Journal of Geospatial Science and Analytics
Journal of Geospatial Science and Analytics (JGSA), a peer-reviewed, open access journal dedicated to advancing the field of geospatial science and its transformative applications. As a multidisciplinary platform, JGSA seeks to foster innovative research, stimulate critical discussions, and bridge the gap between theory and practice in geospatial domains. Every issue in JGSA highlights groundbreaking contributions that reflect the dynamic evolution of geospatial technologies, methodologies, and analytics. Journal of Geospatial Science and Analytics (JGSA), a peer-reviewed, open access journal dedicated to advancing the field of geospatial science and its transformative applications. As a multidisciplinary platform, JGSA seeks to foster innovative research, stimulate critical discussions, and bridge the gap between theory and practice in geospatial domains. Every issue in JGSA highlights groundbreaking contributions that reflect the dynamic evolution of geospatial technologies, methodologies, and analytics.
CV. FoundAE (Foundation of Advanced Education )
Machine learning–based spatiotemporal modeling of earthquake occurrence for disaster mitigation: A case study of a seismically active region
Natural disasters, especially earthquakes, pose a significant threat that can cause significant damage to infrastructure and threaten public safety. West Java, a seismically active region, requires an effective mitigation system to minimize the impact of earthquakes. This study aims to develop an earthquake prediction model in West Java using a machine learning approach. The methods used are Long Short-Term Memory (LSTM) and Convolutional LSTM (ConvLSTM). LSTM is used to predict earthquake magnitude and depth and also prediction for 2025 - 2029, while ConvLSTM is used to predict the location of the earthquake epicenter. Historical earthquake data including location, magnitude, and depth are used to train the model. The results of the study showed that the ConvLSTM model was able to predict 104 earthquake events over the next five years with an RMSE of 7.907, MAPE of 32.65%, and of 0.245, while LSTM for magnitude produced an RMSE of 0.413, MAPE of 7.00%, and of 0.457, resulting in predictions with a magnitude range of 4.10 to 5.03 for all earthquake events for the next 5 years. Depth prediction with LSTM also showed significant results with an RMSE of 1.464, MAPE of 45.50%, and of 0.399, where most earthquakes were predicted to occur in Zone 2 and Zone 3, indicating that the depth of the earthquake tended to be shallow to medium in the next 5 years. These results can be used to increase public awareness and regional resilience through mapping earthquake-prone areas, as well as planning infrastructure strengthening and developing early warning systems.
Copyright (c) 2026 Muhammad Harun Yahya, Muhamad Syazali, Arifan Jaya Syahbana, Rofiqul Umam, Hirotaka Takahashi (Author) https://creativecommons.org/licenses/by-sa/4.0
Geospatial modelling of environmental determinants of malaria in Zimbabwe: A case study from Mudzi district
Understanding the environmental factors influencing malaria transmission is vital to strengthening early warning systems and implementing proactive intervention strategies. While Zimbabwe continues to experience seasonal malaria outbreaks, localized environmental risk assessments remain underexplored, particularly in high-burden districts such as Mudzi. This study employed a geospatial and statistical modelling approach to assess the relationship between environmental variables and malaria incidence in Mudzi District from June 2023 to February 2025. Monthly malaria case data were obtained from all 31 public health facilities across Mudzi, covering the period June 2023 to February 2025. The monthly data was aggregated into custom-defined health access clusters using proximity-based network analysis. Environmental predictors rainfall, land surface temperature, vegetation (NDVI), proximity to water bodies, elevation, and distance to healthcare facilities were derived from MODIS, Sentinel-2, Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), and ancillary datasets. Negative Binomial Regression (NBR) models were applied to account for overdispersion in count data, incorporating time-lagged variables and interaction effects. Spatial autocorrelation was assessed using Global and Local Moran’s I. Temperature (p < 0.001), distance to water (p < 0.001), distance to health facilities (p < 0.001), and stagnant water body area (p < 0.001) were significant predictors of malaria incidence. Time-lagged temperature effects (Lag 1 and Lag 2) improved model fit and revealed delayed ecological impacts. Vegetation (NDVI) exhibited significance in interaction with elevation (p = 0.003). Local Moran’s I identified spatial outliers, notably low-case clusters adjacent to high-incidence zones, highlighting reporting inconsistencies or intervention effects. Environmental conditions associated with malaria risk in Mudzi District exhibit both spatial and temporal heterogeneity. Predictors such as temperature and water proximity demonstrate lagged and interactive effects on transmission dynamics. These findings support the development of localized, seasonally adaptive malaria surveillance strategies in Zimbabwe.
Copyright (c) 2026 Kumbirai N Matingo, Panashe Nyengera (Author) https://creativecommons.org/licenses/by-sa/4.0
Geospatial analysis of factors affecting index of rice harvest success in West Java using INLA-based Bayesian models
Rice production during the 2012-2022 period tends to decline. Many factors lead to a decline in production, one of which is the low on potential of rice success. This issue is an obstacle to achieve food security. West Java is the third largest rice producing province in Indonesia. For this reason, the aim of this research is to establish the potential value of harvest success in West Java Province and form a spatial Bayes regression model of the potential value of harvest success of rice plants in West Java whose inference using the Integrated Nested Laplace Approximation (INLA) approach. The spatial Bayes regression model created is a linear mixed model which includes spatial random effects using the BYM2 method. The explanatory variables of the model are the number of farmer economic institutions, the number of agricultural extension workers, the number of farmer groups, the human development index, and the number of disaster events from 27 districts/cities in West Java with the potential value of harvest success as the response variable. The results of the spatial model show that the variables of the number of farmer economic institutions and the number of farmer groups have a significant influence on the potential value of harvest success. From the results of spatial mapping, it can be seen that there are neighborhood relationships that influence the value of potential harvest success where the eastern region in West Java tends to have a higher probability of harvest success.
Copyright (c) 2025 Novrizal Ramdhani, Glenaldo Achmad Zhafran Evito (Author) https://creativecommons.org/licenses/by-sa/4.0
Spatial modeling of the open unemployment rate in West Java using eigenvector spatial filtering and spatially varying coefficients
Spatial modeling serves as a crucial approach in examining the Open Unemployment Rate (OUR) as it accommodates spatial dependence and regional heterogeneity. This study aims to model the OUR at the regency/city level in West Java Province for the 2020–2024 period using a spatial regression approach, by combining the Eigenvector Spatial Filtering (ESF) and Spatially Varying Coefficients (SVC) methods through the spmoran package in R. The ESF model is employed to reduce spatial autocorrelation in residuals by incorporating eigenvectors derived from a spatial weights matrix, while the SVC model captures local variations in the influence of explanatory variables on OUR across regions. The results reveal that the best-performing models based on Moran’s I is ESF respectively, for each year. Several variables such as Gross Regional Domestic Product (GRDP) per capita at constant 2010 prices, number of poor people, and elevation were consistently significant and influenced regional variations in OUR. Spatial visualization of model predictions indicates a concentration of high OUR values in the northern and western regions of West Java, such as Bekasi City, Karawang Regency, and Depok City, while the southern and eastern regions, such as Pangandaran and Tasikmalaya Regencies, tended to have lower OUR values. These findings underscore the importance of spatial-based policy interventions in the employment sector.
Copyright (c) 2025 I Putu Aditya Brama Putra Cakra Negara, Rofiqul Umam, Alok Shukla (Author) https://creativecommons.org/licenses/by-sa/4.0
Two-stage spatio-temporal clustering of tuberculosis incidence in West Java
Understanding the complex spatio-temporal dynamics of tuberculosis (TB) is critical for effective control in high-burden regions. This study introduces a two-stage analytical framework applied to annual TB data (2016–2024) in West Java. The first stage identifies temporal trends using hierarchical agglomerative clustering (HAC), while the second evaluates spatial structures using join count statistics and bivariate local Moran’s index (LISA). Results reveal four distinct typologies differing in shape rather than magnitude, capturing a divergence between pandemic-sensitive volatile regions and rigid stable regions. While global spatial autocorrelation indicates randomness ( ), bivariate LISA uncovers significant micro-spatial structures, including “High–High” hotspots in Tasikmalaya ( ) and Cianjur ( ). These findings highlight a duality, global independence alongside local interdependence, advocating for spatially explicit interventions that prioritize resources based on temporal resilience rather than uniform policies.
Copyright (c) 2026 Sarah Fadhlia, Eko Primadi Hendri, Ayu Septiani (Author) https://creativecommons.org/licenses/by-sa/4.0
Spatial ecological determinants of infectious diseases using National Health Insurance data: A multivariate canonical correlation analysis
Uncertain climate change and population growth, which increase every year, constitute a varied lifestyle that can escalate the incidence of some infectious diseases. In West Java, the incident of dengue hemorrhagic fever (DHF), malaria, and pneumonia, along with the number of claims under National Health Insurance (BPJS) for these diseases, increased in 2022. This research aims to explore the relationship between ecological factors and the incidence of infectious diseases using multivariate canonical correlation analysis. The response variables are the incidence of DHF, malaria, and pneumonia based on disease-related visits to healthcare facilities captured in the BPJS data sample. The ecological factors used as explanatory variables include population size, average humidity, average rainfall, average temperature, the amount of waste transported to landfills per ton, and the percentage of households with access to adequate sanitation. The results showed a high correlation between ecology and disease incidence. Based on canonical loading and cross loading, the ecological factors that significantly contribute to disease incidence are population, average rainfall, average temperature, and the amount of waste transported to landfills per ton. Statistically, reduction in waste sent to landfills can decrease the incidence of DHF, malaria, and pneumonia. Reduction in population can reduce disease incidence, and decrease in temperature can lower disease incidence. Meanwhile, increase in rainfall can also reduce disease incidence. Therefore, efforts to control population growth, improve access to proper sanitation, and implement effective waste management can have a positive impact on reducing the incidence of infectious diseases in West Java. Therefore, efforts to control the population, improve access to proper sanitation, and effective waste management can have a positive impact on reducing infectious disease incidence in West Java.
Copyright (c) 2025 Rafli Akbar Ramadhan, Purnomo Husnul Khotimah, Rifani Bhakti Natari, Dianadewi Riswantini , Devi Munandar, Muh. Hafizh Izzaturrahim (Author) https://creativecommons.org/licenses/by-sa/4.0
Multiscale geographically weighted regression with LASSO and Group LASSO: Review and application to micro and small enterprises revenue
Micro and Small Enterprises (MSE) holds a crucial role in the economy because it contributes 55% of the state’s income, but MSE still has a lot of deficiencies, so immediate optimization is vital. The purpose of this study is to model and map the MSE income at the regency level in West Java using Multiscale Geographically Weighted Regression (MGWR) with a selection variable process. MGWR is a method that is used to capture a spatial heterogeneity process by allowing effects to vary over space using “borrowed” nearby data that is controlled by various bandwidths for each variable. This research also adds variable selection processes such as LASSO and Group LASSO as an improvement of MGWR to model group-structured data. The response of this study is MSE income in 27 regencies/cities in West Java province, Indonesia, with 144 independent variables that will be selected using LASSO and Group LASSO to become predictor variables in MGWR model. The results of the spatial modelling show that the best model is MGWR with Group LASSO using bi-square kernel function. Based on this result, it can be seen that a group of important variables which significantly affect the MSE income are fertility, energy source, natural disaster, industry, and tourism. Fertility and energy source significantly affect the MSE income in all regencies, but fertility itself has no significant effect in big cities. Then, in the industry and tourism, the number of visiting foreign tourists has the most significant effect.
Copyright (c) 2025 Juli Yandi Rahman, Farell Fillyanno Zevic, Wawan Hendriawan Nur, Irsyad Ramli (Author) https://creativecommons.org/licenses/by-sa/4.0
Spatio-temporal modeling of built-up land expansion (2005–2055) in the eruption prone area of Gamalama volcano, Ternate island, Indonesia
Ternate Island, a volcanic island dominated by the still-active Gamalama Volcano in North Maluku Province, Indonesia, faces complex dynamics in built-up area development driven by accelerated demographic growth and economic activities, particularly in the trade and tourism sectors. The limited availability of flat land has resulted in increasingly intensive expansion of settlements and infrastructure on the volcanic slopes, areas inherently characterized by high volcanic hazard vulnerability. This study employs a Cellular Automata-Markov Chain modeling approach based on multi-temporal satellite imagery and driving variables, including elevation, slope, distance to road networks, and economic center locations, to analyze land cover change dynamics during the periods 2005, 2015, and 2025 and to model projections for 2035, 2045, and 2055. The results demonstrate significant expansion of built-up land from 2005 to 2055. The rate and spatial distribution of built-up land development within disaster-prone zones Prone Zones I and II underscore the urgency of implementing risk-based spatial planning policies, including stringent restrictions on development in hazardous areas, the development of adequate evacuation infrastructure, and the enhancement of community preparedness capacity. The contributions of this research are highly relevant to sustainable regional planning in tropical volcanic areas with high disaster risk, while also emphasizing the importance of integrating disaster mitigation strategies and climate change adaptation into the spatial planning policies of Ternate Island.
Copyright (c) 2025 Heinrich Rakuasa, Vadim V Khromykh (Author) https://creativecommons.org/licenses/by-sa/4.0
Mapping spatial drivers of rice productivity: A case study of Inpari 36 and 37 in West Java with BYM2-INLA
West Java is one of Indonesia’s largest rice-producing provinces. However, rice production has declined by 23.07% since 2018 due to land conversion. Therefore, this study investigates the factors influencing rice productivity by modeling and mapping the productivity of Inpari 36 and Inpari 37 rice varieties using the Besag York Mollié 2 (BYM2) spatial model with Integrated Nested Laplace Approximation (INLA) inference. The response variable was rice productivity in 27 districts/cities in West Java, with explanatory variables including plant-disrupting organisms, pest-resistant organisms, altitude, average temperature, number of village unit cooperatives, number of tillers, and plant height. The results indicated significant spatial patterns, with the number of tillers and plant height positively affecting both varieties. Additionally, the number of village unit cooperatives had a significant effect on Inpari 36. These findings provide spatial-based recommendations for improving rice productivity and food security policies in West Java.
Copyright (c) 2025 I Putu Aditya Brama Putra Cakra Negara, Abdul Fakhar Ramadhani, Ahmad Abdurrazzaq (Author) https://creativecommons.org/licenses/by-sa/4.0/
Unveiling regional drivers of the blue economy in Sumatra: A multiscale space–time regression analysis with variable selection
Indonesia, as a maritime country, positions the blue economy as a cornerstone in the 2025–2045 National Long-Term Development Plan (RPJPN) to support sustainable economic growth, particularly through the development of the fisheries sector and marine tourism. This study aims to construct a Blue Economy Index (BEI) at the district/city level and identify its key determinants using the Regression Weighted Geographic and Temporal Multiscale (MGTWR) method. The BEI is based on environmental, social, and economic dimensions using data from 2020 to 2022 across 154 districts/cities on Sumatra Island. The analysis incorporates 14 predictor variables grouped into four dimensions and selected through Group LASSO and Elastic Net methods. The best-performing model is the RTGTM with Group LASSO variable selection and a Gaussian kernel function, yielding an of 30.71% and an AIC of 1377.52. The social dimension contributes most to the BEI, while Medan City and Subulussalam City consistently record the highest and lowest scores. Significant variables influencing BEI include population factors (number of sub-districts, total population, sex ratio), education factors (number of senior high schools and vocational schools), and environmental indicators (protected drinking water sources and sanitation access). These findings underscore the importance of multi-dimensional, spatial, and temporal approaches in evaluating and advancing blue economy policies at the regional level.
Multiscale Geographically and Temporally Weighted Regression
Copyright (c) 2025 Muhammad Yusuf Al Habsy, Muhamad Syazali (Author) https://creativecommons.org/licenses/by-sa/4.0/
Determining flood vulnerable parameters: A spatial overlay analysis in Kurau subdistrict, South Kalimantan province
Flood has become one of the most common and damaging natural disasters in Indonesia, especially in coastal areas like Kurau Subdistrict, Tanah Laut Regency. In these regions, the combination of geographical features, land use patterns and climatic influences increases the vulnerability of communities that makes it more challenging to implement effective disaster management. This research focuses on evaluating the spatial distribution of flood vulnerable by determine various environmental and human-induced factors uses based on study an overlay method to parameter of flood vulnerability such as land use, buffer river, elevation, rainfall, slope and soil type. The results of the study shows that a flood hazard map was created as considerable part of the area is categorized as having very high and high flood vulnerability, mainly due to factors like widespread agricultural land use, tendency to river channels and low elevation, which emphasizes the need to integrate flood vulnerability evaluations.
Copyright (c) 2025 Ahmad Reyhan Subardin, Rosalina Kumalawati (Author) https://creativecommons.org/licenses/by-sa/4.0/
Rice growth dynamics: NDVI modeling with sentinel-2 and environmental influences
Accurate monitoring of rice growth phase and phenology is crucial for food security in Indonesia, where rice is a staple crop. This study utilizes Sentinel-2 imagery and remote sensing techniques to efficiently assess rice growth dynamics over a large agricultural area at PT. Sang Hyang Seri, Subang, West Java. The Normalized Difference Vegetation Index (NDVI) was derived from Sentinel-2 data and correlated with plant age to determine rice growth stages. Furthermore, the influence of rainfall, soil type, and slope on NDVI values was analyzed to quantify the impact of these physical factors on rice development. Results indicate that the temporal trend of NDVI during the rice growth cycle can be effectively modeled using a second-order parabolic curve. While the overall rice growth duration was approximately 110 days, land units delineated based on physical factors (OATRAL, DFTRL, DFTTL) exhibited variations in NDVI values, suggesting differential plant fertility. Correlation analysis revealed that rainfall, soil type, and slope significantly affect plant fertility, though not the overall growth duration. Nursery duration, however, was found to influence the age of rice planting. These findings demonstrate the utility of Sentinel-2 NDVI for high-resolution monitoring of rice phenology and highlight the importance of considering environmental factors for optimizing rice production in the region.
Copyright (c) 2025 Nur Afifah, Anisa Rarasati, Ike Sari Astuti, Rudi Hartono, Bagus Setiabudi Wiwoho (Author) https://creativecommons.org/licenses/by-sa/4.0/
Assessment of soil erosion in the Chambal River Basin, India using RUSLE model – implications for agricultural sustainability
Soil erosion is the most concerning issue for agricultural sustainability by decreasing the soil quality. The soil erosion within the Chambal River Basin was assessed using the Revised Universal Soil Loss Equation (RUSLE), satellite-derived datasets such as CHIRPS for precipitation (R factor) and MODIS for vegetation cover (C factor). The study spans from 2001 to 2023, highlighting how changes of MODIS derived LULC in permanent wetlands, agriculture land, urban, vegetation, barren land, and water bodies which influences soil erosion. Results show that croplands consistently dominated over 97% of the study area, increasing from 1,25,613 hectares in 2001 to 1,44,774 hectares in 2023. The spatial variability in erosion, with approximately 50% of the area experiencing slight erosion (<10 t/ha/year) and 30% under moderate erosion (10-20 t/ha/year). Severe erosion (>40 t/ha/year) affects 5% of the basin, particularly in steep slope regions. From 2001 to 2023, mean annual soil loss decreased from 9.66 t/ha/yr to 8.98 t/ha/yr, suggesting minor improvements in land management practices. Rainfall erosivity increased from 351.76 mm in 2001 to 388.09 mm in 2023, correlating with intensified rainfall events due to climate variability. The study highlights the importance of integrating remote sensing and RUSLE for understanding erosion dynamics and promoting sustainable land management in the Chambal Basin.
Copyright (c) 2025 Venkadesh Samykannu (Author) https://creativecommons.org/licenses/by-sa/4.0/
Landslide modeling using Weight of Evidence-Information Value (WoE-IV) for disaster risk mitigation in the Amprong Sub-Watershed, East Java Province, Indonesia
Landslide disasters predominantly occur in the upstream sections of river basins due to the steeper slopes typically found in these areas compared to the middle and downstream sections of watersheds. This study aims to compile existing landslide event data to serve as training and validation datasets for mapping and modeling purposes. Specifically, it focuses on analyzing 16 landslide-controlling factors in the Amprong Sub-watershed and developing landslide susceptibility maps using the Weight of Evidence - Information Value (WoE-IV) method. The findings reveal that 46% of the Amprong Sub-watershed area is classified as having a high level of landslide susceptibility, 39% as having a moderate level, and 15% as having a low level of susceptibility. The landslide susceptibility mapping using the WoE-IV method achieved an AUC value of 0.887, indicating that this method provides a reliable and accurate prediction of landslide occurrences in the study area.
Copyright (c) 2025 Miftakhul Firdauz, Heni Masruroh, Listyo Yudha Irawan, Syamsul Bachri (Author) https://creativecommons.org/licenses/by-sa/4.0/
Climate impact on blue economy index: Bayesian spatio-temporal regression with statistical downscaling in Sumatera
Climate change studies inherently involve spatial, temporal, and regional dimensions, while the blue economy’s sustainability is significantly influenced by global climate factors. Sumatera, a key conservation region in Indonesia, faces challenges in managing its marine resources. Limited quantitative research at regency/city level led to the development of Blue Economy Index (BEI) as a measurement tool. This study constructs a BEI from 38 environmental, economic, and social indicators also identifies global climate variables affecting BEI significantly among five analyzed using best spatio-temporal statistical downscaling model with INLA approach. From 2019 to 2022, Medan City consistently achieved the highest BEI scores, while Subulussalam City recorded the lowest. The optimal model through WAIC, a nonparametric model with unstructured space-time interaction, revealed that skin temperature, sea level pressure, and precipitation significantly affect BEI. Unstructured spatial variation mainly influences BEI, while temporal trends show a decline in 2021 due to Covid-19, with dynamic spatial-temporal effects.
Copyright (c) 2025 Athalia Christina, Ro'fah Rachmawati, Novi Hidayat Pusponegoro (Author) https://creativecommons.org/licenses/by-sa/4.0/
Spatial analysis-based risk identification for malaria elimination efforts in Papua
Papua is a region grappling with various health issues, particularly malaria. A study revealed that a staggering 92% of national malaria cases are concentrated in Papua. This poses a significant challenge given the Sustainable Development Goals (SDGs) target of malaria elimination by 2030. A risk analysis was conducted to spatially identify the distribution of malaria. This approach employed Geographic Information Systems (GIS) and Remote Sensing, utilizing the Model Builder and weighted overlay methods. The analysis indicated that 91.77% of the region is at a moderate risk, while 0.33% is at a high risk. Targeted interventions are imperative for areas classified as moderate to high risk. Comprehensive prevention strategies must address core challenges such as early detection, case management, treatment, and improved access to healthcare facilities.
Copyright (c) 2025 Muhammad Iqbal Maulana, Ida Fransiska Pandiangan, Ridho Saputra (Author) https://creativecommons.org/licenses/by-sa/4.0
Assessing water quality: a study of physical and chemical parameters for PDAM customers in Handil Bakti Village
Water sourced from the Regional Drinking Water Company (PDAM) has now been widely used and spread to all water customers from household area. The large number of requests and the distribution process for clean water needs will affect the quality of the water either in physical or chemical parameters. The research aims to determine the quality of water and responses from PDAM customers, which is based on the type of research in qualitative methods and surveys using questionnaires to respondents. The results indicated that the quality of water received by PDAM customers shows variations in compliance with the drinking water quality standards set by Indonesian Minister of Health Regulation No. 492/Menkes/Per/IV/2010. Responses to the research questionnaire revealed that perceptions of water quality—such as clarity, odor, taste, crustiness, water flow volume, and daily water needs—tend to be consistent among respondents.
Copyright (c) 2025 Ahmad Reyhan Subardin (Author) https://creativecommons.org/licenses/by-sa/4.0/
Geographic information system-based mapping of flood-prone areas and evacuation routes in Cilandak Sub-district, South Jakarta City
Disaster is a series of events that have a direct impact in the form of threats to people's lives caused by either natural or non-natural factors so that the direct impacts caused are environmental damage, property losses, psychological impacts and loss of life. The research method used in this study is a qualitative method. Data processing is carried out using Geographic Information System software, namely QGIS version 3.16. With the development of mapping technology, especially in GIS, flood area mapping can be done to determine the final evacuation. The purpose of this study was to determine the level of flood vulnerability based on Geographic Information Systems (GIS) in Cilandak District, South Jakarta City and to determine the direction of temporary evacuation sites for flood disasters in Cilandak District, South Jakarta City. The location used was Kelapa Gading District, North Jakarta City. In this study, the data used were infrastructure data (buildings and roads) and population data. Building data in Kelapa Gading District has 77 buildings with a total affected area of 11,2045 ha. Meanwhile, the roads have a length of 65,385 km. Population data used for analysis is displayed in pixels and divided into five classes from a total population of 27,006 people. From the data on the distribution of buildings and buildings, it is then processed to create an Evacuation Route Map using the network analysis method.
Copyright (c) 2025 Dwi Jati Marta, Nurul Indri Astuti, Ketut Sutarga (Author) https://creativecommons.org/licenses/by-sa/4.0