J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika
Jurnal Ilmiah Teori dan Aplikasi Statistika
Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya
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.
Copyright (c) 2025 J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika
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.
An Analysis of Vertical Mismatch of College Graduates in Central Java in 2025 from a Gender Perspective Using a Multinomial Logistic Regression
Vertical mismatch is a condition in which an individual's level of education does not match the qualifications of the job being undertaken. Although various studies have examined the factors affecting vertical mismatch, empirical evidence regarding gender differences in college graduates at the provincial level is still limited, especially in Central Java Province. This research offers novelty by examining gender differences in match status, overeducation, and undereducation at the provincial level using multinomial logistic regression and Average Marginal Effects (AME). This research aims to analyze the effect of gender on the vertical mismatch of college graduates in Central Java Province in 2025. The data used were Sakernas microdata in 2025. The sample was 4,100 college graduates who worked in Central Java Province. The data analysis was carried out using multinomial logistic regression and AME. The research results showed that overeducation was the most dominant form of vertical maladjustment. Gender had a significant effect on the probability of an individual being in each category of vertical nonconformity. The AME results showed that women had a lower probability of experiencing overeducation of 16.11%, a higher probability of being in a match condition of 14.43%, and a higher probability of experiencing undereducation of 1.68% compared to men. These findings can provide input for universities and policymakers in strengthening the link between higher education and labor market needs by considering gender differences.
Copyright (c) 2026 J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika
Hybrid ICEEMDAN–SVD–LSTM Model for Short-Term Stock Closing Price Forecasting: A Multi-Sector Evaluation on the Indonesia Stock Exchange
This study develops and evaluates a hybrid ICEEMDAN–SVD–LSTM model for forecasting the daily closing prices of BBRI, UNVR, and UNTR over an observation period of 15 years (September 2010–September 2025). The novelty lies in applying ICEEMDAN–SVD–LSTM to stock forecasting on the still underexplored Indonesia Stock Exchange, and in systematically comparing decomposition- and denoising-based models within one uniform framework across three sectors with different volatility profiles. ICEEMDAN was chosen because it yields a cleaner decomposition, with fewer spurious modes and less residual noise than EMD, EEMD, and CEEMDAN, while Hankel-matrix-based SVD denoising increases the signal-to-noise ratio of each component before the LSTM forecasting stage. The model's performance was compared with seven benchmarks: LSTM, EMD–LSTM, EEMD–LSTM, CEEMDAN–LSTM, ICEEMDAN–LSTM, EEMD–SVD–LSTM, and CEEMDAN–SVD–LSTM. In terms of RMSE, MAE, and MAPE, ICEEMDAN–SVD–LSTM produced the lowest errors for all three stocks, with RMSE values of 16.4605 (BBRI), 24.3086 (UNVR), and 70.8494 (UNTR), and lower MAE and MAPE than every benchmark. A Diebold–Mariano test confirmed that this advantage is statistically significant against all benchmark models. These results indicate that the proposed model is effective for short-term stock price forecasting and can serve as a decision-support tool for investors and analysts, while acknowledging market risk.
Spatial Regression Analysis of Leprosy in East Java 2025 Using Spatial Autoregressive and Spatial Error Models with Queen Contiguity
Leprosy remains a public health issue in Indonesia, including in East Java, which has a relatively high number of cases. Differences in social, economic, and health characteristics between regions can cause variations in leprosy cases and spatial clustering. Unlike previous studies, which generally used absolute case numbers and applied one spatial regression model, this study compares spatial autoregressive (SAR) and spatial error (SEM) models using the new case detection rate (NCDR) as the response variable. The study uses Queen Contiguity weighting to analyze the factors that influence the NCDR of leprosy in East Java in 2025. The predictor variables include population density, the percentage of households without toilet facilities, the percentage of impoverished residents, and the number of health centers. Secondary data from 38 districts/cities in East Java were used for the analysis. The analysis revealed significant positive spatial autocorrelation, with a Moran's I value of 0.6059. Of the predictor variables, the percentage of impoverished residents was the only one that significantly affected the NCDR of leprosy. Based on Akaike's information criterion (AIC) and log-likelihood values, the SAR model was selected as the best model. These findings suggest that controlling leprosy requires social and economic interventions, particularly poverty reduction efforts.
Application of Binary Logistic Regression to Identify Determinants of Non-Performing Loans
This study aimed to identify the factors associated with non-performing loans (NPLs) in banks and develop a borrower-level model to estimate the probability of loan default. Unlike previous studies that mainly focus on bank-level financial indicators or macroeconomic factors, this study utilizes borrower characteristics and loan information obtained from credit application data at a commercial bank in Palembang, Indonesia. The study used data from 100 borrowers, with predictor variables including age, number of family dependents, total household income, occupation, educational attainment, loan amount, loan term, and monthly installment amount. Binary logistic regression with backward elimination was applied to identify significant predictors of NPLs and to estimate the probability of loan default. The results showed that occupation, loan amount, and loan term significantly influenced the occurrence of non-performing loans. The final model achieved a classification accuracy of 81% and an area under the receiver operating characteristic curve (AUC) of 0.858, indicating good predictive performance. The obtained binary logistic regression model can be used to estimate the probability of non-performing loans and assist banks in identifying potential credit risks during the credit evaluation process.
Comparative Analysis of Quantile Autoregression and Prophet Models for IDX30 Index Forecasting
Forecasting stock market indices remains challenging due to volatility, structural breaks, and nonlinear dynamics in financial time series. Although Quantile Autoregression (QAR) and Prophet have been widely applied in forecasting studies, comparative evidence on their performance for the IDX30 index in the Indonesian stock market remains limited. This study aims to compare the forecasting accuracy of QAR and Prophet models in predicting the IDX30 index. Monthly closing price data from May 2012 to March 2026 were divided into an in-sample period (May 2012–March 2020) and an out-of-sample testing period (April 2020–March 2026). A one-step-ahead rolling forecasting approach was employed to evaluate model performance. Forecast accuracy was comprehensively assessed using Symmetric Mean Absolute Percentage Error (sMAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrate that the QAR model significantly outperforms Prophet across all evaluation metrics. The QAR model achieved an sMAPE of 3,47%, an RMSE of 21,62, and an MAE of 16,01, which are substantially lower than Prophet’s sMAPE of 8,22%, RMSE of 53,27, and MAE of 38,71. The superior performance of QAR indicates its strength in capturing short-term dependencies and adapting to volatility and structural changes. Practically, QAR offers critical implications for investors and financial analysts; unlike Prophet, which is rigid toward long-term trends, QAR adjusts estimates during extreme market conditions (upper/lower quantiles), leading to more precise and adaptive investment and risk management decisions. These findings confirm that QAR provides a more reliable forecasting framework for the IDX30 index.
Comparison of Simple and Multiple Linear Regression Models for Monthly Rainfall Prediction in Batam Using Temperature andRelative Humidity Predictors
Batam is a strategic coastal region located along an international shipping route, characterized by a tropical climate with high rainfall intensity. These conditions substantially affect key sectors such as transportation and tourism, underscoring the need for accurate climatological predictions. This study develops simple and multiple linear regression models for monthly rainfall, incorporating air temperature and relative humidity as predictor variables. The dataset comprises observations recorded at the BMKG Hang Nadim Meteorological Station for the period 2000–2024. Model performance was evaluated by comparing predicted values with observed data for 2024 using the Root Mean Square Error (RMSE) and the Pearson correlation coefficient ( ). The results show that the multiple linear regression model provides the highest predictive accuracy, with an RMSE of approximately mm and a correlation coefficient of . Among the single-predictor models, relative humidity performed better than temperature, achieving an RMSE of mm ( ) compared with mm ( ). These findings indicate that the integration of temperature and relative humidity predictors is more effective for monthly rainfall prediction, based on the analysis of a 25-year climatological dataset from a tropical maritime region such as Batam. This improvement in rainfall prediction supports more robust climate adaptation and disaster mitigation strategies in Batam.
Implementation of a MATLAB-Based Computational Framework for Multivariate Linear Regression: A Case Study on Economic Growth in Jayapura
Regional economic performance is an important indicator for evaluating development and public welfare. This study implemented and evaluated a MATLAB-based computational framework for parameter estimation in a multiple linear regression model of Gross Regional Domestic Product (GRDP) in Jayapura. The analysis used 15 annual observations covering the period from 2010 to 2024, with infrastructure investment, unemployment rate, and tourist arrivals as explanatory variables. The predictors were normalized using Min–Max scaling, after which the parameters were estimated using the Nelder–Mead algorithm through MATLAB’s fminsearch function and directly compared with the analytical Ordinary Least Squares (OLS) solution. Optimization stability was evaluated using six independent starting values, while model predictive performance was assessed using leave-one-out cross-validation. The results showed that OLS and Nelder–Mead produced practically identical coefficients, fitted values, and performance measures, with a maximum prediction difference of only 4.11×10-5. All Nelder–Mead starting values converged to essentially the same minimum, with a Residual Sum of Squares range of only 8.94×10-8. The model produced an (R^2) of 0.7705 and an adjusted (R^2) of 0.7079. Infrastructure investment had a positive and statistically significant association with GRDP, whereas unemployment and tourist arrivals had negative but statistically insignificant coefficients. The in-sample MAPE was 21.95%, while the cross-validation MAPE was 29.42%, indicating moderate predictive performance. A Durbin–Watson statistic of 0.5644 indicated positive residual autocorrelation; therefore, the inferential results should be interpreted cautiously. These findings demonstrate that Nelder–Mead does not outperform OLS for unconstrained linear regression, but it can accurately and consistently reproduce the OLS solution as a numerical validation within a MATLAB-based computational framework.
Inflation Forecasting Using ARIMA and Its Implications for Online Pricing Strategies in Tanjungpinang City
This study examines the role of inflation forecasting as a supporting input for online pricing strategy formulation in the context of digital businesses in Tanjungpinang. Using monthly inflation data from January 2008 to October 2025 published by the Indonesian Central Bureau of Statistics (BPS), the analysis applies a seasonal ARIMA(0,0,1)(2,0,0)[12] model to generate inflation projections for the period November 2025 to October 2027. Diagnostic evaluations, including residual analysis and goodness-of-fit assessment, confirm that the selected model adequately captures the underlying data structure. The forecasting results indicate that inflation in Tanjungpinang is expected to remain low and relatively stable over the two-year horizon, with moderate and controlled fluctuations ranging from 0.17% to 0.42% per month. Based on these empirical findings, the study then presents a conceptual and literature-based analysis of how the forecast results can inform online pricing strategies, including dynamic pricing, promotional pricing, competitive pricing, cost-based pricing, and algorithmic pricing. It is important to note that the pricing strategy component of this study is interpretive and conceptual in nature, drawing on relevant literature rather than constituting an empirical test of pricing behavior. The analysis highlights that stable inflation conditions allow firms to prioritize demand-driven adjustments, competitive positioning, and margin optimization rather than frequent cost-driven repricing. By integrating regional inflation projections with conceptual pricing considerations in digital markets, this research contributes to the literature on data-driven pricing and offers practical insights for digital enterprises and policymakers.
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.
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.
Analysis of Female Labor Force Participation in Java Island Using Multiscale Geographically Weighted Regression with an Adaptive Bisquare Kernel
Female labor force participation is an important indicator in labor development and gender equality. However, the Female Labor Force Participation Rate (FLFPR) in Java Island still shows disparities across regions, indicating the presence of spatial influences. This study aims to analyze the socio-economic factors affecting the FLFPR in 119 regencies/cities on Java Island in 2024 using the Multiscale Geographically Weighted Regression (MGWR) method with an adaptive bisquare kernel implemented in Python. The data used in this study were obtained from Statistics Indonesia (Badan Pusat Statistik) in 2024. The explanatory variables include the Regency/City Minimum Wage, number of poor population, Gender Inequality Index, Gross Regional Domestic Product, percentage of women in parliament, number of women managing households, and the Gender Development Index. The contribution of this research lies in the specific application of MGWR with an adaptive bisquare kernel for the Java Island region, which allows parameter estimates to vary across locations. The results indicate significant spatial heterogeneity. The minimum wage variable is the most consistently significant factor with a negative effect in most regions. These findings imply the need for region-specific employment and women’s empowerment policies rather than uniform policies across Java Island.
Implementation of Robust Mixed Geographically and Temporally Weighted Regression for Crime Rate Prediction in East Java
The total number of crimes in East Java Province reached 60,102 cases in 2024, indicating spatial and temporal variation in crime patterns. These data characteristics are heterogeneous and contain several extreme values (outliers) that may affect the stability of standard regression model estimates. This study applies the Robust Mixed Geographically and Temporally Weighted Regression (RMGTWR) model to examine spatiotemporal crime patterns in East Java during the 2020–2024 period and to evaluate the model’s ability to handle outliers in heterogeneous data. The evaluation results show that the RMGTWR model produced an of 0.84 and an MSE of 1912.247. This robust approach provides more stable parameter estimates and reduces the influence of the 14 identified outlier observations compared with the non-robust model. The findings indicate that the Human Development Index (HDI) and the Gini Ratio have significant global effects, while the Police Ratio variable shows varying local effects. These results confirm that the RMGTWR approach accommodates data heterogeneity while mitigating distortions caused by outliers in crime analysis in East Java.
Generalized Poisson Regression Modeling Using Fisher-Scoring Optimization for Cases of Malnutrition in Toddlers in Central Java
Malnutrition among infants and toddlers accounts for 45% of global child deaths, making accurate risk modeling essential. In Central Java, data on malnutrition cases show overdispersion (ϕ = 162.58), rendering the Poisson Regression (PR) model invalid. This study applied Generalized Poisson Regression (GPR) with Fisher-Scoring optimization to five predictor variables: complete basic immunization, active community health posts (posyandu), neonatal visits, iron-folic acid tablet (TTD) consumption by pregnant women, and living in poverty. The evaluation results show that GPR is superior with an AIC value of 505.68 and a Pearson Pseudo-R² of 0.1784. Based on the modeling results, iron tablet consumption and the number of poor residents have a significant effect, while active Posyandu does not. Furthermore, the categorical variable for basic immunization and the variable for neonatal visits showed anomalous results and were therefore eliminated to maintain model stability. This study demonstrates that GPR provides more reliable estimates to support targeted health intervention and poverty alleviation policies in Central Java.
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.
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.