Factors Influencing Residential Settlement Preferences in the Ciptomulyo Urban Village Industrial Area

Housing is one of the fundamental human needs. In assembly this housing require, each person needs a put to dwell. When determining the location to reside, individuals have their own preferences. One of these residential preferences is to live in an industrial zone. Water pollution occurred in Ciptomulyo Sub-district due to a leather company disposing of its liquid waste into the river in 2014, and its impacts are still felt until now. Moreover, many residential houses are situated very close to industrial areas. The purpose of this research is to recognize the components that influence the residential preferences of the community. The method used for this study is multiple linear regression analysis. The research findings identified ten factors that influence the residential preferences of the community: government facilities, educational facilities, electricity infrastructure, distance to the industrial area, length of residence, occupation, land price affordability, and security. Residential preferences are dominated by proximity to public facilities and ignore environmental health and comfort factors, so that for the sustainability of residential areas, it is necessary to integrate with industrial areas to play an active role in maintaining the potency.


Introduction
In human life, there are three fundamental needs, one of which is housing.Based on the Republic of Indonesia Law No. 1 of 2011 a house is defined as a building intended for habitation, providing a suitable living space, a place for family development, reflecting the dignity and self-esteem of its occupants, and serving as an asset for its owner.In meeting this housing need, every individual needs a place to reside.When determining the location to reside, individuals have their own preferences.Preferencing is the best possible desire and can reflect the degree of preference [1].Residential preferences refer to an individual's desire regarding the location of their residence, influenced by various factors [2], [3].Communities can have diverse preferences in choosing a place to reside [4], [5].
When determining a place to reside, there is also a tendency to consider the presence of factories and the surrounding environment.As a result, some communities choose to live in industrial areas, mainly due to the availability of job opportunities and easy accessibility [6], [7].Based on this concept of residing, it can be understood that a dwelling place should be habitable.However, there are still many settlements with poor living conditions.One example of such settlements is located around industrial activities.According to [7], areas close to industrial zones are likely to experience environmental pollution.
The industrial sector is a crucial player in the development and economic growth of a region, especially in lower-middle countries [6].It moreover serves as an exertion to meet the requirements related to products and administrations.The presence of industries creates new job opportunities, but along with the positive impacts, industrial activities can also cause negative effects on the surrounding areas [8], [9].The influence resulting from industrial activities leads to the emergence of settlements in the vicinity, and as a result, the impacts of industrial activities will be felt by the residents of these settlements.
Malang is a city that houses numerous industries, ranging from large to small-scale industries.In the Spatial Plan of Malang City (RTRW Kota Malang), it is mentioned that the increasing economic growth in Malang is supported by various sectors, including the industrial sector.As industries continue to grow, environmental concerns also receive significant attention [10].According to the Malang City Spatial Plan for the period 2010-2030, there are five designated industrial areas in the city.Among these five locations, based on land area measurements using ArcGIS and satellite imagery from the year 2021, the Blimbing and Ciptomulyo sub-districts have the largest industrial and warehouse areas, with Blimbing covering an area of 53.34 hectares and Ciptomulyo covering 33.96 hectares.Between these two sub-districts, Ciptomulyo has a smaller difference in land area between industrial and warehouse zones and residential areas, with residential land covering an area of 30.87 hectares.
There are various types of industries in Malang City, particularly in the manufacturing sector.The manufacturing industries contribute significantly to the Gross Regional Domestic Product (PDRB).However, these industries also pose serious environmental problems.According to Dharmawan [11], water pollution occurred in Ciptomulyo sub-district due to a leather company discharging its wastewater into the river in 2014.This wastewater not only contaminated the river but also polluted the groundwater, rendering it unsuitable for the residents who rely on well water as their source of clean water.The impacts of this pollution are still being felt by some communities residing near the designated industrial areas in Ciptomulyo sub-district up until now.The issue of industrial waste is one of the significant concerns in the era of environmentally friendly industrialization [12].If the amount of waste and pollutants generated is substantial, it can pose health hazards to the local population [13].
Despite the negative impacts caused by industrial activities, many communities still choose to reside in the vicinity of the designated industrial areas in Ciptomulyo sub-district.Therefore, it is essential to understand the factors influencing people's preferences for living or residing in the industrial zone of Ciptomulyo using multiple linear regression analysis.Identifying housing preferences becomes an effective tool before formulating strategies to adapt and survive in the environment or considering relocation, as it indicates the level of community resilience in adapting to their surroundings [14].One of the analytical techniques that can be used to determine the factors influencing residential preferences is multiple linear regression.Previous research, like Armela et al. [15], has already employed multiple regression analysis to identify the factors affecting residential preferences.By utilizing multiple linear regression analysis, researchers can examine how various factors, such as economic opportunities, proximity to workplaces, access to amenities, environmental quality, and other socio-economic factors, influence people's decisions to reside in the industrial zone of Ciptomulyo sub-district.This information can be valuable in developing appropriate policies and strategies to address the challenges and enhance the quality of life for residents living near industrial areas.

Method
This research was conducted in the Ciptomulyo sub-district, Malang City.Ciptomulyo subdistrict covers an area of 89.70 hectares and has a population of 14,234 people.The sampling method used was Non-probability sampling, which means that only related members of the population have the opportunity to become respondents related to the research objectives [16], and with the purposive sampling technique.The criteria for purposive sampling used were the residents in the Ciptomulyo sub-district who were affected by the industrial area.The population data used was the total number of households (KK) in Ciptomulyo sub-district in the year 2022, which was 4,898 households.After performing the calculation using the Slovin formula, and then adjusting the sample using the formula to anticipate sample dropouts, a total of 388 respondents were needed.
The variables used in this research consist of factors influencing the choice of residence.The variable type consist of a dependent variable (Y), which is the community settlement preference, and several independent variables (X) that encompass various aspects of the living environment.The independent variables (X) include aspects such as Green Open Space Facilities (X1.1),Health Facilities (X1.2),Government Facilities (X1.3),Worship Facilities (X1.4), Educational Facilities (X1.5),Entertainment Facilities (X1.6), Trade and Service Facilities (X1.7),Public Transportation Availability (X1.8),Water Infrastructure (X1.9),Electricity Infrastructure (X1.10),Road Infrastructure (X1.11),Drainage Infrastructure (X1.12),Distance to City Center (X2.This research adopts a quantitative approach to determine the factors influencing community settlement preferences.To achieve this, multiple linear regression analysis is employed, which aims to establish the relationships or capacities between two or more variables [17].The results of multiple linear regression consist of coefficient of determination, F-test result, and T-test result.Data for the research is collected through both primary and secondary surveys.The primary survey involves direct data collection in the field using a questionnaire.The questionnaire is designed to gather information about the community's preferences regarding their place of residence in the industrial area and their current living conditions.A Likert scale with a range of responses from 1 to 5 is used in the questionnaire.The data gathered on the factors influencing settlement preferences are considered ordinal data, which is then converted using the successive interval method to transform it into interval data before conducting the regression analysis.

Multiple Linear Regression Analysis 3.1.1 Independent Variables's Contribution to The Dependent Variables
The magnitude of the influence of the regression model in predicting the dependent variable is measured using R Square / Coefficient of Determination.A low R-squared value indicates how little the independent variable(s) can explain the dependent variable.Adjusted R-squared is the value of R-squared used in this research because when using R-squared, it increases with the addition of each independent variable, whereas the value of Adjusted R-squared can increase or decrease when adding one independent variable to the model.Table 1.Coefficient of Determination Analysis.Model R R Square Adjusted R Square 1 .736 a 0.542 0.502 The resulting output has an adjusted R-squared value of 0.502, as seen in the table above.This indicates the extent of the contribution of independent variables to the dependent variable (residential preference), which is 50,2%.It can also be concluded that 50,2% of the variation in residential preferences can be explained by the variation in the independent variables used in this study.The remaining 49,8% influence is explained by other factors outside the model.

F-test
To confirm whether the independent factors collectively have a significant impact on the dependent variable, an F-test is conducted.If the significance value (sig) < 0.05 or the calculated Fvalue (F-hitung) > the tabulated F-value (F tabel), then the alternative hypothesis (Ha ) is accepted, and the null hypothesis ( H0 ) is rejected.This means that the independent variables have a significant simultaneous influence on the dependent variable.On the other hand, if the significance value (sig) > 0.05 or the calculated F-value < the tabulated F-value, then H0 is accepted, indicating that the independent variables do not have a significant simultaneous influence on the dependent variable (Ghozali, 2018).The analysis result table for F-test in this research can be seen in table below.Based on the information provided in the F-test table, the calculated F-value is 13.592 with a probability or significance level of 0.000.It is evident that the obtained probability is smaller than 0.05, and the calculated F-value is greater than the tabulated F-value.Consequently, Ho is rejected, and Ha is accepted.Thus, it can be concluded that residential preference can be predicted using the regression model.

T-test
The T-test is used to evaluate whether the independent variables can significantly influence the dependent variable.The basis for the decision making of the T-test is as follows: if the sig < 0.05, or if the calculated T-value (t hitung) > tabulated t-value (t tabel), then it can be concluded that the alternative hypothesis (Ha) is accepted, and the null hypothesis (Ho) is rejected.In other words, there is a partial effect of the independent variable on the dependent variable.On the other hand, if the significance value (sig) is > 0.05, or if the calculated F-value (F hitung) is less than the tabulated Fvalue (F tabel), then Ho is accepted.Consequently, there is no significant partial effect of the independent variable on the dependent variable (Ghozali, 2018).This research employs a confidence level of 95%, which yields a tabulated t-value (t tabel) of 1.966.Based on the T-test analysis, there are ten independent variables that significantly influence the dependent variable.These influential variables were determined based on two criteria: a significance value (sig) < 0.05 and a calculated t-value (t hitung) > the tabulated t-value (t tabel), as presented in the previously mentioned table.The ten significant independent variables are as follows: Health Facilities (X1.2),Government Facilities (X1.3),Education Facilities (X1.5),Electricity Infrastructure (X1.10),Distance to Industrial Area (X2.2),Length of Residency (X3.3),Occupation (X3.5),Land Price Affordability (X4.2),Air Quality (X5.1), and Security (X5.6).

Interpretation of Regression Model
According to the findings of the T-test conducted, the independent variable included in the regression model is a significant factor.The independent variables to be used in the regression model, constant values, and coefficient values are listed in the table below: The constant of this regression equation is -1.807, which means that when the values of all X variables are 0 or when there is no contribution from the X variables, the community's settlement preference is -1.807.This constant value also supports the theory of aspects that influence community settlement preferences, such as the availability of facilities and infrastructure in the settlement, easy access, affordable prices, and a comfortable environment.As a result, the community has a preference or desire to settle in that location.
The variable "Health Facilities" has a beta coefficient value of 0.121.A positive coefficient value for the "Health Facilities" variable means that the more complete the health facilities available in Ciptomulyo Urban Village, the higher the community's desire to live there (Y) will increase by 0.121, assuming other variables are constant.Based on the results of the primary survey, 44% of the respondents feel that their health facility needs are met because there is already a community health center (puskesmas) in Ciptomulyo Urban Village.However, 9% of the respondents feel that the health facilities in Ciptomulyo Urban Village are insufficient.Therefore, to increase the settlement preferences of the community, it is necessary to improve the availability and services of health facilities.These research findings are consistent with a study by Azizah et al (2018), which highlights the importance of main infrastructure such as educational facilities in supporting daily activities as essential elements for settlement with high levels of necessity.
The "Government Facilities" variable has a beta coefficient value of 0.104.A positive coefficient value for the "Government Facilities" variable means that the more complete the government facilities that can facilitate the community in Ciptomulyo Urban Village, the higher the community's desire to live there (Y) will increase by 0.104, assuming other variables are constant.Based on the monograph of Ciptomulyo Urban Village in 2022, the area has 31 meeting halls and an art building.Additionally, there are several government offices in Ciptomulyo Urban Village, such as the Environmental Department and the Public Works Department.The presence of these buildings leads 47% of the respondents to feel that their government facility needs are met.However, 16% of the respondents feel that their needs are not fully met, possibly due to the buildings' underutilization.Therefore, to increase the settlement preferences of the community, it is also necessary to improve the facilities of government facilities.These research findings are in line with Drabkin's theory [18], which suggests that communities tend to choose locations that have good service and availability of facilities and infrastructure.
The "Educational Facilities" variable has a beta coefficient value of 0.191.A positive coefficient value for the "Education Facilities" variable means that the more complete the education facilities in Ciptomulyo Urban Village or each increment of one level higher in the education facilities variable, the higher the community's desire to live there (Y) will increase by 0.191, assuming other variables are constant.Community access to educational facilities is expressed as the existence of complete educational facilities at all levels; Kindergarten to high school level in Ciptomulyo Urban Village.According to the survey results, the majority of respondents feel that their needs are met for preschools (36%), elementary schools (59%), and junior high schools (47%).However, for high schools, only 34% of the respondents feel that their needs are met.Based on the existing conditions and the community's perceptions of the availability of education facilities, education facilities become one of the factors that increase the settlement preferences of the community.These research findings are consistent with a study by Azizah et al (2018), which emphasizes that main infrastructure, such as health facilities, is essential in supporting daily activities as critical elements for settlement needs.
The "Electricity Infrastructure" variable has a beta coefficient value of 0.163.A positive coefficient value for the "Electricity Infrastructure" variable means that the easier the access to electricity infrastructure in Ciptomulyo Urban Village, the higher the community's desire to live there (Y) will increase by 0.163, assuming other variables are constant.According to the primary survey, 53% of the respondents chose the answer that the availability of electricity infrastructure falls under the "easy" category.This is because respondents experienced power outages only 1-2 times or very rarely in a year.Based on the respondents' choices and the existing conditions, electricity infrastructure becomes one of the factors that increase the settlement preferences of the community.These research findings are in line with the theory based on Catanese and Snyder (1989), where one of the eight criteria influencing the choice of residence is facilities, and one of the facilities mentioned is the ease of obtaining electricity.
The "Distance to Industrial Area" variable has a beta coefficient value of 0.085.A positive coefficient value for the "Distance to Industrial Area" variable means that for each one-level increase in the distance to the industrial area in Ciptomulyo Urban Village, the community's desire to live there (Y) will increase by 0.085, assuming other variables are constant.In Ciptomulyo Urban Village, there are many industrial locations that have various impacts such as odors, damage to electronic devices, and respiratory disturbances.Respondents are aware that the farther their settlements are from the industrial locations, the less they will be affected directly by the presence of factories or industrial areas.As a result, the settlement preferences of the community will increase.
The "Length of Residency" variable has a beta coefficient value of 0.185.A positive coefficient value for the "Length of Residency" variable means that for each one-level increase in the length of time respondents have lived in Ciptomulyo Urban Village, the community's desire to continue living there (Y) will increase by 0.185, assuming other variables are constant.Based on the results of the primary survey, 38% of the respondents have been residing in Ciptomulyo Urban Village for a long time, and an additional 27% have been living there for a very long time.These findings align with the statement by Surjono et al (2021), which suggests that the longer respondents live in an area, the stronger the emotional attachment between individuals and their place of residence.
The "Occupation" variable has a beta coefficient value of 0.087.A positive coefficient value for the "Occupation" variable means that for each one-level increase in the association of the community's occupation with the industry, the community's desire to live there (Y) will increase by 0.087, assuming other variables are constant.In this research, occupations are categorized based on their connection with the industry.The majority of the community members work by operating small shops (warung) in their homes.Additionally, there are residents who work as sellers in nearby markets, and there are also respondents who work in permanent industrial positions (employed in factories).These research findings align with a study by Maryati (2018), which states that a housing development can be built based on demand from a group of individuals who sometimes work in the same field or industry.
The "Affordability of Land Price" variable has a beta coefficient value of 0.107.A positive coefficient value for the "Affordability of Land Price" variable means that for each one-level increase in the affordability of land prices in Ciptomulyo Urban Village (i.e., if land prices become cheaper), the community's desire to live there (Y) will increase by 0.107, assuming other variables are constant.Based on the results of the primary survey, 36% of the respondents feel that the land prices in their area are affordable.These research findings are in line with Azizah et al's study [4] , which states that in making decisions regarding settlements, communities will always compare the cost required for the settlement process.In Ciptomulyo Urban Village, the land is relatively affordable because some houses are built on land owned by PJKA, and as a result, some residents only pay land rent, while others do not have to pay for the land at all.
The "Air Quality" variable has a beta coefficient value of 0.183.A positive coefficient value for the "Air Quality" variable means that for each one-level increase in the air quality in Ciptomulyo Urban Village, the community's desire to live there (Y) will increase by 0.183, assuming other variables are constant.Based on the results of the primary survey, 31% of the respondents feel that the air quality in Ciptomulyo Urban Village is relatively good, although there are occasional odors from the nearby industrial area.On the other hand, 30% of the respondents feel that the air quality in their settlement is poor due to strong odors from the industrial area, causing headaches.The industrial activities have an impact on the air quality around Ciptomulyo Urban Village, originating from the production processes and industrial vehicles passing through the area.The impacts perceived by the community vary, ranging from odors to damages to electronic items.These research findings are in line with Harris et al's study (2020), which states that the environment is a fundamental consideration in determining a place of residence.As respondents' social status increases, so does their need for a good living environment, including air quality.
The "Security" variable has a beta coefficient value of 0.336.A positive coefficient value for the "Security" variable means that for each one-level increase in the safety level in Ciptomulyo Urban Village, the community's desire to live there (Y) will increase by 0.336, assuming other variables are constant.Based on the perception of the respondents regarding the safety level in Ciptomulyo Urban Village, 42% of the respondents feel that their environment is very safe.The quality of the location, including safety, is a significant factor considered by people when choosing a place of residence (Harris et al., 2020).An environment that is safe is sought by respondents for their peace of mind, making them more inclined to settle or live in that area.
These research findings are consistent with Azizah et al.'s study (2018), which highlights that a safe environment contributes to a comfortable feeling for the community, as they do not need to be anxious about the surrounding conditions of their settlement.The community's dependence on public facilities such as schools and health facilities makes the tendency to move is low and the community tolerates an uncomfortable environmental quality.However, for the sustainability of life, stable environmental quality in residential areas needs to be considered and activities that are sources of waste need to be involved in maintaining environmental quality in residential areas.

Conclusion
From the multiple linear regression results, it is found that the independent variables collectively have a significant influence related to settlement preferences of the community.50,2% of the variables tested influence people's decisions related to housing location, while the rest are influenced by external variables which means that most area's conditions will be an important reasoning in choosing a housing location, specifically in this research area's conditions that are dominated by industrial activities.These interpretation factors can be seen from the dependent variable which determines housing location decisions which are very dependent on industrial activities seen from that have a significant partial influence on the settlement preferences of the community.All significant variables have positive coefficients, which means that if the values of the variables "Health Facilities," "Government Facilities," "Educational Facilities," "Electricity Infrastructure," "Distance to Industrial Area," "Length of Residency," "Occupation," "Affordability of Land Price," and "Security" increase by one level, the settlement preferences will also increase.Based on the beta coefficient values, the factor that has the most significant influence on settlement preferences is "Security" with a beta coefficient value of 0.336.On the other hand, the factor that has a significant influence but with the smallest effect is "distance to industrial area" with a beta coefficient value of 0.085.

Table 4 .
Constant and Independent Variable Coefficient.