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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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INDONESIA
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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Inflation Forecasting Using ARIMA and Its Implications for Online Pricing Strategies in Tanjungpinang City
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 19 No 1 (2026): 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.vol19.no1.a11394

Abstract

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 a MATLAB-Based Computational Framework for Multivariate Linear Regression: A Case Study on Economic Growth in Jayapura
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 19 No 1 (2026): 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.vol19.no1.a11451

Abstract

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.
Comparison of Simple and Multiple Linear Regression Models for Monthly Rainfall Prediction in Batam Using Temperature andRelative Humidity Predictors
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 19 No 1 (2026): 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.vol19.no1.a11476

Abstract

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.
Comparative Analysis of Quantile Autoregression and Prophet Models for IDX30 Index Forecasting
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 19 No 1 (2026): 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.vol19.no1.a11507

Abstract

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.
Application of Binary Logistic Regression to Identify Determinants of Non-Performing Loans
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 19 No 1 (2026): 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.vol19.no1.a11533

Abstract

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.
Spatial Regression Analysis of Leprosy in East Java 2025 Using Spatial Autoregressive and Spatial Error Models with Queen Contiguity
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 19 No 1 (2026): 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.vol19.no1.a11581

Abstract

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.
An Analysis of Vertical Mismatch of College Graduates in Central Java in 2025 from a Gender Perspective Using a Multinomial Logistic Regression
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 19 No 1 (2026): 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.vol19.no1.a11635

Abstract

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.
Hybrid ICEEMDAN–SVD–LSTM Model for Short-Term Stock Closing Price Forecasting: A Multi-Sector Evaluation on the Indonesia Stock Exchange
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 19 No 1 (2026): 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.vol19.no1.a11694

Abstract

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.

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