian cancer classification. We use data of patients suffer from ovarian cancer from RS Al-Islam Bandung to demonstrate the method. The accuracy expectation in this paper around 70%.

Poisson Regression is a standard model for data counts that can be used to determine the relationship between the response variable and predictors variables. Equidispersion is assumptions that must be met in Poisson Regression. Equidispersion is a condition that the variance of the response variable is equal with the average of the response variable. In real cases, these assumptions are often not meet. In real cases, there are overdispersion and underdispersion cases. Generalized Poisson Regression (GPR) is one method that can handle cases of overdispersion and underdispersion. The GPR model is used to estimate regression parameters. Many articles proposed to use only Maximum Likelihood Estimation (MLE) to estimate the parameters of GPR. This article will develop the parameter estimation method of the GPR model, which is using the Generalized Method of Moments (GMM). GPR model is applied in the case of diarrhea in infants in Pasuruan Regency, East Java. The best model is chosen by the value of AICc. The smaller the value of AICc, the better the model. The best model is a model that includes exclusive breastfeeding, complete basic immunization, and healthy living behavior in the model.

Poisson regression is a standard model for data counts that can be used to determine these factors. Equidispersion is assumptions that must be met in poisson regression. Equidispersion is a condition that the average of response variable is equal with the variance of the response variable. In real cases, there are overdispersion or underdispersion cases. Generalized Poisson Regression (GPR) is one of method that can handle cases of overdispersion or underdispersion. Multivariate Adaptive Regression Splines (MARS) is a nonparametric regression that can handle data whose behavior changes in sub-intervals, so that there is a knot point that indicates the occurence of changes in data behavior patterns. Multivariate Adaptive Generalized Poisson Regression Splines (MAGPRS) model is used as the development of the MARS and Generalized Poisson Regression. This research use Weighted Least Squares (WLS) with Berndt Hall Hall Husman (BHHH) algorithm to obtain parameter model estimator. Afterwards, get the test statistic on the model Multivariate Adaptive Generalized Poisson Regression Splines using Maximum Likelihood Ratio Test (MLRT). Finally, the application of MAGPRS model was carried out in the case of the number of Acute Respiratory Tract Infection in babies.

Lognormal distribution plays an essential role in the distribution modeling of right-skewed data in many areas. In social sciences, for instance, it can be used to model women's age at first marriage pattern, a key indicator in studying fertility patterns. Distribution fitting is a fundamental and essential stage of data modeling before doing advancing the analysis. Kolmogorov-Smirnov (KS) distance is applicable as maximum goodness-of-fit (GOF) estimators for distribution parameters. Minimizing KS distance is optimization problem. Particle swarm optimization (PSO) algorithm is a general optimizer that can handle various optimization problems. This study assesses the characteristics of minimum KS distance estimator for lognormal distribution parameters. KS distance estimators were obtained via optimization using the PSO algorithm, so the combination of these is called the PSO-KS algorithm. We conducted a simulation to assess the performance of PSO-KS, Maximum Likelihood (MLE), Method of Moment (MME). The bias and mean square error (MSE) of point estimators were used in simulation to assess the characteristics of estimators. Meanwhile, MSE of distribution fitting, KS distance, and log-likelihood value were used to evaluate the GOF characteristics. Moreover, we demonstrated the performance of the algorithm by implementing it to women's age at first marriage data in Indonesia. The results show that based on the bias and MSE properties, the PSO-KS point estimators yield similar characteristics with MLE, but better than MME. From the GOF perspective, PSO-KS outperforms in MSE of distribution fitting and KS distance, but not in log-likelihood value. We also observed these patterns in the women's age at first marriage data. The contributions of this study are two-fold, first to assess the PSO-KS algorithm in the lognormal distribution case. Second, it implements the algorithm on women's age at first marriage data, which has broad social, economic, and public health implications.

Regression analysis is one method in statistics that is used to determine the pattern of functional relationships between response variables with predictor variables. Semiparametric regression approach is a combination of parametric regression and nonparametric regression. The most popular estimator for nonparametric regression or semiparametric regression is spline truncated estimator. Spline is the estimation method that is most often used because it has excellent statistical interpretation and visual interpretation compared to other methods. Regression modelling using longitudinal data is often found in everyday life, where observations are carried out for each subject over a period of time. Interval estimation is often examined by nonparametric regression and semiparametric regression; this estimation aims to determine predictor variables that have a significant influence on the response variable. One indicator used in poverty analysis is the poverty line. Based on Indonesia's macro poverty analysis calculations, in the period March 2016 to March 2017, the poverty line increased by 5.67 percent, with increases in urban and rural areas at 5.79 percent and 5.19 percent respectively. Modelling using semiparametric spline truncated regression for longitudinal data on data on the percentage of poor people in Indonesia produces the best model using W₁ weighting and one point knot. Based on the results of the study with a significance level of 0.05, it was found that the percentage of poor people was influenced by the human development index (HDI) and the unemployment rate. This semiparametric regression model has a minimum GCV value of 1.677, MSE of 5.477 × 10⁻² and R² value of 98.67%.

Small Area Estimation (SAE) is an indirect method that has been widely used for estimating parameters in a small area or small domain by borrowing strength of predictor variables from census or registration. This study uses Hierarchical Bayes (HB) method under the univariate and bivariate Fay-Herriot (FH) model to estimate monthly average per capita expenditure of food and non-food commodities for each district level in Province of Bali in 2014. Then estimation results from both models will be compared. The bivariate FH model is expected to increase the accuracy of the results of estimation by taking into account correlation between two types of expenditure rather than perform univariate estimation separately. Thirteen predictor variables from the administrative record of village data (PODES 2014) are included in each model as factors that affect these two types of expenditure. From the result, there are three variables that have significant effect on food expenditure, both in univariate and bivariate FH model. While, for non-food expenditure both model show different result on significant variables. Based on the results of the performance comparison, the best model is bivariate FH model since it has smaller Mean Square Prediction Error (MSPE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) value than univariate FH models. In addition, the bivariate FH model produces shorter 95% credible interval of estimated values. These conditions indicate that jointly modeling can improve the accuracy of estimation. Bivariate FH also produces significant improvement in adjusted R² value. Finally, the mapping result shows the same pattern for two types of expenditure. The highest monthly average per capita expenditure is more localized in the southern districts of Bali. While the lowest expenditure is more localized in the eastern and western districts of Bali.

A predator-prey model with disease in both populations is proposed to illustrate the possibility of disease transmission between prey and predator through contact and predation. We used saturated incidence rate which takes behavioural changes of healthy population into consideration when disease spreads around them. The existence of eight non-negative equilibrium points is analysed and their local stability has been investigated. Numerical simulations are given to illustrate analytic results.

Choroidal neovascularization describes the abnormal growth of vessels from the choriocapillaris through the Bruch membrane into the space beneath the retinal pigment epithelium or the space beneath the retina. Choroidal neovascularization detection is considered to be the most important feature in the pathogenesis and treatment of a number of chorioretinal disorders, one of which to detection in digital fundus retinal images. In this study, we classify choroidal neovascularization by using statistical modeling approach based on local linear estimator. Based on 40 in samples and 10 out samples data images, we obtain the same accuracy of classification of 90 percent and their sensitivity are 90 percent and 83.33 percent, respectively. So, we conclude that the local linear is a good estimator to classify choroidal neovascularization on fundus retinal image.

One phenomenon that can be observed in granular systems is the Brazil Nut Effect (BNE), that is, a phenomenon in which large-size particles lift up when vibrated vertically. In this experiment, structural changes in a pseudo-two-dimensional model of a granular system experiencing BNE were observed from the perspective of network analysis. The system consisted of 199 granular beds of 0.68 cm of diameter with a 2.5 cm diameter intruder placed in a 3mm wide double-window box that was slightly larger than the thickness of the bed and the intruder. The system was subjected to vibrations with a frequency of 13.33 Hz and an amplitude of 0.75 cm, so the BNE could be observed. For the purpose of the analysis, the granular beds were considered the nodes of a network and the relationships between adjacent beds (were contact force occurred) represented its edges. The analysis, consisting of image processing, network extraction, network parameters calculation and community detection, was performed using Wolfram Mathematica v. 11.3. The experiment was able to calculate the change in the network parameters including degrees, clustering coefficients, betweenness centrality, and modularity for the system with intruders and systems without intruders. The parameter values corresponding to each system were markedly different, clearly showing the influence of the intruder. The authors were also able to successfully map the evolution of the community structure in both types of granular systems one step at a time using a modularity optimization method.

Survival analysis can be used to examine events that may occur more than once in one individual or is called a recurring event. This event can be seen in cases of stroke recurrence. The occurred recurrence shows a more severe recurrence stage than before (recurring events are not identical). The Stratified Cox model with Conditional I, Conditional II, and Marginal methods can be used to analyse recurring events, especially in recurrent events that are not identical. The used data are secondary data from the medical record archives of stroke patients at Chasbullah Abdulmadjid Hospital Bekasi City from March 1, 2016 to April 30, 2016. Based on the Wald test and the backward elimination steps, we found two explanatory variables that influence the recurrence rate of stroke which were hypertension and hypercholesterolemia. The model from the three methods is a model with a combination of variables of hypertension and hypercholesterolemia. Patients suffering from hypertension or hypercholesterolemia have the potential to experience a recurrence of stroke compared to patients who do not have hypertension or hypercholesterolemia. Based on the AIC value of each method, the best method for recurring data on stroke patients is the Conditional I method.

Diabetes mellitus or commonly referred as diabetes is a metabolic disorder caused by high blood sugar level and the pancreas does not produce insulin effectively. Diabetes can lead to relentless disease such as blindness, kidney failure, and heart attacks. Early detection is needed in order for the patients to prevent the disease being more severe. According to the non-normality and huge dataset in medical data, some researchers use classification methods to predict symptoms or diagnose patients. In this study, Learning Vector Quantization (LVQ) is used to classify the diabetes dataset with Chi-Square for feature selection. The result of the experiment shows that the best accuracy is achieved at 80% and 90% of the data training and the performance measurement, which are precision, recall, and f1 score are the highest when the model contains all the features in the dataset.

Sinusitis is an inflammation of the sinus wall, a small cavity interconnected through the airways in the skull bones. It is located on the back of the forehead, inside the cheek bone structure, on both sides of the nose, and behind the eyes. Chronic sinusitis is caused by infection, growth of nasal polyps, or irregularities of the nasal septum. This condition can affect teenagers, adults, and even children. To classify sinusitis we use Kernel Based Fuzzy C-Means (FCM) Clustering Algorithm, which is the development of Fuzzy C-Means (FCM) Algorithm. FCM is one of the widely used clustering technique. FCM algorithm comprises of sample points used to make whole and sub vector spaces according to the size of the distance. However, when non-linear data is separated, the convergence is inaccurate and slow. To overcome this problem, a Kernel-Based Fuzzy C-Means algorithm that makes use of kernel functions as a substitute for Euclidean distance utilized. It maps out samples to high-dimensional space to increase the differences between cluster centres, so they can overcome FCM deficiencies and improve linear machine capabilities. Data was obtained from the laboratory of Radiology at Cipto Mangunkusumo National General Hospital, Indonesia, with a 100% accuracy.

Image enhancement sputum is needed to identify bacteria mycobacterium tuberculosis (TB). The number of TB bacteria in sputum images determines the severity of tuberculosis sufferers. In this paper, we study image enhancement sputum techniques by using spatial domain filter-based methods such as median filtering, Gaussian filtering, adaptive noise-removal filtering and bilateral filtering. These filtering techniques are used to overcome the problems when taking sputum images such as adjusting the focus of the lens, lighting and dirt that stick to the lens and on the slide glass. The obtained results for 100 data sputum images from this study are average means square error (MSE) of median filtering, Gaussian filtering, adaptive noise-removal filtering and bilateral filtering, i.e., 30.68, 17.10, 18.92 and 26.28, respectively. Also, average peak signal-to-noise ratio (PSNR) of them are 33.70 dB, 35.91 dB, 35.59 dB and 34.01 dB, respectively. The avarage computational time are 0.09 sec, 0.18 sec, 0.38 sec and 134 sec respectively.

Regression analysis is a method for determining a causal relationship between the response and predictor variables. The regression model has been developed in various ways, one of them is based on the distribution of the response variables. In this study, the response variables follow trivariate gamma distribution, such that the regression model developed is Trivariate Gamma Regression (TGR). The purposes of this study are to obtain the parameter estimators, test statistics, and hypothesis testing on parameters are significance (overall and partial) of the TGR model. The parameter estimators are obtained using the Maximum Likelihood Estimation (MLE). The overall test for the model's significance is using Maximum Likelihood Ratio Test (MLRT), and the partial test is using the Z test. Based on the results of this study, it can be inferred that the parameter estimators obtained from the MLE are not closed form. Hence a numerical method is needed. In this study, the algorithm of numerical optimization used is BFGS quasi-Newton.

Wasting is a condition of a children characterized by a lack of weight by measuring weight for height. Currently, to monitor the growth conditions for childrens in Indonesia, we use the Towards Healthy Card called as Kartu MenujuSehat (KMS) which is guided by WHO 2005. The samples used to design WHO-2005 standard charts are children from Brazil, Ghana, India, USA, Norway, and Oman that have different physical conditions from children in Indonesia. Therefore, the using of standards growth charts from other countries cause incompatibility with Indonesian's children growth. To illustrate the growth patterns of children in East Java, we use the semiparametric least square spline estimator that gives more flexible pattern. In this study we used weight (kg) as a response variable, height (cm) as a predictor variable for nonparametric component, and gender as a predictor variable for parametric component. The results show the semiparametric least square spline estimator can explain the growth patterns of children well because it has determination coefficient (R²) of 99.78% and mean square error (MSE) of 0.0353. The standard chart of weight for height of boy is higher than that of girl and percentage of wasting nutritional status of girl greater than that of boy.

It is given the paired data following a nonparametric regression model for longitudinal data. The regression curve is approached by the smoothing spline function. The smoothing spline is a function that is able to map the data well and has a small error variance. This current study aims to obtained from completing PWLS (Penalized Weighted Least Square) optimaziton. Besides, the GCV method is used to select the smoothing parameter.

Lung tumor is a group of abnormal cells that are formed from the process of excessive and uncoordinated cell division in the lung or known as a neoplasia. Neoplasia refers to the growth of new cells that are different from the growth of cells around it. The Tumor can formed to be benign tumors that not cause cancer and malignant tumors that can cause cancer. Chest X-ray is the most technique that used for detecting a lung tumor. Image processing is done by mean for distinguishing the classification lung tumor. Based on previous research the most used method is the mathematical method, but the result obtained are not maximal. Therefore, in this study we propose methods to classify lung tumor by using statistical modelling approach with logit link function based on parametric model, and nonparametric model using penalized spline estimator. Based on the proposed method, we get the classification accuracy of 80% for parametric model approach and 85% for nonparametric model approach, it means that the nonparametric model approach is better than the parametric model approach.

The application of mathematics in the field of bioinformatics has been widely developed. For example Support Vector Machines (SVM) and Random Forest (RF) are state of the art for classification of cancer in many applications. One of them is Chronic Kidney Disease (CKD). CKD is one of the kidney diseases that sufferers are increasing and have symptoms that are difficult to detect at first. Later, microarrays in gene expression are important tools for this approach. Microarrays gene expression provides an overview of all transcription activities in biological samples. The purpose of this research is a hybrid model combining Random Forest (RF) and Support Vector Machine (SVM) can be used to classify gene expression data. RF can highly accurate, generelize better and are interpretable and SVM (called RF-SVM) to effectively predict gene expression data with very high dimensions. In addition, from the simulation results on data from the Gene Expression Omnibus (GEO) database, it is shown that the proposed RF-SVM is a more accurate algorithm on CKD data than RFE-SVM.

Breast cancer is the second leading cause of death in women in the world. The classification is the initial process of executing patient treatment, which is important as it increases life expectancy as well as quality. In this paper, a new method is proposed based on kernel, which is modified from KC-Means: it combines K-Means, Fuzzy C-Means algorithm, and kernel function. The C-Means algorithm is applied on the centers of a fixed number of groups founded by K-Means, and the kernel function is expected to improve the accuracy of classification with its ability to separate data which cannot be separated linearly. We applied the proposed method on a dataset of 201 breast cancer and 85 non-breast cancer samples from the UC Irvine Machine Learning Repository. Results concluded that fast fuzzy clustering has an accuracy of 85.26%, but fast fuzzy clustering based on kernel is 89.74%, with a better running time on average than 90.95% with the same method.

Cancer has been known as a disease consisting of several different types. Cancer is a life threatening disease in the world today. There are so many types of cancer in the world, one of which is colon cancer. Colon cancer is one of the number one killers in the world. However, because there isn't any obvious symptom of colon cancer at an early stage, people do not realize that they suffer from it. Even though cancer formation is different for each type of cancer, it is still a big challenge to make cancer classification with good accuracy. Many machine learning has been applied to the data of human's genes in order to get the most relevant genes in the classification of cancer. The author proposes the Naïve Bayes Classifier model as a classification method to show that the model has good accuracy, good precision, good recall, good f₁ — score in classifying the data of patients suffering from colon cancer or not. In this proposed model, Naïve Bayes Classifier is a technique prediction based on simple probabilistic and on the application of the Bayes theorem (or Bayes rule) with a strong independence assumption. Therefore, this model is able to make higher classification accuracy with less complexity. In particular, it achieves up to 95.24% classification accuracy, thus this model can be an efficient analysis tool.

This research focuses on the dynamical of a Leslie-Gower predator-prey model with competition on predator populations. The model represents an interaction between one prey and two predator populations. The analysis shows that there are four equilibrium points, namely the extinction of predator populations point, the extinction of the first predator population point, the extinction of the second predator and the interior point. The existence of the interior equilibrium point is investigated by using Cardan criteria. Local stability analysis shows that both predator populations have never been extinct together. The second and third equilibrium point is local asymptotically stable under some conditions. Numerical simulations are carried out to investigate the stability of the interior point as well as to show that more than one equilibrium point may be asymptotically stable together for a set of parameter.

The Indonesian transportation sector is currently the nation's largest consumer of petroleum products and a significant source of greenhouse gas (GHG) emissions overall. Many policies have been implemented by central and local governments, from the introduction of alternative fuels until demand management like odd-even policy; results, however, are in most cases disappointing. This paper explained a system dynamics model for the road transportation sector in Indonesia. The model considers basic policy options under Activity, Structure, Intensity, and Fuel (ASIF) framework and includes two main objectives, i.e., reduction of energy consumption and CO₂ emission. Policy scenarios were developed to cover business as usual (REF), transport demand management (TDM), the introduction of fuel economy (FE) standard and feebate system (FEE), adoption of electric vehicles (EV) which considers future technological improvement factors and fuel switching strategy. The result indicates that it is mandatory to have a policy mix as the most effective strategy to reduce energy consumption, CO₂ emission and achieve national energy mix target in 2050.

Technology is growing very fast. We can now access everything using internet anywhere and anytime. That is why it is important to have internet security since we are always open to an online fraud, property damage and theft. IDS (Intrusion Detection System) can be used to detect any system or network attack. In this empirical study, we use dataset from KDD Cup 1999, which consist of five classes: normal, probe, dos, u2r and r2l. There is some classifier method for IDS, but in this study, we will use Fuzzy Robust Kernel C-Means (FRKCM) with Polynomial kernel and Fuzzy Entropy Kernel C-Means (FEKCM) with RBF kernel to find a better result that increase accuracy of the network attacks. There will be an accuracy comparison between FRKCM method and FEKCM method. The accuracy result from this study is 99% with time execution faster.

Starting from the analogy of gravitational forces to explain the volume of bilateral trade, the Gravity model has become a very popular model in international trade research. The Gravity Model has also been widely developed by adding various independent variables. Instead to measuring trading volume and various factors that influence the volume of trade, there is not much utilization of the gravity model to measure trade potential. This research is intended to implement a gravity model to measure the potential of Indonesian fruit trade. The measurement of trade potential is carried out by using data on the three main group of Indonesian exported fruits (based on 6 digits HS Code) which traded in 16 years. The classical gravity model, employed in this research contains independent variables, such as the amount of tariffs, the existence of free trade agreements, population, GNP of each country, distance and share of trade. The method of analysis used refers to the gravity model applied by Susanto, et al (2007) and regression analysis method applied by Arita et al (2014). Since its easily fits with some important stylized facts, it is easy to use real data, and also easily estimates using Ordinary Least Square (OLS), the Gravity Model can be a comprehensive instrument for managing big data to present rapid and dynamic estimates of international trade in line with the demands of the Revolution Industry 4.0.

The second order indicator model can be the first order having formative or reflective indicators of an underlying second order. The research used principal component analysis in the first order and factor analysis in the second order. The variable used in the research was ihsan behavior. This research aims to apply multivariate analysis, i.e. the principal component analysis in the first order and the factor analysis in the second order to obtain the latent variable data of ihsan behavior in the second order indicator model. The data used in this research were primary data by distributing questionnaires. Respondents of this research were lecturers of the Faculty of Economics and Business at the University of X. The research results generated latent variable data in the form of ihsan behavior. Ihsan behavior was reflected in six indicators, i.e. doing something perfectly, repaying goodness with more goodness, reducing optimally unpleasant consequences, as a solution when justice cannot be realized, as a logical consequence rather than faith, and as an investment in future success.

The problems one of that appeared in everyday life is how to explain the shape of the relation pattern between a response with some predictor. Regression analysis is a statistical method used to estimate the relation pattern between response (y) with predictor (x). Development of nonparametric regression of Fourier series involving multiple predictor, has been more developed for predictors of the same pattern. We need to develop different estimator for different predictors of multivariable nonparametric regression. Theoretical research will be focused on estimator form, and applied to simulation data. The estimation of the combined regression function of the Fourier and spline truncated sequence is obtained through the optimization of penalized least square (PLS). Based on the simulation result obtained that the larger the sample size and the smaller the size of the variance, it will result in better estimation value of parameter and knot.

One of the tasks of the government is to maintain price stability reflected in the stability of inflation through the regulation of the money supply. Therefore the government needs to know the forecast of the inflation rate and the money supply. In modeling inflation and the money supply simultaneously in Indonesia, three things need to be accommodated, namely the relationship between variables, the existence of space-time relationships and the effect of Eid al-Fitr. The spatial vector autoregressive model with calendars variation can accommodate these three things. The purpose of this study is to compare two types of spatial vector autoregressive models with calendar variations, namely restricted and non-restricted coefficient to model inflation and the money supply in Surabaya, Malang, Kediri, and Jember simultaneously. Results of this study indicate that the non-restricted spatial vector autoregressive model with calendar variation is better than the restricted one. This can be seen from the value of MSE of the non-restricted model that is smaller than the restricted model.

Poverty is a classic problem in almost all regions in Indonesia both at the provincial and district levels. Polewali Mandar district is a district with the highest poverty rate in West Sulawesi Province. Handling the problem of poverty requires the availability of data to the smallest level so that the policies taken by the government can be right on target. One method for obtaining poverty estimates reaches the smallest area using the Small Area Estimation (SAE) method. Spatio-Temporal Fay-Herriot models is one of the SAE methods that has considered spatial and time effects. Auxiliary variables are needed in the SAE method to get good estimation results. Availability of auxiliary variables that affect poverty in Polewali Mandar district is very necessary. Therefore the purpose of this study was to obtain auxiliary variables that influence poverty in Polewali Mandar district. Bootstrap parametric procedures are used to obtain factors that influence poverty in Polewali Mandar district, while for spatial weighting matrices use customized contiguity based on the main business fields in the village. The response variable is the percentage of poverty in each village affected by the Susenas sample. The results obtained were the variables that affected poverty in Polewali Mandar district were the percentage of farm worker's families, the percentage of the population receiving Jamkesmas / Jamkesda, ratio of population to number of micro and small industries, average number of household members, ratio of population to number of health facilities and percentage of families living on river banks.

Chronic Kidney Disease is the second chronical and catastrophic disease after heart disease in terms of treatment cost. This is because CKD symptoms occurs on final stages, that is fourth and fifth, in which it is too late for treatment. Therefore, final stage patient must receive continuous medication, such as haemodialysis. So early detection on a patient CKD is necessary to prevent patient to be chronic. Studies of gene genes are used to classify microarray data with global CKD decisions or not. So to get accurate results in this study using SVM-RFE with the addition of the Particle Swarm Optimization algorithm as a gene selector to be more optimal and it consideration of the fixed gene in its condition which is important information of the CKD gene itself. This research is then expected to be able to classify globally with CKD output or not CKD. As a result, for the CKD microarray data accuracy using RPSO schemed SVM highest than only using SVM-RFE.

Cox survival analysis is a statistical method used in survival data, which examines an event or occurrence of a particular event. In survival analysis, the response variable is survival time, usually called the T failure event. In the development, survival analysis involves spatial effects. One of the spatial effects is point effect, which coordinates of adjacent points will give an influence. Spatial model involves points called Geographically Weighted Regression (GWR). In this research, data distribution used is Weibull distribution, which survival time data is divided into three periods. Parameter estimation used is Bayesian Approach. Bayesian approach is better used in survival analysis that has a lot of censored data. The research purpose is getting survival function, hazard function, and Cox survival model with GWR and Weilbull distributed data and determining the prior distribution and posterior distribution in Bayesian approach. The result of this research is reducing the new hazard function from Weibull distribution and changing μ to become the GWR model, and then obtained model is $h(t,x)=\rho {t}^{(\rho -1)}\exp \left({\beta }_{0}({u}_{i},{v}_{i})+\displaystyle \sum _{k=1}^{p}{\beta }_{k}({u}_{i},{v}_{i}){x}_{ik}+{e}_{i}\right)$. Parameters in the result are estimated using Bayesian approach.

The problem of HIV and AIDS in Indonesia is a frightening health problem with a number of cases that tend to increase each year. The aim of this research is to model the number of HIV and AIDS cases in Indonesia using bivariate negative binomial regression approach. Bivariate negative binomial regression is a regression method for modeling a pair of response variables in the form of count data with negative binomial distribution and correlating to each other. This research uses secondary data from the ministry of health in 2017 about the number of HIV and AIDS cases in Indonesia. From the results of this research, we obtained the deviance value of 38.9197 which was used to describe the goodness of fit test.

Forecasting the amount of Pneumonia patients could help medical practitioners to prepare the required medicines, aid-workers, or even prevent it by sharing knowledge to parents, elders, and smokers. This problem poses great concerns on the lives of many people, therefore, adequate accuracy is required in forecasting. Fuzzy Time Series (FTS) is an alternative way to forecast data. By using ARIMA and Holt's Exponential Smoothing, there are some problems that are difficult to obtain the best model. Using our FTS method, we modified the Cheng algorithm by using higher order (using two or more historical data) to make the accuracy better by seeing the Mean Absolute Percentage Error (MAPE). Data was selected from the amount of Pneumonia Patients in Jakarta from 2008 to 2018. We use R to carryout ARIMA and Holt's Exponential Smoothing. Forecasting's accuracy will decrease if the timeframe between these occurrences is lengthy. As a result of this, we made use of 5 periods which are January until May 2019. The result obtained was compared against ARIMA and Holt's Exponential Smoothing, as well as the MAPE are 9.70%, 16.85%, and 18.55% respectively.

This study discusses the influence of smoking behavior of both active and passive smokers on the growth of lung cancer population through mathematical models. There are four population in this model, namely: susceptible population, active smoker population, passive smoker population, and population of lung cancer patients. The model is then analyzed using stability theory of nonlinear differential equations. Based on analysis result, the model has three equilibrium points: extinction equilibrium point, smoker-free equilibrium point and endemic equilibrium point. These equilibrium points are asymptotically stable under certain conditions. Moreover, education is involved as a control which is applied to susceptible population. The purpose of this optimal control is to minimize the population of smokers and lung cancer as well as the education costs. Pontryagin's principle is then implemented to solve optimal control problems. Finally, numerical simulations are carried out to determine the effectiveness of the controls used.

Parameter estimation in the linear regression model using ordinary least square (OLS) method is less precise to analyze data containing outliers. It is because outliers can cause unstable parameter estimate. In addition, the existence of outliers causes residuals to be larger so that the residual's variance is not constant (heteroscedasticity). One model that is able to overcome the effect of outliers is quantile regression because it can accommodate the non-homogeneous variances in modeling. In this study, the confidence interval of the parameter estimate in the quantile regression model was obtained, i.e., the Bias-Corrected and accelerated (BCa) bootstrap method. The proposed method was applied in modeling the open unemployment rate in Indonesia in 2017. The quantile value used in this study is quantile 0.05, 0.5, and 0.95 with 1500 resampling in BCa-bootstrap approach. The empirical result shows that the best quantile regression model is obtained at the value of quantile 0.95 which has a Pseudo R² value is 60.45 percent. The model at quantile 0.95 shows that the percentage of youth, economic growth rate, and labor force participation rate have a significant effect on the open unemployment rate in Indonesia.

In spatial analysis, the experimental variogram fitting generally uses a mathematical variogram model such as monotonous spherical, exponential Gaussian along with increasing distance lag. However, there are certain cases, in this case the land price in Manado which raises experimental variogram in the form of non-monotonous (hole effect) as well as sinusoidal waves. This means that experimental variogram matching must be done as well as possible based on the positive definite function or at least following the hole effect pattern at the first peak. This study offers a variogram multiplicative-additive operation to combine monotonous and non-monotonous models in a nonparametric manner. The monotonous model used is a Gaussian-type for the amplitude of the hole effect, while the non-monotonous model is the first order zero-form Bessel function to indicate the wavelength. Based on measurements of the accuracy of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error), combining the two models is relatively good and successful in fitting experimental variograms of land prices that have indicators of hole effect with periodicity. The hybrid composition of these models (non monotonous and monotonous models) provides a better approach to the experimental variogram compared to the previous mathematical monoton variogram models. To model Manado's land price which has a hole effect, it would be more appropriate to use the Bessel composition model on the basis of p = 1 and the Gaussian-type model at m = 1 (or Exponential).

This study aims to determine the factors that influence the food security of farmer households in the Papua border region. Twelve factors used in this study are maternal education, number of family members, price of rice, price of sweet potatoes, prices of cooking oil, prices of instant noodles, income, area of arable land, distance of buying food, share of food expenditure, reception of the rice for poor families (raskin), and farmer status (either local or transmigrant). This research uses primary data from direct interviews by asking a list of questions to farmer households in Jayapura City and Keerom District. The samples are randomly selected, and the total respondents are 160 farmer households, then the data analyzed by Ordinal Logit Regression. The results show that most of the household farmers classified as the food secure condition. Partially the number of family members, cultivated land area, the share of food expenditure and the price of sweet potato/cassava have a significant effect the probability of the occurrence of food security for farmer households significant at the 5% level of error, while dummy raskin significant at an error rate of 10%. The cultivated land area and dummy raskin have a positive effect on the food security of farmer households, while the number of family members, the price of sweet potato/cassava, and the share of food expenditure have a negative effect on farm household food security.

In this paper, we present a mathematical model of maize foliar disease with standard incidence rate. The present model is an improvement model from Collins and Duffy, where Collins and Duffy consider a mathematical model with the bilinear incidence rate. The present model has two equilibria namely the disease-free equilibrium and endemic equilibrium. We find that the disease-free equilibrium is asymptotically stable whenever the basic reproductive ratio is less than one. On the other hand, the endemic equilibrium will exist and be asymptotically stable whenever the basic reproductive ratio is greater than one. Furthermore, we perform numerical simulations to confirm the analytical results.

Facebook, Instagram, Twitter are some popular social media. DeLegge and Wangler (2017) have developed a Susceptible-Infectious-Removed type mathematical model to describe social media popularity. The DeLegge and Wangler model was a bilinear incidence rate model. In this paper, we improve the DeLegge and Wangler model by considering standard incidence rate. The presented model takes the form of an ordinary differential equation system that describes dynamic of susceptible (population of who are not social media users), infectious population (population of social networks users) and removed population (population of who leave social media). The presented model has three equilibria namely the "no social media users" equilibrium, "very popular social media" equilibrium and "popular social media" equilibrium. We find that the three equilibria are conditionally asymptotically stable. We also perform some numerical simulations to verify the analytical results.

The need for application of android-based experimental physics is urgent, beside to facilitate students to do the lab, students can reach the simulator through gadget. Previously Balmer series experimental physics applications have been made in the form of Java Applet with the concept of 2D Virtual Reality and it has been used since 2010 until 2016. However, for security reasons, since 2015 the development of Internet technology is no longer support Applet in its web browser. Therefore, the Balmer series Experimental Physics simulator needs to be overhauled into another form that is easier and safer to access in to the form of Android applications. In this research android applications engine generator has been made for Balmer series Experimental Physics simulator so that applications based on Java Applet on PC can be run in Android gadget. The engine consists of image processing, object transformation, browser connector, interactor and visualizer. In this report we show the Balmer series Experimental Physics modules that have been successfully created in the form of android applications.

Simulation of computational fluid dynamic (CFD) in isothermal fluid flow is very important in engineering and modeling problem. The simulation can explain the closed-flow behavior as complement with experimental field. Lid Driven Cavity as the tool for CFD benchmark is important to investigate. Lattice Boltzmann Method (LBM) as non-conventional CFD is used as present method investigation. The keys parameters of lid driven cavity problem are Reynolds number and the parts of moving boundaries that driven the cavity. In the present investigation was used the variations of Reynolds numbers: 500, 1000 and 2000. The lid that driven the cavity are top and bottom part. The results presented in visual flow pattern and in two dimensional velocity plot.

As healthcare is becoming one of the most rapidly changing industries by the increasing type of diseases, technology plays an important role in helping medical staffs solve those medical problems. Soft tissue tumors are tumors in the musculoskeletal system that involve soft tissue (tissue other than bone tissue). It includes muscle tissue, nerves, blood vessels, fat, and connective tissue. This soft tissue tumor is divided into two, namely benign and malignant. To prevent any medical errors in classifying patients' data, one machine learning called Stochastic Support Vector Machine is being studied. In this study, we will evaluate soft tissue tumor patients' data in Nur Hidayah Hospital, Yogyakarta, Indonesia using Stochastic Support Vector Machine to see its accuracy. The result is encouraging that Stochastic Support Vector Machine works better than the original Support Vector Machine.