J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika
Jurnal Ilmiah Teori dan Aplikasi Statistika
Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya
Implementation of Spatial Autoregressive Analysis to Determine Factors Affecting the Population Dependency Ratio in West Sumatera
The population dependency ratio illustrates the burden borne by the working-age population in supporting non-working-age groups. This study analyzes the spatial patterns of the dependency ratio across districts/cities in West Sumatra Province in 2024 using a Spatial Autoregressive Model (SAR) and simultaneously identifies the factors influencing it. The results show that Total Fertility Rate (TFR) and Median Age at First Marriage (MAFM) significantly increase the dependency ratio, whereas Contraceptive Prevalence Rate (CPR) and the proportion of elderly population are not statistically significant. The positive and significant spatial lag coefficient (ρ = 0.0852) indicates that higher dependency ratios in neighboring regions contribute to an 8.52% increase in a given region’s dependency ratio, confirming the presence of spatial spillover effects. The spatial approach also reveals interregional variation, demonstrating that the SAR model effectively captures both local and neighboring influences, providing a more accurate understanding of demographic dynamics. These findings underscore the need for targeted policies such as fertility control, and reproductive health education considering interregional interactions to manage the population dependency burden more effectively.
Copyright (c) 2026 J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika
Complience Evaluation Based on National Fire Safety Standards and QSPM Strategic Priority Analysis
Fire is a critical disaster with significant impacts on human safety and building functionality. This study aims to assess the preparedness of PT. The research employs a sequential exploratory mixed methods approach, integrated with SWOT analysis and the Quantitative Strategic Planning Matrix (QSPM). Primary data were gathered through observations, interviews, and regulatory-based checklists. Results indicate a grand average compliance index of 85.9%, categorized as "Good". However, SWOT analysis reveals vulnerabilities in human resource competency and passive infrastructure. The QSPM results prioritize Human Resource Capacity Building & Technical Certification (TAS 2.65) as the most strategic intervention to address residual risks in high-risk industrial environments.
This study aims to identify the preparedness of the PT. XYZ to the potential dangers of fire.
Modeling Aggregate Losses for Third Party Liability Insurance Using the Panjer Recursive Method
The increasing number of passenger vehicles in densely populated areas such as Jakarta, West Java, and Banten has increased the risk of financial losses due to accidents, theft, and vehicle damage. This study aims to model the aggregate loss distribution in Third Party Liability (TPL) insurance using the Panjer recursive method. The data used are real observations from PT. XYZ for passenger vehicles insured between IDR 125 million and IDR 200 million in the 2018 underwriting year. Claim frequency is modeled using a Poisson distribution, while claim severity follows a Pareto Type II distribution. Model parameters are estimated using the Maximum Likelihood Estimation (MLE) method, and goodness-of-fit is evaluated using the Chi-square and Kolmogorov–Smirnov tests. The results show that the aggregate loss distribution can be effectively constructed using the Panjer recursive method. A dominant discrete probability mass occurs at zero aggregate loss with a probability of 0.992664, while the continuous component covers positive losses up to IDR 23,400,000. This result indicates that TPL claims in the observed portfolio are extremely sparse, which has important implications for premium pricing and risk management in motor vehicle insurance.
Text Mining for Classifying Potentially Depressive Tweets on X Using IndoBERT
Depression is a severe issue in Indonesia, where sufferers often do not seek professional help and prefer to express themselves through social media such as the X. This study uses a text mining approach to classify potential depression using a dataset of 5,000 Indonesian-language tweets from October 2024 to January 2025. The preprocessing steps involves case folding, cleaning, normalization, and stopword removal. The dataset was labeled into two classes: potentially depressive and normal, then divided into 80% training data and 20% test data. A pre-trained IndoBERT model was adjusted with a learning rate of 2e-05, batch size of 8, and epoch of 2 for this depression potential classification task. The evaluation results showed that the IndoBERT model performed well with an accuracy of 87%, precision of 87%, recall of 87%, and f1 score of 87%. However, the model’s performance affected by class imbalance, so it tended to be better at predicting the majority label (normal) than the minority label (depression). Therefore, rebalancing is recommended to prevent similar occurrences. The IndoBERT model used in this study was initialized from an emotion classification model, manual labelling was conducted by researchers in collaboratoin with psychiatrists to ensure clinical relevance. Finally, the trained model was deployed into a web-based application using Streamlit. This application was created as a preliminary screening tool to assist psychiatrists, not as a diagnostic system.
Univariate vs. Multivariate: Comparing Univariate Panel Data and Panel SUR Approaches in Modeling Stunting, Wasting, and Underweight in Indonesia
Objective: To support Indonesia’s progress toward the SDG Zero Hunger target, this study compares the performance of univariate panel data regression against the Panel Seemingly Unrelated Regression (Panel SUR) model in analyzing stunting, wasting, and underweight (2007–2023). Method: Using a two-way Feasible Generalized Least Squares (FGLS) approach, the study simultaneously estimates the malnutrition system to account for cross-equation error correlations. Results: The results demonstrate that Panel SUR outperforms the univariate model by correcting the theoretical inconsistency of the Low Birth Weight (LBW) coefficient—switching it from a counterintuitive negative to a positive sign while significantly increasing the explanatory power for the wasting equation and achieving superior estimation efficiency as evidenced by a lower Mean Square Error (MSE). Implications: These methodological improvements are substantively critical; correcting the LBW sign ensures that policy interventions accurately prioritize prenatal nutrition as a determinant of acute malnutrition, avoiding misleading inferences common in isolated modeling approaches. Conclusion: Consequently, Panel SUR offers a more robust empirical framework for formulating integrated nutrition policies than traditional univariate methods.
A Dynamic Spatial Durbin Panel Model for Analyzing Poverty Rates in West Java Province
Poverty is a complex socioeconomic issue influenced by various internal and external factors and remains a major concern in Indonesia. Based on data from the Statistics Indonesia (BPS), the national poverty rate in 2025 reached 8.57%, decreasing by 0.1% from the previous year. In West Java Province, although poverty shows a declining trend, it remains the second-highest in terms of the number of poor residents, totaling about 3.67 million people. This study analyzes the factors affecting poverty levels in West Java by considering spatial and temporal interregional effects. The data consist of the poverty percentages of districts and cities in West Java and their related variables from 2019 to 2024. The method used is a dynamic spatial panel regression model with a spatial durbin approach. The results reveal significant spatial and dynamic effects on poverty. GRDP per capita significantly influences poverty within a region, while the unemployment rate, GRDP per capita, and population growth rate in neighboring regions also have significant effects. The model explains 99.59% of the interregional variation in poverty. These findings highlight the importance of regional coordination and sustainable policies for poverty reduction, particularly through job creation, economic equity, and population growth control.
Frequency Data Modeling of Passenger Transport Auto Insurance Claims Using the New Poisson Mixed Weighted Lindley Distribution
Vehicle insurance is an important instrument in risk management. However, claim frequency modeling often faces overdispersion issues, rendering the equidispersion assumption in Poisson distribution invalid. Alternative distributions such as Negative Binomial have been widely used to address this issue, but they still have limitations in capturing claim heterogeneity in some insurance data. This study applies the New Poisson Mixed Weighted Lindley (NPWL) distribution to passenger transport vehicle insurance claim frequency data in Indonesia sourced from PT. XYZ in the 2013 underwriting year. Parameter estimation was performed using the Maximum Likelihood approach, and model fit was evaluated using the Chi-Square test. The results show that the NPWL model provides a good fit to the data, with a Chi-Square test statistic value of 0.7341, which is smaller than the critical value. The parameter estimates obtained were 4.5919 and 0.8539, which resulted in a mean value of 0.3811 and a variance of 0.4033. The variance being greater than the mean indicates that NPWL is able to capture overdispersion more flexibly than the conventional Poisson model, making it more relevant for practical applications in setting vehicle insurance premiums.
Spatial Analysis of Gross Enrollment Ratio in Higher Education Using the Geographically Weighted Regression Approach
This study analyzes the factors influencing the Gross Enrollment Rate of Higher Education (GER-HE) in Indonesia by emphasizing regional differences that cannot be adequately captured by global Ordinary Least Squares (OLS) or panel data models. The analysis is based on secondary data for 2023 obtained from Badan Pusat Statistik (BPS) and the Ministry of Education, including Gross Regional Domestic Product (GRDP), education funding, lecturer–student ratio, number of students, per capita expenditure, total population, and the poverty depth index. To capture spatial heterogeneity in these relationships, the study applies Geographically Weighted Regression (GWR) with a Gaussian kernel weighting function. The results indicate that the GWR model explains 50.57% of the variation in GER-HE, reflecting improved model performance after accounting for spatial variation across regions and providing a better fit than the OLS regression. The effects of explanatory variables on GER-HE vary across provinces, allowing regions to be classified into five groups based on combinations of statistically significant factors, particularly the number of students, per capita expenditure, and the poverty depth index. These findings suggest that higher education policies should be tailored to the specific characteristics of each regional group to enhance GER-HE and reduce interprovincial disparities.
Rejecting Reduction: Clarifying the Concept of Deep Learning in Mathematics Teaching in the Era of Artificial Intelligence
This article aims to clarify and clarify the dual interpretations of the term' deep learning' in the context of mathematics education in the era of artificial intelligence. The term is often reduced to merely an AI-based technology that relies on the internet. In contrast, in the pedagogical domain, deep learning refers to a learning approach that emphasizes deep conceptual understanding, connections between ideas, and the transfer of knowledge to new situations. This study adopts a conceptual review approach based on literature analysis, using secondary sources such as journal articles, books, and policy reports published between 2000 and 2024. The findings show that deep learning technology holds potential to support mathematics learning through features such as handwriting recognition, automated evaluation systems, intelligent tutoring, and adaptive learning. However, the implementation of this technology also faces serious challenges, including limitations in contextual data availability, uneven digital infrastructure, the opaque nature of model interpretation, and issues of ethics and data privacy. On the other hand, the pedagogical approach to deep learning places the teacher as the main actor in designing meaningful learning experiences. Therefore, the integration of technology and pedagogy must be carried out critically and contextually. Educational innovations in the AI era must remain grounded in humanistic principles and an awareness of students' sociocultural realities—especially in the diverse context of Indonesia.
Copyright (c) 2025 J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika
Association of Poverty Categories, Educational Characteristics, and Area of Residence in Indonesia Using a Three-Way Log-Linear Model
Contingency tables are one way to present data with all categorical variables. The analysis used to model the contingency table is a log-linear model. The log-linear model is also used to estimate parameters and see the association between variables. This research aims to utilize the three-way log-linear model to model and see the association between poverty category variables, educational characteristics (level of education and reading and writing ability) of the head of the household, and area of ​​residence in Indonesia in 2023. Research is done by forming a saturated and homogeneous log-linear model first, then comparing the difference in deviance values ​​from the two models with the table chi-square value or choosing the smallest AIC value to determine the best model. The results obtained are a significant saturated model. This means that there is an association between the poverty category variable, the education level of the head of the household, and the area of ​​residence. There is also an association between the poverty category variable, the reading and writing ability of the head of the household, and the area of ​​residence. In addition, there is a greater tendency for poverty for heads of households who have a primary school education or less and cannot read and write.
Copyright (c) 2024 J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika
Analisis Regresi Logistik Biner Multilevel pada Status Kemiskinan di Pulau Jawa menggunakan Algoritma MCMC Metropolis-Hasting
Pulau Jawa adalah pulau paling padat penduduk di Indonesia. Namun, ada beberapa provinsi di Pulau Jawa yang mengalami masalah kemiskinan. Provinsi Jawa Tengah memiliki tingkat kemiskinan sebesar 11,32% pada tahun 2018, lebih tinggi dari persentase kemiskinan pulau Jawa, yang merupakan akumulasi dari kemiskinan di semua kabupaten dan kota di provinsi tersebut. Model regresi logistik multilevel mempunyai struktur data hirarki yang terdiri dari satu variabel prediktor yang diukur pada level paling bawah (level 1) dan satu variabel penjelas yang diukur pada setiap level atau level selanjutnya. Struktur hirarki data kemiskinan mengindikasikan bahwa data tersebut berasal dari beberapa level, dimana level yang lebih rendah yaitu Kabupaten/Kota tersarang pada level yang lebih tinggi yaitu Provinsi. Data persentase kemiskinan daerah diubah ke dalam bentuk biner menjadi variabel status kemiskinan sehingga metode pendugaan parameter dilakukan dengan pendekatan model regresi logistik biner hirarki dengan algoritma Metropolis-Hasting. Pemodelan ini membantu pemerintah dalam mengambil kebijakan terhadap kelompok kabupaten/kota kategori miskin berdasarkan nilai Indeks Pembangunan Manusia (IPM). Analisa pemodelan menunjukkan bahwa variabel IPM memberikan dampak yang sama saja bagi kecenderungan status kemiskinan kabupaten/kota dengan asumsi parameter lain konstan. Variabel interaksi antara IPM dan dana program Bantuan Pangan Non Tunai memberikan dampak kecenderungan kabupaten/kota di Pulau Jawa berstatus tidak miskin sebesar 1,07 kali daripada miskin.
Copyright (c) 2023 J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika
Multiperiod Logit on Survival Analysis of Financial Distress in Manufacturing Company
The company is required to be able to maintain its survival so that the company's goals can be achieved properly. Financial distress is one of the factors that causes the company to be unable to maintain its viability so that the company's goals are not achieved. The factors that cause the company to be in a state of distress are internal and external factors. This is descriptive quantitative research which used 16 financial ratios, IHSG and BI rate. This research method is a quantitative method using time series data with a multiperiod logit model. Determination of the sample using purposive sampling so that there are 79 samples used in this study. Based on the results of the description of the Kaplan Meier curve, log rank test, multiperiod logit model with variable selection, it means that companies survive and financial distress have prominent differences in profitability ratios and market measure ratios. Meanwhile, based on the results of the partial test 4 out of 5 financial ratios, the results of the selection of variables have a significant effect on financial distress. The five best companies to invest in with a minimum hazard opportunity value are companies with issuer codes SKBM, IGAR, PBRX, PSDN and UNIC.
Application of Agglomerative Hierarchical Clustering Method for Grouping Non-Cash Food Assistance Recipients in Ngambon Bojonegoro
One of the sub-districts in Bojonegoro that received non-cash food assistance was Ngambon sub-district. The non-cash food assistance provided in Ngambon sub-district has not been on target. This is because underprivileged people do not get assistance, while people who can afford it actually get non-cash food assistance. So, research is needed with the aim that non-cash food assistance provided by the government can be distributed according to procedures. The method used in this study is agglomerative hieralchical clustering to group recipients of non-cash food assistance from the people of Ngambon Bojonegoro. The variables used were 12 indicators of non-cash food assistance set by the Bojonegoro district Social Office. The data used were 131 recipients of non-cash food assistance spread across five villages in Ngambion sub-district. Grouping results with the single linkage method are less relevant. Meanwhile, with the average linkage and complate linkage methods, five clusters were obtained, and with ward linkage, three clusters were obtained. Based on the elbow rule, it was found that ward linkage is the best grouping method, with cluster 1 totaling 57 people, cluster 2 totaling 53 people and cluster 3 totaling 21 people.
Pemodelan Multivariate Adaptive Regression Spline (MARS) Pada Faktor-Faktor yang Mempengaruhi Kemiskinan di Provinsi Maluku dan Maluku Utara
Penanggulangan kemiskinan di Provinsi Maluku dan Maluku Utara masih belum maksimal, hal ini karena masih terjadi peningkatan kemiskinan di daerah tersebut. Penelitian ini bertujuan untuk mengetahui faktor-faktor apa saja yang mempengaruhi kemiskinan di Provinsi Maluku dan Maluku Utara. MARS merupakan salah satu metode klasifikasi yang mampu menangani data ber-dimensi tinggi dengan pola data yang tidak diketahui sebelumnya, serta dapat diterapkan untuk melihat interaksi di antara variabel yang digunakan. Penelitian ini diperoleh hasil bahwa model MARS terbaik dengan kombinasi BF = 39, MI = 3 dan MO = 1 dan GCV sebesar 0.1728. Adapun variabel yang berpengaruh secara signifikan terhadap kemiskinin di Provinsi Maluku dan Maluku Utara yaitu variabel persentase lapangan pekerjaan kepala rumah tangga, persentase rumah tangga yang menggunakan jenis atap, persentase rumah tangga yang tidak menggunakan dinding tembok, persentase fasilitas penerangan yang digunakan, persentase penduduk yang sakit tetapi tidak berobat karena tidak memiliki biaya untuk berobat, persentase pendidikan terakhir kepala keluarga yang tidak/belum pernah sekolah dan persentase rumah tangga yang tidak memiliki fasilitas air minum
MARS, GCV, Kemiskinan, Provinsi Maluku, Provinsi Maluku Utara
Identifying Workforce Size Determinants in Rebana and Arumanis Using Least Absolute Shrinkage and Selection Operator (LASSO) Regression
Development of West Java’s Rebana and Arumanis Areas is intended to strengthen the economy and expand employment opportunities. Identifying influential factors is essential for formulating effective and well-targeted policies to enhance workforce absorption in these two areas. This study aims to analyze and identify the main factors affecting workforce size in the Rebana and Arumanis Areas using the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. To determine the optimal (L1) penalty in LASSO, 5-fold cross-validation (k = 5) was applied, yielding an optimal penalty value of 363.03. The results indicate that the factors ranked from most to least important for workforce absorption in Rebana and Arumanis are: (1) the number of MSMEs, (2) the Human Development Index (HDI), (3) Gross Regional Domestic Product (GRDP), (4) the regional minimum wage, (5) realized Foreign Direct Investment (FDI), (6) road length, and (7) realized Domestic Direct Investment (DDI). Model performance was evaluated using an average (R2) of 83.09%. These findings highlight the importance of strengthening the MSME ecosystem and implementing productivity-oriented regional development planning to promote workforce absorption in Rebana and Arumanis.
Forecasting Taxpayer Registration Using ARIMA Models: A Case Study of KPP Pratama Meulaboh
The number of registered taxpayers (WP) in a given region serves as a critical indicator of both the effectiveness of tax administration and the level of public participation in fulfilling tax obligations. KPP Pratama Meulaboh, as one of the vertical units of the Directorate General of Taxes, has experienced year-to-year fluctuations in taxpayer registration, highlighting the need for a systematic analytical approach to support forward-looking administrative planning. This study aims to forecast the number of registered corporate taxpayers at KPP Pratama Meulaboh using a time series approach based on the Autoregressive Integrated Moving Average (ARIMA) model. The dataset consists of annual observations covering the period 1982–2024. The analysis followed a structured procedure, including variance-stabilizing transformation, stationarity testing using the Augmented Dickey–Fuller (ADF) test, model identification through autocorrelation and partial autocorrelation analysis, and model selection based on the Akaike Information Criterion (AIC). Several ARIMA specifications were evaluated, and ARIMA(1,0,1) was selected as the optimal model, yielding the lowest AIC value (–66.0104) and statistically significant parameters. Diagnostic checks confirmed that the model residuals satisfied the white noise assumption. The selected ARIMA(1,0,1) model was subsequently used to generate ten-year-ahead forecasts, which indicate a steady upward trend in the number of registered corporate taxpayers over the forecast horizon. These results provide practical insights for tax authorities in planning administrative capacity, strengthening compliance monitoring, and supporting strategic tax base expansion within the jurisdiction of KPP Pratama Meulaboh.
Analysis of the Influence of Life Expectancy and Per Capita Food Expenditure on the Human Development Index in Central Java, 2023
This study analyzes the effect of life expectancy and per capita food expenditure in urban areas on the Human Development Index (HDI) in 35 regencies/cities of Central Java Province. A multiple linear regression method was employed to estimate both simultaneous and partial effects, with HDI as the dependent variable and life expectancy and per capita food expenditure as independent variables. The estimation results indicate that both life expectancy and per capita food expenditure significantly affect HDI. Partially, life expectancy has a positive and significant effect on HDI (β = 1.913), while per capita food expenditure has a negative and significant effect (β = -0.384). The negative coefficient suggests that increased food expenditure does not necessarily correspond to higher HDI, which may reflect consumption inefficiency or low nutritional quality of food. The Adjusted R² value of 0.726 indicates that the regression model explains 72.6% of the variation in HDI, with the remainder explained by other factors outside the model. These findings highlight that human development policies in Central Java should focus not only on increasing purchasing power but also on improving health quality and the efficiency of food consumption patterns. The results can serve as a basis for formulating more targeted and sustainable human development policies in Central Java.
Rehabilitation and Law Enforcement as Optimal Controls in a Mathematical Model of Social Behavior
Social behavior is the result of interactions between individuals, which can lead to tendencies toward either positive or deviant behavior. From this perspective, a mathematical model of social behavior is developed, dividing the population into criminal and non-criminal groups. Previous studies generally considered only law enforcement strategies—such as arrest and imprisonment—as responses to deviant behavior, without incorporating the aspect of rehabilitation. This study examines the application of optimal control in a mathematical model of social behavior to minimize the number of individuals in the criminal group, using rehabilitation and law enforcement as control variables. The optimal control problem is solved using Pontryagin’s Minimum Principle, and numerical simulations are performed using the Forward-Backward Sweep Method. The simulation results show that a combination of rehabilitation and law enforcement strategies can significantly reduce the criminal population in the model.
Classifying Disadvantaged Districts/Cities in Indonesia: A Support Vector Machine Approach
The terms "underdeveloped" and "non-underdeveloped" regions highlight the gap between regions in Indonesia. The government determines the status of underdeveloped regions every five years. Presidential Decree No. 63 of 2020 determines 62 districts/cities in Indonesia as underdeveloped regions. This study aims to classify disadvantaged district status using the Support Vector Machine (SVM) algorithm with three kernel types: linear, polynomial, and Radial Basis Function (RBF). SVM was selected for its effectiveness in handling high-dimensional data and non-linear classification tasks. The dataset, sourced from BPS and JDIH in 2022, comprises 20 variables covering socioeconomic, infrastructure, and public service indicators. The data distribution is imbalanced, with only 62 out of 514 districts labeled as disadvantaged. Optimal parameters were determined experimentally: linear (C = 0.1), polynomial (C = 1, d = 3), and RBF (C = 1, γ = 0.1). Based on evaluation results, the linear kernel achieved the best performance on the given dataset, with an accuracy of 0.94, precision of 0.91, recall of 0.81, and F1-score of 0.85. The model classified 45 districts as disadvantaged and 469 as non-disadvantaged. A total of 29 districts showed discrepancies compared to the official classification. These differences may indicate either changing ground conditions or limitations in policy criteria, highlighting the potential of data-driven approaches to support more targeted and equitable regional development planning.
Application of ARIMAX-LSTM Model in Forecasting the Price of Broiler Chicken in Central Java
Central Java's economy grew 4.98% in 2023 with the trade sector as the main driver, including the broiler chicken meat commodity whose production increased from 621,718.06 tons (2021) to 791,997.10 tons (2023). However, the price of this commodity experiences considerable fluctuations, mainly influenced by external factors such as increased demand during the national holiday period and the price of substitute products such as chicken eggs and beef that can affect the purchasing power of broiler chicken meat. Data on chicken meat prices, chicken egg prices, and beef prices were obtained from the official website of PIHPS (Strategic Food Price Information Center), while data for the week before the holiday was obtained using the Python library “holidays”. This research develops a Hybrid ARIMAX-LSTM model to predict chicken meat prices more accurately. The ARIMAX model is used to capture the linear pattern of chicken egg prices by considering external variables (egg prices, beef, and national holidays), while the LSTM captures non-linear residual patterns that cannot be explained by the ARIMAX model. The results show that the Hybrid model produces a MAPE of 1.19%, which is more accurate than the single ARIMAX (MAPE 1.38%). The predicted January 2025 price ranges from IDR 35,300 - IDR 35,900/kg, showing stability without extreme fluctuations. This research provides a predictive solution that can be used by the government and businesses in price control and market stabilization.
Logistic Regression for Sentiment Analysis of Insecurity Phenomena on Platform X
The phenomenon of insecurity as a psychological symptom is increasingly becoming a topic of discussion on social media. This study aims to analyze public sentiment toward the phenomenon of insecurity as expressed through posts on Platform X. The use of sentiment analysis in the context of insecurity is crucial because the phenomenon is subjective and often undetectable in real life. In this context, sentiment analysis is an effective tool for systematically and objectively exploring user sentiment. Data was collected from Indonesian-language tweets in January 2025 using related keywords such as “insecure”,“minder”, and “overthinking.” After undergoing text preprocessing, the data was classified into three sentiment categories: positive, neutral, and negative. Logistic regression was employed as the classification method, with 10-fold cross-validation used to evaluate model performance. The study’s results show a dominance of negative sentiment at 73.34%, with positive and neutral sentiments accounting for 20.38% and 6.28%, respectively. The model’s average accuracy reached 83.13%, with the best performance in detecting negative sentiment. Wordcloud visualizations revealed a dominance of negatively nuanced words such as “takut”,“rendah” and “sendiri.” These findings underscore the importance of deeper understanding of the psychological dynamics of the digital public. This study also paves the way for data-driven interventions to support mental health literacy in online spaces.
Exploring Association of Household Conditions and Community Behavior in Flood Events in Banjarbaru Using Apriori Method
Floods in Banjarbaru have mostly been studied from the perspective of natural and physical causes, such as rainfall and the region's topography. Meanwhile, the association between household conditions and community behavior during floods is rarely explored quantitatively. This research aims to fill that gap by applying the Apriori algorithm to questionnaire data from flood-affected households to find association rules. The study found that disruptions in livelihoods during floods tend to be followed by a decrease in income, while floods lasting more than one day generally trigger the evacuation of family members and prompt the government to provide temporary shelters. These key rules imply that flood mitigation policies should prioritize early warning systems, pre-positioning of shelter facilities, and targeted economic assistance to enhance the resilience of affected households.
Evaluating Patient Satisfaction in Surabaya Public Health Centers Using an Integrated IPA–Kano Framework
Quality healthcare services at community health centers (Puskesmas) are crucial for enhancing public satisfaction and trust, particularly in large cities like Surabaya. This study aims to identify and prioritize service attributes using Importance Performance Analysis (IPA), classify them using the Kano method, and integrate both approaches to formulate a comprehensive service improvement strategy. A survey method with questionnaires was administered to 85 patients at Puskesmas Surabaya, and the data were analyzed using IPA and Kano methods. The IPA results show that four variables fall into the low-priority quadrant: registration speed, the attitude of the staff in providing service, waiting room conditions, and the availability of adequate parking spaces. Meanwhile, the Kano method classified all attributes as One Dimensional (O), meaning that improving the quality of these attributes will proportionally enhance patient satisfaction. Conversely, satisfaction will decrease if the performance of these attributes declines. The integration of IPA and Kano methods highlights the need to improve the speed of registration, staff service attitudes, waiting room conditions, and parking availability. These findings emphasize the importance of focusing on these aspects to improve overall service quality and patient satisfaction.
Implementation of Geographically and Temporally Weighted Regression with Cross Validation and Generalized Cross Validation Methods for Deforestation Modeling in Kalimantan
Deforestation in Indonesia has received national and international attention for its ecological, economic and social impacts. Kalimantan is an area with high deforestation rates triggered by various factors that vary between locations and time. This study aims to model the deforestation rate in Kalimantan during the period 2014 to 2022 using the Geographically and Temporally Weighted Regression (GTWR) method. The model was tested with a combination of Fixed and Adaptive Gaussian Kernel weighting functions and Cross Validation (CV) and Generalized Cross Validation (GCV) bandwidth determination methods. The results show that the best model is GTWR with Fixed Gaussian Kernel and GCV based on R2 and AIC values. Spatial-temporal analysis shows that neither variable is significant in 2014, forest fires are significant in 2019, and in other years both variables are broadly significant. The findings provide insights into the spatial and temporal dynamics of deforestation factors to support area-based policies.
Bonus-Malus Premium for Third Party Liability Insurance with Poisson-Lindley Distribution Claim Frequency and Exponential-Inverse Gamma Distribution Claim Severity
Insurance is a form of mutual cooperation that provides protection against unforeseen risks. With insurance, individuals can feel more secure about potential future losses, whether related to themselves or their property. As the number of motor vehicles in Indonesia increases, so does the risk of traffic accidents, making motor vehicle insurance particularly Third Party Liability (TPL) insurance increasingly important. To enhance fairness, insurance companies implement premium systems based on claim history, one of which is the bonus-malus system. This study discusses premium calculation in a bonus-malus system for TPL insurance, assuming that claim frequency follows a Poisson-Lindley distribution and claim severity follows an exponential-inverse gamma distribution. The data used are secondary data obtained from PT. XYZ for the 2019 underwriting year, focusing on policyholders in category two. The analysis results indicate that the selected distributions fit the data well. The optimal bonus-malus system determines that the initial pure premium to be paid by new policyholders is Rp22,970. Premiums in subsequent years are adjusted based on claim activity: increasing if a claim is made and decreasing if no claim occurs.
Poisson-Lindley and Exponential-Inverse Gamma Distributions
Implementation of K-Means Cluster for Districts or Cities in West Java Province Based on Unemployment Indicators
The high disparity of open unemployment rate among regions in West Java Province is an issue that requires mapping based on regional socioeconomic characteristics. The purpose of this study is to group districts/cities in West Java based on the open unemployment rate and its influencing factors using the K-Means Cluster method. The data used is secondary data for the year 2023 obtained from the Central Bureau of Statistics. Assumption test was conducted using KMO and Bartlett's Test to ensure sample adequacy and feasibility of data structure. The results of the analysis show that the regions in West Java are divided into three clusters. Cluster 1 consists of regions with high development but also high unemployment rates, such as Bandung City and Bekasi Regency. Cluster 2 includes regions with medium socioeconomic conditions, such as Depok City and Bandung Regency. Cluster 3 consists of regions with low development, low HDI, and high poverty, such as Ciamis, Garut, and Pangandaran. These findings indicate the existence of significant inequality among regions and can serve as a basis for the formulation of more targeted and region-based unemployment reduction policies.
Forecasting Stock Prices Using a Nonlinear Approach with the Exponential Smooth Transition Autoregressive (ESTAR) Model
Increased interest among Indonesians in investing in a single financial asset has driven significant growth in the capital market. However, high market volatility brings the risk of loss that needs to be anticipated. One of the relevant models in dealing with these problems is using the nonlinear Exponential Smooth Transition Autoregressive (ESTAR) model. ESTAR is an extension of the Autoregressive (AR) model that uses smoother transitions to handle nonlinear time series data. This study aims to predict stock prices using the ESTAR nonlinear model to help investors deal with market uncertainty and manage short-term risk. The data used is the daily closing price of PT Bank Central Asia Tbk shares, for the period January 2022 to December 2024. The research methodology includes stationarity test, AR(p) parameter estimation, ESTAR(p,d) model parameter estimation, and prediction accuracy evaluation using Mean Absolute Percentage Error (MAPE). The results show that the AR(1) model is the best order model and the ESTAR(1,1) model is the final optimal model. Evaluation of the prediction results for the next one month period, shows that the MAPE value is 2.79% which indicates the model's performance in predicting stock prices is very good.
The Modeling of The Poverty Rate In Indonesia From 2018 to 2023 Using A Panel Data Regression Approach
Indonesia was ranked sixth out of eleven Southeast Asian countries with the highest poverty rate, highlighting the need for effective strategies to address poverty issues. The governmental strategy was aligned with the Sustainable Development Goals (SDGs), with the primary objective of achieving zero poverty. In this study, the poverty rate in Indonesia from 2018-2023 exhibited fluctuating trends, marked by both increases and decreases over the years. This phenomenon reflects the dynamic nature of poverty levels in the country. Poverty rates are assumed to be related to education and the economy. Referring to the statement, this study involves the percentage of poverty as the dependent variable and access to clean water access, gini ratio, open unemployment rate, and literacy rate as independent variables. Based on the structure of the research data, the dynamics of the poverty rate from 2018-2023 involve both cross-sectional and time series data structures. In this case, the panel data regression method based on the Random Effects Model is appropriate and can accommodate the identification process to the conclusion. This study aims to identify the factors that influence the poverty rate. Furthermore, the findings indicate that all independent variables have simultaneous and partial effects on the poverty rate.
Cluster Analysis Using the Ward Algorithm for Grouping Regency / City in Central Java Province Based on Poverty Indicators 2023
Poverty is still a major issue in Central Java Province, with the poverty rate recorded at 10.23% in 2023. Uneven poverty reduction in various regions adds to the complexity of this problem. This study aims to classify districts/cities in Central Java based on poverty indicators, using a hierarchical cluster analysis approach through Ward's algorithm. The data used is secondary data from the Central Bureau of Statistics (BPS), which includes six variables that affect poverty. One them is the percentage poor people (X1), which represents the proportion of the population below the poverty line and is an important indicator in assessing the welfare of a region. The results show that the application of hierarchical cluster analysis through Ward's algorithm produces four clusters of districts/cities based on poverty indicators in 2023, each with different characteristics. The first cluster consists regions with high poverty rates and limitations in education, health, and economic infrastructure. Regions in this cluster require special attention in policy formulation. Based on the characteristics obtained from each cluster, these findings can be used to design more targeted and data-based development policies, such as budget allocation, poverty reduction programs, and improving access to basic services according to the conditions each cluster.
Geographically Weighted Negative Binomial Regression (GWNBR) Modeling In Infant Mortality Rate Cases In South Sulawesi
Geographically Weighted Negative Binomial Regression (GWNBR) is a method used to model count data that exhibit overdispersion and spatial heterogeneity. South Sulawesi is one of the provinces experiencing an increase in infant mortality cases. Therefore, this study aims to obtain a better model for mapping the factors that influence infant mortality cases in South Sulawesi Province. The method used in this study is GWNBR with an Adaptive Tricube Kernel as the weighting function. The results show that the GWNBR model with Adaptive Tricube Kernel weighting produces the smallest AIC value, which is 223.4447, making it more effective for modeling infant mortality cases in South Sulawesi Province. The variables significantly affecting infant mortality cases include X1 (Percentage of Exclusive Breastfeeding), X2 (Percentage of Early Initiation of Breastfeeding), X3 (Complete Baby Visit Coverage), X4 (Percentage of Vitamin A Supplementation), X5 (Number of Community Health Centers), X6 (Percentage of Low Birth Weight Babies), X7 (Delivery Coverage in Health Service Facilities), and X8 (Iron Tablet Supplementation to Pregnant Women).