Spatial Rearrangement of Manufacturing Industrial Zones Based on MCDM in Mojokerto Regency. Case Study Northern Mojokerto Region

Mojokerto Regency is part of the national strategic area (Gerbangkertasusila) in East Java. Gerbangkertasusila serves as the driving force behind East Java’s economy, with the manufacturing industry becoming the prime mover as it contributes to 2022 GRDP by 56.68 percent. The spatial plan of Mojokerto Regency includes the development of an industrial estate in the northern part, specifically in Jetis and Dawarblandong Districts, covering a total area of 8.553 hectares. However, the government’s attempt to make industrial estates has only been realized by 2.65% since 2012. This research aims to assess the suitability of space allocation within the industrial estates in the northern region of Mojokerto. The research method uses spatial analysis approach, multi criteria decision making (MCDM). Spatial data were obtained through RBI and Bhumi ATR, also Mojokerto Regency’s RTRW as comparative data to assess the suitability of industrial locations. This study also considers other spatial aspects, such as projections of land use based on cellular automata, population, infrastructure, transportation, and physical conditions. Analysis result shows that the allocation of space requirements for industrial estate development is 1,081 hectares or 12.64 percent of the original plan. The area is much smaller than the planned area in the Mojokerto Regency’s RTRW. The study identifies four potential locations for Industrial Estate development in the northern Mojokerto region, consisting of two in Jetis District and two in Dawarblandong District. The research results can be an input for the industrial cluster development in the northern Mojokerto region.


Introduction
In the process of urban development, the conversion of land use from non-urban to urban uses occur, including the growth of urban land use and the transformation of green areas into industrial land [1].These two phenomena are interconnected; on one hand, urban land use expansion can lead to an increase in industrial land use.Conversely, agglomeration and industrial growth can drive urban land use expansion [2].Industrial activities play a crucial role for conditioning urban innovation and development in shaping the city's form and act as the prime mover of urban growth [3].In developing countries, the prime mover refers to the industrial sector that provides numerous job opportunities, leading to a shift of the population from the agricultural to the informal sector.According to Balchin Isaac & Chen, Indonesia is considered a contemporary industrializing country, where the majority of the workforce is engaged in agriculture, but the emergence of industrial activities linked to the global market gradually transforms its socio-economic characteristics [4].As long as industrial activities remain the prime mover in a region or city, industrial land growth will continue to increase along with urban expansion.The industrialization process in Indonesia began in the 1980s and peaked in the early 2000s [5].However, this phenomenon did not last long due to a shift in economic structure from the manufacturing sector towards the service and informal sectors.This shift is evident from the role of the manufacturing industry in GDP growth, which was 20% in 1971, 43% in 1980, 40% in 2000, and eventually decreased to 20% by 2019.On the other hand, the service and informal sectors continued to grow, accounting for 35% in 1971 and reaching 54% in 2019 [6].This phenomenon is referred to as deindustrialization.Deindustrialization in Indonesia is classified as negative deindustrialization, which can threaten Indonesia's competitiveness and economic growth [7].Moreover, Indonesia is considered a country experiencing premature deindustrialization, an economic phenomenon seen in developing nations where the peak of manufacturing in terms of employment and output is reached at significantly lower income levels and shares compared to early industrialized countries [8].However, before national industries could become robust, according to Ministry of the State Secretariat (Kementerian Sekretariat Negara Republik Indonesia) the national economy rapidly shifted towards the service sector [9].Therefore, as a response to the middle-income trap, Indonesia currently focuses on promoting industrialization since economic diversification is seen as making a positive contribution to economic representation [10].This objective is explicitly stated in Law No. 27 on the Long-Term National Development Plan (RPJPN) from 2005 to 2025.
The second largest metropolitan area after Jabodetabek, Gerbangkertasusila (GKS), serves as the economic growth center of East Java Province, specializing in the industrial and trade and service sectors.GKS plays a vital role in driving East Java's economy, with the manufacturing industry being the primary contributor, accounting for 56.68 percent of the 2022 GRDP.As one of the regencies within GKS, Mojokerto Regency experiences rapid development in its industrial sector and infrastructure [11].With the expansion of industrial activities, Mojokerto Regency undergoes non-built-up land conversion, particularly converting agricultural land into built-up areas for residential, commercial, and industrial purposes [12].
The Mojokerto regency is divided into two poles separated by the Brantas River, and this area is commonly referred to as the north and south sides of the Brantas River in Mojokerto Regency.The central activities in these two areas differ, with the southern region, including Mojokerto City, being dominated by residential, commercial, office, and public service activities, while the northern region is dominated by residential, agricultural, and plantation activities.The study area in this research is located in two of the four sub-districts in the northern part of the Brantas River.The Gross Regional Domestic Product (GRDP) of Mojokerto Regency in 2022 amounted to 63.6 trillion Indonesian Rupiah, with the largest contribution coming from the manufacturing sector at 36.1 trillion or 56.6% [13].Therefore, the industrial sector is the leading sector in Mojokerto regency.The northern region of the Brantas River, including the districts of Jetis and Dawarblandong shown in Figure 1, is designated for the development of a large industrial estate covering 8.553 hectares.However, the current established industrial estate only spans 227 hectares.In other words, the realized industrial estate represents a mere 2.65% of the Mojokerto Regency's spatial planning for the period 2012-2032 [14].This indicates a lack of precision in the spatial planning of Mojokerto Regency, as it has been a decade since the plan's implementation, yet the realization rate remains low.Hence, to optimize the development of the industrial estate in Mojokerto Regency, particularly in the northern region of the Brantas River, spatialbased planning is essential to determine potential locations suitable for industrial land development.Enhancing the efficiency of industrial land use is highly significant for the sustainable development of industries [15].This paper aims to identify suitable locations for industrial land development using the Multi-Criteria Decision Making (MCDM) approach.Furthermore, several other spatial aspects need consideration in reorganizing land use planning in the northern region of the Brantas River, including land use modeling, physical and environmental factors, and transportation.The findings of this paper are expected to provide valuable insights for the Mojokerto Regency government (see figure 1) in formulating land use planning for the future.

Physical and Environmental Aspects
Land capability analysis is conducted based on Indonesia's Minister of Public Works Regulation No. 20/PRT/M/2007, which defines nine Land Capability Units (LCUs) as limiting factors for determining land capability classes, encompassing seven thematic layers; morphology, workability (land ease of work), slope stability, foundation stability, water availability, drainage, erosion, waste disposal, and natural disasters.These analyses involve overlaying specific criteria for each LCU to determine the final land capability classes, specifically for the determination of industrial cluster zones.A weighted suitability model analysis is developed using GIS techniques by taking into account both land characteristics and user requirements to propose suitable locations for manufacturing industry [16] This analysis is based on Multi-Criteria Evaluation, which considers various thematic layers to analyze problems, producing alternatives [17].These models use a common measurement scale to integrate diverse and dissimilar inputs, facilitating a comprehensive and unified analysis.The analyses inform the further guidelines for development requirements and limitations in accordance with Minister of Public Works Regulation.The steps of land capability analysis are illustrated in Figure 2.

Figure 2. Steps of Land Capability Analysis
Every Land Capability Unit (LCU) is composed of various patterns of physical and environmental aspects for the determination of the industrial estate in Jetis and Dawarblandong.Weighted overlay analysis is used using a Geographical Information System (GIS) tool.A weighted overlay analysis is a spatial analysis technique that entails assigning weights or significance values to individual layers and subsequently layering and aggregating them to derive a comprehensive suitability score for each specific location and is based on the principle of Multi-Criteria Evaluation [18].MCDM (multi criteria decision making) methods serve as a crucial tool for decision-makers in structuring a decision-making problem, identifying their preferences, and building a decision recommendation consistent with those preferences [19].
The analysis of land capability by Indonesia's Minister of Public Works involves several key steps as follows: First, a comprehensive weighted overlay analysis is conducted for each LCU to determine its capability level.Next, a capability value is assigned to each level within the LCUs, ranging from 1 (lowest) to 5 (highest).These values are then multiplied by the corresponding weights (Table 1) assigned to each LCU, based on their influence on urban developmeSnt.The LCUs are superimposed by summing up the multiplication results onto one map, providing a range of values indicating land capability in the planning area.To divide the land capability classes, specific ranges of values are determined, resulting in distinct land capability zones as shown in Table 2. Employing GIS analysis proves to be a highly time-efficient approach, as it effectively manages, processes, and upgrades extensive georeferenced data from various sources at multiple spatial and scale levels.Additionally, a GIS-based approach offers cost reduction benefits in site selection, making it a more economical option for decision-making processes [20]

Forecasting Land Use
The simulation model for land use prediction is conducted using Cellular Automata (CA) and operated in LanduseSim 2.3.1 software developed by Nursakti Adhi Pratomoatmojo (Institut Teknologi Sepuluh Nopember).One of the advantages of this software is that users can control the driving and constraint factors of land use changes based on the characteristics of each region.The land use prediction model in this software goes through four stages: determining the driving and inhibiting factors of land use changes, creating transition potential and zoning, defining land use transition rules, and simulating the model.The simulation is only conducted for industrial and settlement land uses, which are areas of human activities.
The base maps used for the simulation are the land use maps of 2013 and 2023 obtained from georeferenced satellite imagery and the basic plan of Mojokerto's spatial arrangement.The land use maps are digitized at a scale of 1:5000.The resulting land use classification consists of six classes: water body, forest, industry, settlement, plantation, and agriculture.Then, there are variables that drive the changes in land use, which will be used in creating the potential transition maps.The variables acting as driving and inhibiting factors include distance to the toll gate, distance to infrastructure, distance to industries, distance to settlements, distance to primary and secondary roads, and distance to service centers.
The Transition Potential Map is created by spatial weighting between distance variables.These weights are obtained through the Analytical Hierarchical Process (AHP), resulting in different weights for each variable for settlement and industrial land, as their characteristics differ.The variable scores for settlement land are as follows: distance to the toll gate (0.06), distance to infrastructure (0.14), distance to industries -farther is better (0.03), distance to settlements (0.27), distance to main roads (0.12), distance to secondary roads (0.28), and distance to service centers (0.1).Meanwhile, the variable scores for industrial land use are: distance to the toll gate (0.18), distance to infrastructure (0.07), distance to industries (0.3), distance to settlements -farther is better (0.09), distance to main roads (0.25), distance to secondary roads (0.05), and distance to service centers -farther is better (0.06).

Industrial Zone Allocation
In determining suitable land for establishing industrial areas, the author followed the guidelines outlined in the Regulation of the Minister of Industry of the Republic of Indonesia Number 30 of 2020 concerning Technical Criteria for Industrial Zoning.By adhering to these guidelines, it is expected to identify industrial areas that can promote the utilization of local resources and environmental impact control.
The variables used also align with the technical criteria, such as the distance to the district center, distance to settlements, topography, distance to water sources, and distance to main roads, which were then analyzed using the Multi-Criteria Decision Analysis (MCDA) method and spatial weighting technique with the assistance of ArcMap software.MCDA was utilized to determine the weight of each variable before applying the spatial weighting technique.

Network Analyst -finding route
Network analysis involves finding the most optimal route by considering factors such as distance, cost, and road ability [21] to support industrial activities.Network Analysis uses GIS tools and takes into the road class [22] categories stipulated in Law Number 22 of 2009 concerning Road Traffic and Transportation.GIS tools consist of Road Network data extracted using Open Street Map using QGIS and analyzed using ArcGIS.The work stages of network analysis are illustrated in Figure 3.

Level of Service
The analysis of Level of Service (LOS) aims to determine whether the road services provided are sufficient to meet the mobility needs of the community and industry or unfulfilled yet.In this study, the calculation of LoS takes an approach by using the Google Traffic Map.This map contains daily, hourly, and minute-by-minute traffic data, making it a reliable and accauracy source for assessing traffic conditions [23].
This method was introduced by Ali & Abid when they analyzed LoS in Baghdad, Iraq.The researchers attempted to utilize this approach on the primary roads, encompassing both local and collector streets within Jetis-Dawarblandong. Traffic conditions on Google Traffic Maps were represented by different colors: green indicated smooth traffic, yellow indicated moderately smooth traffic, red indicated traffic that was not smooth, and dark red indicated very slow traffic, which is a total traffic jam.The data collection process was carried out during peak hours, which were 06.00-07.00 in the morning, 12.00-13.00during the day, and 18.00-19.00at night.The output of this method is the delay time, measured in seconds.The longer the delay time, the longer it takes for drivers to pass through the road or the road conditions are having problems.The formula used to calculate delay time is as follows [24].To determine the final speed limit, the percentage of existing speed with the speed based on the road function is classified.As a result, researchers have taken a sample of roads to measure travel time directly, and based on the survey, travel time calculated through Google Maps is deemed acceptable for applying this formula.The level of service classification is laid out in Table 3.The roads chosen are frequently used by the community and industrial logistics and serve as primary local roads and primary collectors.These roads are Jalan Raya Mlirip, Jalan Raya Jetis-Wringinanom, Jalan Dawarblandong-Kedamean, Jalan Meyjen Sukono, Jalan Raya Kupang and Jalan Sumberwuluh.

Indentification of land capability
The overlay analysis of land capability units reveals that the planning areas designated for the Jetis and Dawarblandong industrial clusters demonstrate a notable predominance of sufficient calcification, as evidenced by the comprehensive findings presented in Table 4 and Figure 4.This analysis involves the integration of nine Land Capability Units (LCUs) to generate a comprehensive land capability map for the designated areas.This result is noteworthy as it aligns with the previous findings [11] regarding Mojokerto's land capability, which also suggest that these classes maintain a high level of capability.As per Indonesia's Minister of Public Works Regulation No. 20/PRT/M/2007, the identification of suitable zones for manufacturing industries in Jetis and Dawarblandong, taking into account environmental sustainability and the essential stages of spatial planning in Indonesia, is confined to areas categorized as "sufficient".This classification necessitates the fulfillment of specific conditions, including the implementation of integrated wastewater treatment facilities at the industrial area and the establishment of only non-hazardous manufacturing industries.

Land Use Change
The changes in land use over the past ten years in North Mojokerto were obtained from the overlay results of land use maps for the years 2013 and 2023 using ArcGIS software.The dominant land uses are plantations/farms covering an area of 8,957.2hectares, agriculture covering 2,749.3hectares, and settlements covering 1,841.91 hectares.This is contrary to the land use plan of Mojokerto Regency, where industrial land use, which was supposed to dominate, actually occupies a very small area of 227.08 hectares.From Table 5, there are two patterns of land use changes in the North Mojokerto region.Firstly, there is a conversion from agricultural land to built-up land (settlement and industry).The conversion of agricultural land to settlements mostly occurs in Jetis District, covering an area of 315.55 hectares.Similarly, the conversion of agricultural land to industrial land mostly occurs in Jetis District, covering 83 hectares.The changes in industrial land use in this area are indeed minimal, as the existing industrial conditions are still limited in this region.The maps of land use changes are shown in Figure 5.

Land Use Modeling
Land use prediction is useful for formulating effective land use and planning policy suitable for local conditions, thus supporting local socioeconomic development and global environmental protection [25] to support infrastructure planning effectiveness.In studies concerning land use changes, Cellular Automata (CA) has been proven as an appropriate method for assessing spatial dynamics of land use through simulations and land use modeling based on user-defined scenarios [26,27].The land use prediction in this paper utilizes Landusesim software.In LanduseSim, CA works in a multi-loop iteration according to the specified time-steps [27].As the initial stage of simulating land use changes, it is necessary to provide a transition potential map.In Figure 6 in the case of the study area, accessibility factors such as distances to roads, service centers, settlements, industries, and infrastructures are assumed to be the drivers of residential and industrial land use growth.On the other hand, protected areas like water bodies, Sustainable Agricultural Land (LP2B), and spatial planning schemes are assumed to be inhibiting factors for land growth.Based on the simulation results using CA, it is evident that residential and industrial areas will continue to experience growth, especially in Jetis District.Jetis District directly borders Mojokerto City and has direct transportation access to Sidoarjo and Surabaya.The growth of residential areas in Jetis District is influenced by proximity to roads and existing settlements.Table 6 displays the information that over the next twenty years, it is estimated that the residential land use in the Northern Mojokerto Region will increase by 326.26 hectares, reaching 2,168.17hectares.Meanwhile, industrial land use is projected to grow by 160.06 hectares, reaching 387.14 hectares.The main driving factors for industrial growth are proximity to existing industries and major roads.As a result, industries are primarily seen to grow in Jetis District, as the industrial areas in the Northern Mojokerto Region are concentrated in Jetis District, while Dawarblandong District only houses warehouses.Like in other developing countries, this land use modeling in Mojokerto regency can be concluded to have experienced rapid industrial land growth [11] which is the conversion of green areas into industrial [1,12].

Industrial Land Suitability
The purpose of industrial site selection is to find the desired conditions according to predetermined criteria.In the process of selecting an industrial location, several factors are required to choose the most optimal site.The decision-making process relies on various data related to the site selection.There are numerous factors that can influence the development of an industrial area, such as the community, environment, and smooth operation of industrial activities [28].Therefore, an analysis for determining suitable land is necessary for the establishment of an industrial area.
In conducting the analysis for determining suitable land for the establishment of an industrial area, the author utilized guidelines from the Regulation of the Minister of Industry of the Republic of Indonesia Number 30 of 2020 concerning the Technical Criteria for Industrial Zoning.By referring to these guidelines, it is expected to identify industrial areas that can promote the utilization of local resources and environmental impact control.
The variables used also refer to the technical criteria, such as distance to the regency center, distance to settlements, topography, distance to water sources, and distance to main roads, which are then analyzed using the Multi-Criteria Decision Analysis (MCDA) method and overlay technique with the assistance of ArcGIS software.In determining the best alternative locations for establishing the industrial area, the Analytical Hierarchy Process (AHP) is employed to assess the importance of each criterion.This assessment is carried out subjectively, involving experts and local government officials.The experts involved in this criterion assessment are from the Department of Industry and Trade, the Regional Development Planning Agency of Mojokerto Regency, and academics from the Department of Urban and Regional Planning at the Institut Teknologi Sepuluh Nopember (ITS).After obtaining the scores for each criterion in Table 7, the determination of industrial locations is then carried out using spatial weighting in ArcGIS software.Based on the analysis results, it is found that Jetis District has suitable land for industrial areas covering 2,566.9hectares, while Dawarblandong District has suitable land for industrial areas covering 748.1 hectares.It can be observed on the map in Figure 8 that the suitable land for industrial areas is located along the Brantas River and Bengawan Solo River to facilitate water supply, as the study area faces a high vulnerability of drought.

Deliniating Industrial Land
Based on the analysis of industrial location suitability above, proposed industrial locations with moderate to high suitability levels are identified.Other factors used in the selection of proposed locations include ease of transportation access, proximity to water bodies, and being distant from densely populated settlements.The results obtained reveal four new industrial area locations, divided into two in Dawarblandong District and two in Jetis District.Presented in Figure 9 and Table 8, to determine the types of industries for each location, the author refers to the Mojokerto Regency Industrial Development Plan for the period 2020-2040.

Optimal Route
Looking at the map provided in Figure 10, it is evident that Industrial Area Plan 1 and 2 offer the best route to the Belahanrejo toll gate.Industrial Area Plan 3 has the most efficient route to the Penompo toll gate, and Industrial Area Plan 4 has the optimal route to the West Mojokerto toll gate.

Conclusion
As the majority of the areas are deemed suitable for planning as manufacturing industry zones, falling under the "sufficient" classification, the industrial zones are strategically located along the Brantas River and Bengawan Solo River, ensuring convenient access to water supply.The analysis then identifies four potential new industrial area locations, with two in Dawarblandong District and two in Jetis District.Furthermore, a transportation analysis confirms that each industrial zone has access to the nearest toll gate, ensuring ease of mobility.Nevertheless, some roads require special attention, and certain roads necessitate capacity improvements.
This research is well-suited to be undertaken due to the substantial demand and growing trend for industrial areas, as well as the diverse natural and artificial characteristics of Jetis and Dawarblandong.As a result, the process of determining the industry's location can be conducted more exact according to the criteria.Nevertheless, the proposed solution to tackle urban issues by the development of industries as leading to a healthier ecosystem and an improved standard of living for people or reducing pollution, must incorporate more persuasive and well-structured steps, utilizing scientific tools and techniques to achieve more effective outcomes.

Figure 3 .
Figure 3. Work Stage of Network Analysis-Finding Route

Figure 6 .
Figure 6.Drivers of Land Use Growth

Figure 7 .
Figure 7. Cellular Automata (CA) Predicted Settlement and Industrial Land Use Change 2028-2043 in Northern Mojokerto Regency

Figure 8 .
Figure 8. Industrial Land Suitability Based on Multi Criteria Approach

Figure 9 .
Figure 9. Industrial Location Plan Based on Multi-Criteria Approach

Figure 11 .
Figure 11.Land Use Planning in North Mojokerto Region Based on Spatial Analysis

Table 1 .
Weight of Landuse Capability 5

Table 3 .
Level of Service Classes

Table 4 .
Land Capability Unit Classification Land

Table 5 .
Land Use Change in Northern Mojokerto Region 2013-2023

Table 6 .
Settlement and Industrial Land Use Growth in Northern Mojokerto Regency

Table 8 .
Planned Industrial Sector 3.4 Transportation Analysis as a Support for Industrial Activity

Table 10 .
Area of Land Use Planning in North Mojokerto Region