Jurnal Aplikasi Statistika & Komputasi Statistik
ONLINE ISSN: 2615-1367 PRINT ISSN: 2086-4132 Jurnal Aplikasi Statistika & Komputasi Statistik (JASKS) is an official publication of Politeknik Statistika STIS. JASKS is dedicated to publishing original research in applied statistics and computational statistics. This journal was first published in 2009. The publication schedule is two times a year, in June and December. 2016, Based on the LIPI No.747 / Akred / P2MI-LIPI / 04/2016, ASKS Journal was accredited by LIPI. 2018, JASKS was accredited Sinta 2 by Kementerian Riset dan Teknologi/ Badan Riset dan Inovasi Nasion. (Link SK) 2023, JASKS will process re-accreditation Sinta 2024, JASKS was accredited Sinta 4 by Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi (Ministry of Education, Culture, Research and Technology). (Link SK)
Dimension Reduction of Socioeconomic Factors in Deforestation Analysis in Indonesia Using Sparse PCA
Introduction/Main Objectives: Deforestation remains a major environmental challenge in Indonesia under diverse socio-economic conditions. This study applies Sparse Principal Component Analysis (SPCA) to identify the key socio-economic variables associated with deforestation patterns. Background Problems: Analyses of deforestation drivers often involve numerous correlated variables, leading to multicollinearity and making interpretation difficult. Therefore, an approach is needed to reduce data dimensionality while retaining the most relevant information. Novelty: This study employs SPCA to simultaneously perform dimensionality reduction and variable selection, producing a more interpretable framework for identifying socio-economic factors related to deforestation at the provincial level in Indonesia. Research Methods: Provincial-level socio-economic data from Statistics Indonesia were analyzed using SPCA to address multicollinearity and derive interpretable components. Spatial autocorrelation was assessed using Moran’s I. Finding/Results: SPCA reduced the variables into two interpretable components and identified six key contributing variables while excluding three with limited influence. Moran’s I values for the first (0.402) and second (0.258) sparse principal components indicated significant positive spatial clustering of provinces with similar deforestation-related characteristics. Research Limitations: The analysis is limited to provincial-level secondary data and may not fully capture local-scale variations or all determinants of deforestation.
Copyright (c) 2026 Jurnal Aplikasi Statistika & Komputasi Statistik
Analysis of Factors Influencing Waste Generation in East Java
Introduction/Main Objectives: Waste accumulation poses a serious threat to environmental sustainability and hinders the achievement of Sustainable Development Goal (SDG) 12 regarding responsible consumption and production patterns. Background Problems: East Java consistently ranks second highest in waste generation among Indonesian provinces; this paper investigates the demographic, economic, and environmental determinants of waste generation, specifically addressing the research question of how these factors vary across regencies. Novelty: This study extends previous waste generation studies by applying Geographically Weighted Regression (GWR) to the East Java context, initially considering demographic, economic, and environmental variables, and identifying spatial variations in the significant determinants of waste generation. Research Methods: Secondary data from 35 regencies/cities in 2023 were analyzed using GWR with a Bisquare Fixed kernel, which was selected as the optimal weighting function compared to Fixed kernels and OLS. Finding/Results: Surabaya City recorded the highest waste generation, while the GWR model achieved a goodness-of-fit of 92.72%, higher than the multiple linear regression model. The results confirm that the influence of waste generation determinants is not uniform across regions, indicating significant spatial heterogeneity in East Java.
Classification of Village Development Status in Bekasi Regency Using Ensemble Learning and SMOTE-Based Class Balancing
*]:pointer-events-auto [content-visibility:auto] supports-[content-visibility:auto]:[contain-intrinsic-size:auto_100lvh] scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id="request-WEB:14641d17-07ce-4147-8f7a-52556b4975b8-35" data-testid="conversation-turn-14" data-scroll-anchor="true" data-turn="assistant"> Introduction/Main Objectives: This study aims to classify village development status in Bekasi Regency using machine learning based on the 2024 Village Potential Statistics (PODES) and the Village Development Index (IDM). Background Problems: Conventional descriptive assessments ignore complex socio-economic relationships, and class imbalance further reduces model predictive performance. Novelty: This study integrates PODES data, ensemble learning, and SMOTE to improve classification, providing a reliable, data-driven framework for village profiling and planning. Research Methods: Following preprocessing and a 70:30 split, SMOTE was applied to the training data, and four tree-based models (Decision Tree, Bagging, Random Forest, XGBoost) were evaluated using standard classification metrics. Finding/Results: The Random Forest model combined with SMOTE achieved the best classification performance, with an accuracy of 0.7778 and consistently high AUC values across all classes. The most influential predictors were the dominant economic sector, number of farmer groups, availability of basic health services, and presence of micro-business units. These findings demonstrate that combining ensemble learning with SMOTE improves village development classification and provides valuable support for evidence-based rural development planning in Bekasi Regency.
Mapping and Modeling Crime Factors in North Sumatra Using GWGPR
Introduction/Main Objectives: Crime remains a significant social issue influenced by socio-economic factors and exhibiting spatial variation, particularly in North Sumatra Province, which recorded the highest number of criminal cases in Indonesia in 2024. This study aims to identify significant factors affecting crime and examine the spatial variation of their effects across districts/cities in North Sumatra. Background Problems: Global regression models often fail to capture crime patterns due to overdispersion and spatial heterogeneity, leading to inconsistent relationships across regions. Novelty: This study employs Geographically Weighted Generalized Poisson Regression (GWGPR), which simultaneously addresses overdispersion and spatial heterogeneity, providing a more robust localized analysis than global models. Research Methods: Using secondary data from 33 districts/cities in North Sumatra, the variables include population density, open unemployment rate, mean years of schooling, and Gini ratio. The analysis involves Poisson regression,dispersion testing,Generalized Poisson Regression, spatial heterogeneity testing, and GWGPR. Finding/Results: The significant factors affecting crime are the open unemployment rate, mean years of schooling, and population density, while the Gini ratio is not significant. Limitation: This study is limited by the use of data covering only the year 2024 and a limited set of socio-economic variables, which may not fully capture all factors associated with crime.
Ensemble Boosting Models for Forecasting Rice Prices in Indonesia
Introduction/Main Objectives: Rice is a key staple commodity influencing food security and inflation in Indonesia, making accurate price forecasting essential. In this study, we aim to compare ensemble boosting models and identify the best-performing model for rice price prediction. Background Problems: Notably, rice prices exhibit non-linear patterns over time, while classical statistical methods have limitations in capturing such complexities, resulting in suboptimal forecasting performance. Novelty: This study proposes a lag-based approach that uses lag variables as the only predictors, arranged across multiple input schemes to flexibly capture historical patterns without external variables. Research Methods: Daily national medium rice price data (Jan 2021–Jan 2026) from the National Food Agency are modeled using Gradient Boosting Machine (GBM) and LightGBM, with hyperparameter tuning via Optuna. The forecasting framework relies exclusively on significant lag variables without incorporating exogenous factors. Model performance is evaluated using RMSE, MAE, and MAPE. Findings/Results: LightGBM with optimized hyperparameters achieves the best performance (RMSE = 66.389; MAE = 50.213; MAPE = 0.362%). Furthermore, forecasts for the next 89 days indicate stable prices around Rp13,360–Rp13,395/kg, with no significant fluctuations.
Examining the Local Effects of Food Security Index Components Across Kalimantan Using Geographically Weighted Regression
Introduction/Main Objectives: Food security remains a critical concern across Kalimantan Island, where substantial spatial disparities exist among its 56 regencies and cities, making conventional global regression models inadequate for capturing localized differences. Background Problems: This study addresses the limitation of Multiple Linear Regression in accounting for spatial heterogeneity in the relationships between Food Security Index components and the overall index, raising the question of which components exhibit spatially varying local effects across locations. Novelty: This study presents the first spatially explicit analysis of food security determinants at the regency and city level across Kalimantan, employing Haversine distance combined with adaptive Gaussian kernel weighting within GWR a combination not previously applied in this context. Research Methods: GWR was applied to cross-sectional 2024 data from the Food Security and Vulnerability Atlas, incorporating Cross Validation bandwidth selection and Weighted Least Squares parameter estimation. Finding/Results: The GWR model outperformed MLR with an R² of 59.63% and MSE of 38.5241. The ratio of population per health worker and average years of schooling for women were the most spatially dominant components, significant in 45 and 43 locations respectively, supporting the need for location-specific policy interventions across Kalimantan.
Integrating Multi-Criteria Decision Analysis and Machine Learning for Fine-Scale Mapping of Safe Drinking Water Access in Bengkulu Province, Indonesia
Introduction/Main Objectives: This study aims to develop a 1 km × 1 km level estimation model of safe drinking water access using multisource satellite imagery, point of interest (POI), and aquifer productivity maps. Background Problems: There is a lack of alternative data sources for estimating safe drinking water access that are cost-, time-, and labor-efficient while maintaining high accuracy and frequent updates. Novelty: This study integrates Multi-Criteria Decision Analysis (MCDA) and machine learning methods to estimate and map safe drinking water access at a 1 km × 1 km resolution. Research Methods: Multisource geospatial data were used to construct the model. Within the MCDA approach, the Weighted Product Model (WPM) was employed to develop the Safe Drinking Water Access Index (SDWAI). Meanwhile, the machine learning regression algorithms Adaptive Boosting Regression (ABR) and Gradient Boosting Regression (GBR) were applied to estimate safe drinking water access at a fine spatial scale. The study was conducted in Bengkulu Province, Indonesia. Finding/Results: WPM yielded the best MCDA performance ( = 0.3699, RMSE = 10.6566, MAE = 9.5427, MAPE = 0.1405), while ABR showed the best machine learning performance ( = 0.4361, RMSE = 10.0813, MAE = 8.3750, MAPE = 0.1333).
Copyright (c) 2025 Jurnal Aplikasi Statistika & Komputasi Statistik
Analyzing Medium and Long Text Indonesian Tourism Feedback Using Topic Modeling and Sentiment Analysis
Introduction/Main Objectives: Tourism is a vital sector supporting Indonesia’s economic growth, making the effective utilization of public feedback essential for improving service quality. Most feedback is collected through web-based forms in the form of open-text responses that provide rich insights but remain underutilized due to their unstructured nature. Background Problems: This study examines the challenge of identifying the most suitable topic modeling and sentiment analysis techniques for analyzing medium- and long-text feedback in the Indonesian tourism context. Novelty: The novelty lies in the comparative evaluation of classical topic modeling algorithms against modern embedding-based approaches combined with multiple Indonesian transformer models, which has not been extensively explored in tourism-related datasets. Research Methods: The research compares LDA and NMF with BERTopic, Top2Vec, kBERT, and kUSE using coherence scores, and evaluates sentiment analysis using majority voting across transformer architectures. Finding/Results: The results show that BERTopic performed best for medium-length text, while NMF was optimal for long text, and a RoBERTa-based model achieved the highest sentiment agreement. Positive sentiment often appeared in feedback on facilities and fees, whereas negative sentiment dominated topics on environmental and governance issues. These findings offer valuable insights for tourism managers and policymakers in prioritizing improvements and refining strategies.
Implementation of Twofold HB Beta SAE Model to Estimate Out-of-School Children with Disabilities in Indonesia
Introduction/Main Objectives: The high percentage of out-of-school children with disabilities in Indonesia reveals a significant gap in educational participation. Background Problems: Due to the absence of disability-focused surveys, accurate data are only available at the national level, which is insufficient to represent regional conditions. Novelty: With the increasing demand for small area data, this study estimates the percentage of out-of-school children with disabilities at the provincial and district levels simultaneously, using small area estimation (SAE). Research Methods: This study applies SAE using a twofold subarea-level model with a Hierarchical Bayes (HB) beta approach, covering all 34 provinces and 514 districts/cities in Indonesia. This model was developed using data from the National Socio-Economic Survey (Susenas) and the Village Potential Statistics (Podes). Finding/Results: The twofold HB beta SAE model achieves higher precision than direct estimation, as shown by lower relative standard errors (RSE) across regions. Furthermore, spatial patterns indicate that the percentage of out-of-school children with disabilities is mostly between 35.36% and 45.34%, with clusters concentrated in Kalimantan and Papua.
Satellite Imagery for Classification Analysis of Abrasion Areas on Panaitan, Banten
Introduction/Main Objectives: Abrasion causes severe environmental degradation and socio- economic losses, Waton and Karang Gundul Islands have already subsided due to erosion, posing risks to Panaitan Island, a national park that also faces deforestation, infrastructure development, and vegetation loss which may intensify abrasion. Background Problems: Limited spatial data on coastal abrasion in Panaitan Island hampers effective monitoring and management, highlighting the need for spatially explicit analysis. Novelty: This study identified and classified abrasion-prone areas on Panaitan Island (a rarely exposed island) with rarely variables which have impactful indices such as MVI, TCI, and LSWI. Research Methods: Landsat 8 and Sentinel-2 imagery from 2018 and 2023 were analyzed to assess changes in vegetation, mangroves, surface temperature, and soil moisture. Random Forest, Support Vector Machine, and Logistic Regression were employed to classify abrasion-prone areas. Finding/Results: The analysis revealed signs of abrasion covering 2.04 km², with Random Forest achieving the highest accuracy (82.23%) and NDVI as the most influential variable; abrasion was mainly associated with declining forest and mangrove cover, soil moisture showed weak correlation, while moderate surface temperature had a positive effect. Preventive measures such as reforestation and mangrove rehabilitation are recommended to mitigate risks and ensure long-term environmental sustainability.
Spatial Heterogeneity of Food Security in Indonesia: Unpacking the Roles of Technology and Democracy Index
Introduction/Main Objectives: Food security is a key concern for all countries, especially Indonesia. Technological development and democratic quality are vital for sustainable food security. This study aims to determine the impact of technology and democracy on food security. Background Problems: The relationship between food security and these two factors remains uncertain. Moreover, the extant literature on the spatial impacts on food security yields results that are inconclusive. Novelty: This study offers a comprehensive depiction of the impact of spatial relationships between variables, with a particular focus on the quality of democracy and technology, on the multidimensionality of food security. Research Methods: A spatial lag model is applied to ascertain the impact of technological and democratic on multidimensional food security using data from 34 provinces in 2022. Finding/Results: The results reveal significant spatial dependence in Indonesia’s food security. Technological development and democratic quality positively and significantly affect food security, while urbanization and food crop land expansion show negative and positive effects, respectively. Spatial spillover accounts for approximately 37%–38% of the total impact of each explanatory variable. These findings suggest that technology adoption, democratic strengthening, and interprovincial collaboration are crucial for improving food security.
Estimating Economic Activity Using Geospatial Big Data in East Java, Indonesia: Relative Spatial GDP Index Approach
Introduction/Main Objectives: GRDP serves as a fundamental indicator for assessing regional economic performance in Indonesia and plays a critical role in development planning. Background Problems: Conventional GRDP measurement in Indonesia relies on survey-based approaches, which are time-consuming, costly, and provide limited spatial detail. Novelty: This study introduces a Relative Spatial GDP Index (RSGI) constructed from geospatial big data such as remote sensing and point of interest (POI) to estimate GRDP more granular in East Java. This approach represents the first geospatial data driven GRDP index developed at such fine spatial resolution in Indonesia. Research Methods: Four weighting schemes were applied to generate RSGI variations, which were then evaluated through regression modeling against official GRDP. They are equal weight, pearson correlation, spearman correlation, and principal component analysis (PCA). Finding/Results: The RSGI PCA produced the best performance (RMSE = 0.73047; MAE = 0.48185; MAPE = 7.00%; R² = 0.7618). PCA weight outperformed other weight by capturing shared variance and generating objective weights that better represent spatial economic intensity. The RSGI PCA demonstrates a strong and significant correlation with GRDP at the sub-district level and provides a robust tool for fine-scale economic estimation.
Forecasting Farmer Exchange Rate (FER) in Southeast Sulawesi Province Using Cheng’s Fuzzy Time Series Method
Introduction/Main Objectives: This study aims to forecast the Farmer Exchange Rate (FER) in Southeast Sulawesi Province for 2024 as a basis for short-term economic assessment and policy-related analysis. Background Problems: FER is a key indicator of farmers’ purchasing power and agricultural welfare; however, its monthly dynamics are characterized by fluctuations and uncertainty, making conventional forecasting methods less effective in capturing its behavior. Novelty: This study contributes by implementing the the Fuzzy Time Series (FTS) Cheng approach for FER forecasting in Southeast Sulawesi, emphasizing its suitability for handling vagueness and nonlinear patterns inherent in agricultural economic indicators. Research Methods: The analysis utilizes monthly secondary FER data obtained from BPS-Statistics of Southeast Sulawesi Province, covering the period from January 2014 to December 2023. Forecast accuracy is evaluated using the Mean Absolute Percentage Error (MAPE). Finding/Results: The forecasting results indicate that the FER values for January, February, and March 2024 are each estimated at 105.93. The model achieved a MAPE of 0.3027%, corresponding to an accuracy level of 99.6973%, which places the forecasting performance in the “excellent” category.
Financial Literacy’s Impact on Interest in Sharia Investment: An Examination Using SEM-PLS
Introduction/Main Objectives: This research explores the impact of financial literacy, including knowledge, confidence, and ability, on interest in Sharia investment. Background Problems: the relationship between financial literacy and interest in sharia investment. Novelty: Considering that each province has different community characteristics, especially in the Southwest Papua Province area which is included in the 3T areas (underdeveloped, frontier, and outermost), an analysis was carried out and using SEM-PLS for analysis of Ddta. Research Methods: Using the Structural Equation Model Partial Least Square (SEM-PLS) method with the response variable sharia investment interest and the financial literacy variable with the dimensions of knowledge, confidence and ability. Finding/Results: The results show that financial literacy has a positive and statistically significant effect on interest in Sharia investment. Although its explanatory power is limited (R² = 0.094), the findings indicate that financial literacy functions as an enabling factor rather than a sole determinant of Sharia investment interest. These results suggest that improving financial literacy alone is insufficient to substantially increase interest in Sharia investment. Therefore, policies aimed at promoting Sharia investment should integrate financial education with institutional trust-building, product accessibility, and socio-religious engagement, particularly in underdeveloped and frontier regions such as Southwest Papua.
Estimation of Gross Regional Domestic Product per Capita at the Sub-District Level in Bali, NTB, and NTT Provinces Using Machine Learning Approaches and Geospatial Data
Introduction/Main Objectives: This study aims to estimate Gross Regional Domestic Product (GRDP) per capita at the sub-district level. Background Problems: Currently, GRDP per capita is calculated only at the district level by BPS. Novelty: This study estimates GRDP per capita at the sub-district level using a model developed at the district level, applying machine learning and linear regression methods. Research Methods: The model was constructed using geospatial data sourced from satellite imagery, OpenStreetMap, (Village Potential Statistics) PODES, directories of large mining companies, and directories of the manufacturing industry at the district level. Linear regression and machine learning methods, including neural networks, random forest regression, and support vector regression, were used to develop the model. The research focuses on three provinces: Bali, West Nusa Tenggara (NTB), and East Nusa Tenggara (NTT). Findings/Results: The best-performing model was support vector regression, with MAE and MAPE evaluations of 10.33 million and 26.11%, respectively. The results indicate that sub-districts with high GRDP per capita are typically urban areas that serve as economic hubs. The Williamson Index results show that districts in the eastern region have higher inequality levels compared to those in the western region.
The Impact of ICT on Regional Economy in Indonesia Through MSEs as Mediators: Application of Causal Mediation Analysis in Instrumental-variable Regressions
Introduction/Main Objectives: The development of information and communication technology (ICT) and small and micro enterprises (SMEs) can encourage regional economic growth. Background Problems: Studies on the impact of ICT on the rural economy at the village level are very limited. Furthermore, the Indonesian study neglects to tackle the endogeneity issue associated with this variable and the indirect effects of ICT on the regional economy. Novelty: Using SMEs as a mediator, this study examines the impact of ICT (internet signal strength) on the village's local economy (nighttime light), both directly and indirectly. ICT is considered to be endogenous. Research Methods: This study employs causal mediation analysis in instrumental-variable (IV) regressions at the village level in 2018 and 2021, using lightning strike intensity as IV. Finding/Results: Internet signal strength can increase the number of SMEs, and this increase can positively and significantly impact the local economy. In addition, the direct impact of internet signal strength on the local economy is significantly negative. However, the total impact of internet signal strength is significantly positive.
Copyright (c) 2024 Jurnal Aplikasi Statistika & Komputasi Statistik
Early Study of LLM Implementation in Survey Interviews
Introduction/Main Objectives: This research aims to conduct a preliminary study into the use of LLMs for extracting information to fill out questionnaires in survey interviews. Background Problems: BPS-Statistics Indonesia used paper-based questionnaires for interviews and is recently utilizing the Computer Assisted Personal Interviewing (CAPI) method. However, the CAPI method has some drawbacks. Enumerators must input data into the device, which can be burdensome and prone to errors. Novelty: This study uses a large language model (LLM) to extract information from survey interviews. Research Methods: This study utilizes a text-to-speech application to translate interview results into text. Translation accuracy is measured by the Word Error Rate (WER). Then the text was extracted using the ChatGPT 3.5 Turbo model. GPT-3.5 Turbo is part of the GPT family of algorithms developed by OpenAI. Finding/Results: The extraction results are formatted into a JSON file, which is intended to be used for automatic filling into the database and then evaluated using precision, recall, and F1-score. Based on research conducted by utilizing the Speech Recognition API by Google and the ChatGPT 3.5 Turbo model, an average WER of 10% was obtained in speech recognition and an average accuracy of 76.16% in automatic data extraction.
Comparison of Binary and Traditional Partial Least Squares Structural Equation Modeling: A Study on The Role of Multidimensional Poverty Dimension to Social Protection in Java Island
Introduction/Main Objectives: The traditional Partial Least Squares Structural Equation Modeling (PLS-SEM) method uses an ordinary least squares regression approach that assumes that indicators must have a continuous scale. When the indicators are categorical, the use of traditional PLS-SEM becomes less appropriate. Background Problems: Multidimensional poverty consists of dimensions that are measured by a binary scale. The use of binary PLS-SEM is better than traditional PLS-SEM in modeling the effect of dimensions on social protection on Java Island. Novelty: The use of binary PLS-SEM with factor scores from the item response theory model applied to the role of dimensions of multidimensional poverty to social protection has not been carried out yet. Research Methods: This study introduces binary PLS-SEM, which is modified from traditional PLS-SEM by changing the data input using a tetrachoric correlation matrix. Finding/Results: Empirical results show that the binary PLS-SEM measurement model is better than traditional PLS-SEM. Evaluation of the structural model shows that the path coefficients of binary PLS-SEM are better than traditional PLS-SEM. Both approaches have an overall model fit. The order of multidimensional poverty dimensions that affect social protection are education, living standard, and health.
Development of a Hybrid Fuzzy Geographically Weighted K-Prototype Clustering and Genetic Algorithm for Enhanced Spatial Analysis: Application to Rural Development Mapping
Introduction/Main Objectives: Clustering methods are crucial for geodemographic analysis (GDA) as they enable a more accurate and distinct characterization of a region. This process facilitates the creation of socio-economic policies and contributes to the overall advancement of the region. Background Problems: The fuzzy geographically weighted clustering (FGWC) method, which is a GDA technique, primarily handles numerical data and is prone to being stuck in local optima. Novelty: This study proposed two novel clustering methodologies: fuzzy geographically weighted k-prototypes (FKP-GW) and its hybrid clustering model, which combines genetic algorithm-based optimization (GA-FKP-GW). Research Methods: This research conduct simulation study comparing two of the proposed clustering method. For the empirical application, this study applied clustering technique using the official Village Potential Survey of Temanggung, Indonesia. Finding/Results: The evaluation results of experiments conducted on simulated data and study cases indicate that the proposed method yields distinct clustering results compared to the previous method while being comparably efficient. The empirical application identifies four distinct groups from the clustered villages, each displaying unique characteristics. The results of our research have the potential to benefit the development of the GDA method and assist the local government in formulating more effective development policies.
The Application of Partial Proportional Odds Model on Determinants Analysis of Household Food Insecurity Level in Papua, Indonesia
Introduction/Main Objectives: Food insecurity in Papua, Indonesia, is still high. However, the study on that issue is limited. This research aims to analyze the determinants of food insecurity in Papua. Background Problems: An ordinal logistic regression can be used. However, this model generally requires the parallel lines assumption. However, somehow, the assumption is often violated. Novelty: This study used a model that relaxes the assumption of parallel lines. This model can capture the condition that some parameters are assumed to meet parallel lines and some do not. Research Methods: In this case, the partial proportional odds model was applied to find the determinant of household food insecurity status by using the National Socioeconomic Survey (SUSENAS) data. Finding/Results: The results show that a female head of household, age 60 years and above, junior high school education and below, has a higher tendency to be at least mildly food insecure, and the effect is the same for each level of food insecurity. Household heads who do not work, work in agriculture, and have household drinking water sources that are not feasible can aggravate the food insecurity level. Meanwhile, food assistance provided by the government influences reducing food insecurity levels.
Adding MSNBURR-IIa Distribution to MultiBUGS
Introduction/Main Objectives: The MSNBurr-IIa distribution is a neo-normal distribution designed to fit right-skewed data better. This article aims to integrate the MSNBurr-IIa distribution into MultiBUGS, thereby enabling Bayesian estimation of its parameters. Background Problems: Markov Chain Monte Carlo (MCMC) is a popular method for Bayesian computations, although its implementation is frequently challenging. MultiBUGS, a statistical tool that uses the BUGS language, is used to make this easier. Novelty: This paper details integrating the MSNBurr-IIa distribution into MultiBUGS, allowing for estimating its parameters. The module's effectiveness is demonstrated through its application on both simulated data and regional economic growth data of Indonesian districts/cities in 2021. Research Methods: The MSNBurr-IIa module was developed using five steps: requirement, design, development, testing, and implementation in simulation and real-world data. It was built with Blackbox Component Builder, an integrated development environment (IDE) for the Component Pascal programming language. Finding/Results: The findings confirm that MultiBUGS, with the MSNBurr-IIa module, successfully estimates the distribution’s parameters across various datasets.
The Implementation of Geospatial Analysis on Hotel Occupancy Rate
Introduction/Main Objectives: One of the main attributes of hotel selection and customer satisfaction is its location. Background Problems: Strategic location leads to higher demand for accommodation. Accommodation demand is reflected in hotel occupancy levels, which indicate the percentage of reserved rooms at a specific period. Novelty: This study aims to investigate the effect of spatial location on hotel occupancy rates by analyzing data collected in online hotel reservation applications. A study related to the effects of location and hotel occupancy has never been conducted in Indonesia. Research Methods: We use data from hotels located in the province of Yogyakarta, which contains 245 hotels spread over three regencies/cities, namely Yogyakarta City, Sleman Regency, and Bantul Regency. We conducted a spatial regression analysis, namely the Spatial Error Model (SEM), with a spatial weight matrix using a radius of 3.2 km. Finding/Results: We found that spatial locations affect the occupancy rates of hotels based on the online hotel reservation application that we observed. These spatial locations include the distance from the hotel to the airport, the distance from the hotel to the bus stop, and the number of nearby restaurants, offices, and hotels.
Implementation of a RESTful API-Based Evolutionary Algorithm in a Microservices Architecture for Course Timetabling
Introduction/Main Objectives: Implement an evolutionary algorithm within a RESTful API for a course timetabling system that employs a microservices architecture. Background Problems: The current course timetabling at Politeknik Statistika STIS uses the third-party application (aSc Timetables), which lacks a generator as a service, resulting in its inefficiency due to the lack of integration with SIPADU NG. Novelty: The evolutionary algorithm is built as a service (RESTful API) within a microservices architecture and supports custom constraints for timetables. Research Methods: One of the evolutionary algorithm families, the (1+1) evolutionary strategy, is implemented and used to create a course timetable 1000 times. Each course timetable created will have its cost calculated to assess the goodness of the algorithm implementation. The developed RESTful API is also evaluated through black box testing. Finding/Results: For the odd semester data, 40.5% of the trials yielded a cost value between 4 and 5, while for the even semester, all trials produced a cost value below 1. The resulting cost value is close to 0, which indicates that the timetable created has minimal violations. Additionally, black box testing concluded that the service operates as expected, delivering the anticipated output.
Spatial Dependencies in Environmental Quality: Identifying Key Determinants
Introduction/Main Objectives: Environmental quality is essential to human development because it reflects the condition of our natural surroundings. Background Problems: Understanding the determinants of environmental quality is crucial for Indonesia as it helps identify the key factors influencing environmental quality. Novelty: This study seeks to identify the determinants of environmental quality in regencies and municipalities on Java Island, incorporating spatial effects into the analysis. Research Methods: The dependent variable is environmental quality index. The independent variables are GRDP in industrial sector, GRDP in agricultural sector, urban population rate, population density, and poverty rate. We applied spatially lag regression model using contiguity spatial weight matrix. Finding/Results: This study shows the spatially lag regression model outperforms the OLS model. GRDP in the industrial sector, GRDP in the agricultural sector, urban population rate, and population density have negative effects, suggesting the increases in these variables were associated with lower environmental quality.
Quantile Regression with Constrained B-Splines for Modelling Average Years of Schooling and Household Expenditure
Introduction/Main Objectives: Education serves as a driving force for the transformation of society to break the cycle of poverty. This study examines the relationship between average years of schooling and per capita household expenditure in Kalimantan Tengah Province in 2020. Background Problems: The method of estimating a regression model that is assumed to follow a certain form of regression equation such as linear, quadratic and others is called parametric regression. However, researchers often encounter difficulties in determining the model specification through data distribution, so the method used is nonparametric regression. Novelty: This research uses a quantile-based approach to explore how the impact of education on per capita expenditure varies across different levels of household education. This provides a more nuanced understanding of the relationship, showing not just whether education matters, but how its influence changes at different levels of educational attainment. Research Methods: The relationship between average years of schooling and per capita household expenditure is modeled using a quantile regression model with the constrained B-Splines method. Finding/Results: Based on the established classification, it can be concluded that an increase in the average years of schooling among household members tends to have a greater impact on raising per capita expenditure.
Aspect-Based Sentiment Analysis of Transportation Electrification Opinions on YouTube Comment Data
Introduction/Main Objectives: This research aims to conduct an aspect-based sentiment analysis of transportation electrification opinions on YouTube comment data. Background Problems: It is difficult to summarize the sentiment of many YouTube user comments related to electric vehicles (EVs) based on their aspects; therefore, aspect-based sentiment analysis is needed to conduct further analysis. Novelty: This study identifies five aspects of EV and their sentiments at the same time. The aspects are usefulness, ease of use, comfort, cost, and incentive policies. One of this study’s methods is the transfer learning model. This model can be a solution to overcome the shortcomings of deep learning in classifying aspect-based sentiment classification on small datasets. Research Methods: The sentiment classification model used is a machine learning model, namely support vector machine (SVM) and transfer learning models from pre-trained IndoBERT and mBERT. Finding/Results: Based on the experimental results, transfer learning from the IndoBERT model achieved the best performance with accuracy and F1-Score of 89.17% and 52.66%, respectively. Furthermore, the best IndoBERT model was developed with input in the form of a combination of aspects and comment sentences. Experimental results show that there is an improvement in performance with accuracy and F1-Score of 90% and 60.70%, respectively.
Small Area Estimation Approaches Using Satellite Imageries Auxiliary Data for Estimating Per Capita Expenditure in West Java, Indonesia
Introduction/Main Objectives: The economy of a country can determine the welfare of its people. One of the economic indicators in Indonesia is per capita expenditure, which has the lowest estimation at the district level. Background Problems: Sub-district level estimates provide detailed information on inequality that cannot be explained at the district level. Unfortunately, sub-district level estimates of per capita expenditure in Indonesia have poor Relative Standard Error (RSE) values. Research Method: The Small Area Estimation (SAE) method can improve estimator accuracy on small samples by using auxiliary variable information. Novelty: The existence of big geospatial data such as remote sensing provides an advantage in the efficient use of auxiliary variables. Finding Result: The Empirical Best Linear Unbiased Prediction (EBLUP) model using Nighttime Light Intensity (NTL) as an auxiliary variable provides the best results of the five proposed models. Remote sensing data can potentially be used in SAE auxiliary variables.
Patterns, Determinants, and Elasticity of Household Food Consumption in Indonesia (Period 2021-2022)
Introduction/Main Objectives: The increase in strategic food commodity prices significantly contributed to inflation, with the food, beverage, and tobacco category reaching 3.59% in March 2022. This price hike reduced household purchasing power, affecting welfare. This research examines how rising food prices impact household food consumption patterns in Indonesia. Background Problems: This study explores the effects of rising food prices on household consumption patterns. It addresses two key questions: What are household food consumption patterns, and what factors influence them? What is the elasticity of food demand in Indonesia between March 2021 and March 2022? Novelty: The study’s novelty lies in calculating food demand elasticity using demand theory assumptions, ensuring reliable results—unlike many studies that overlook these assumptions. Research Methods: The study uses the Linear Approximated-Almost Ideal Demand System (LA-AIDS) model with the Seemingly Unrelated Regression (SUR) method. Findings/Results: The results show that rising food prices in March 2022 changed household food consumption patterns. Own-price elasticity was negative, indicating reduced demand. Cross-price elasticity varied, with some food groups showing negative and others positive effects. All food groups were classified as normal goods based on expenditure elasticity.
Clustering Regencies/Cities Vulnerable to Air Pollution in the Java Island: Fuzzy Geographically Weighted Clustering
Introduction/Main Objectives: Air pollution has become a critical global concern with substantial effects on human health and the environment. Background Problems: Java Island in Indonesia, recognized for its high population density and industrial activities, necessitates focused effort in resolving this issue. Novelty: While air pollution research has been enormous, there has been no effort to cluster regencies or cities on Java Island utilizing spatially-based data. This research seeks to cluster regencies and cities on Java Island according to air pollution levels and to compare geodemographic and non-geodemographic clustering methodologies. Research Methods: This study employs secondary data regarding air pollution, obtained from the Openweather API. This study employs a geodemographic clustering technique, namely fuzzy geographically weighted clustering (FGWC), optimized by the artificial bee colony (ABC) algorithm. Finding/Results: The study findings indicate that the geodemographic clustering method ABCFGWC surpasses Fuzzy C-Means (FCM) according to the TSS (Tang-Sun-Sun) index. The data reveal that the Greater Jakarta or Jabodetabek area and its adjacent territories are more susceptible to air pollution. The findings of this study are expected to enhance the spatial planning and mapping of air pollution management strategies on Java Island.
Pemodelan Angka Prevalensi Stunting di Indonesia Menggunakan Regresi Nonparametrik Spline Truncated Multiprediktor
Stunting merupakan gangguan pertumbuhan dan perkembangan yang dialami anak akibat gizi buruk, infeksi berulang, dan stimulasi psikososial yang tidak memadai.. Berdasarkan data Survei Status Gizi Nasional (SSGI) tahun 2022, prevalensi stunting di Indonesia adalah 21,6% dimana angka tersebut masih diatas angka standar menurut WHO yaitu 20%. Penelitian ini dilakukan dengan tujuan untuk menganalisis faktor-faktor yang mempengaruhi angka prevalensi stunting di Indonesia menggunakan regresi nonparametrik spline truncated multiprediktor. Data penelitian merupakan data sekunder yang diambil dari Statistik Kesehatan 2022 dengan variabel respon berupa angka prevalensi stunting.