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Muhammad Athoillah
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kampus II Universitas PGRI Adi Buana Surabaya Jl. Dukuh Menanggal XII, Surabaya 60234 Jawa Timur, Indonesia.
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J Statistika : Jurnal Ilmiah Teori Dan Aplikasi Statistika
Published by Universitas PGRI Adi Buana Surabaya
ISSN : 20890028     EISSN : 26547511     DOI : https://doi.org/10.36456/jstat.vol16.no2
Core Subject : Economy, Science,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance
This journal publishes scientific articles in the form of research results, case studies, or literature reviews on various aspects related to the field of statistics, scientific data and their applications. Such as Computing, Time Series, Multivariate, Data Mining, Biostatistics, Survival Analysis, Econometrics, Spatial Analysis, Actuarial, Quality Control, Bayesian Analysis, Development Research in Statistics, Natural Language Processing, Applied Mathematics, Applied Statistics. However, the editorial team does not rule out other topics in the fields of statistics and scientific data.
Arjuna Subject : -
Articles 278 Documents
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Exploring Association of Household Conditions and Community Behavior in Flood Events in Banjarbaru Using Apriori Method
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10474

Abstract

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.
Logistic Regression for Sentiment Analysis of Insecurity Phenomena on Platform X
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10545

Abstract

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.
Application of ARIMAX-LSTM Model in Forecasting the Price of Broiler Chicken in Central Java
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10555

Abstract

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.
Classifying Disadvantaged Districts/Cities in Indonesia: A Support Vector Machine Approach
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10569

Abstract

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.
Rejecting Reduction: Clarifying the Concept of Deep Learning in Mathematics Teaching in the Era of Artificial Intelligence
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10570

Abstract

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.
Rehabilitation and Law Enforcement as Optimal Controls in a Mathematical Model of Social Behavior
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10579

Abstract

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.
Analysis of the Influence of Life Expectancy and Per Capita Food Expenditure on the Human Development Index in Central Java, 2023
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 2 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no2.a10663

Abstract

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.
Forecasting Taxpayer Registration Using ARIMA Models: A Case Study of KPP Pratama Meulaboh
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 2 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no2.a10769

Abstract

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.
Implementation of Spatial Autoregressive Analysis to Determine Factors Affecting the Population Dependency Ratio in West Sumatera
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 2 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no2.a10786

Abstract

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.
Identifying Workforce Size Determinants in Rebana and Arumanis Using Least Absolute Shrinkage and Selection Operator (LASSO) Regression
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 2 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no2.a10790

Abstract

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.

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