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All Journal Journal of Advanced in Information and Industrial Technology (JAIIT)
Trimono
UPN "Veteran" Jawa Timur, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Bayesian Spatio-Temporal Conditional Autoregressive Modeling of Stunting Risk Factors in East Java Ardia Eva Ardiani; Trimono; Kartika Maulida Hindrayani
Journal of Advances in Information and Industrial Technology Vol. 8 No. 1 (2026): May
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v8i1.762

Abstract

This study analyzes stunting cases in East Java Province using district/city-level panel data covering the period 2022-2024. The data were obtained from the Indonesian Nutritional Status Survey (SSGI), the Indonesian Health Survey (SKI), and official statistical sources, consisting of stunting cases and several health, socioeconomic, and environmental indicators across 38 districts and municipalities. The study applies a Bayesian Spatio-Temporal Conditional Autoregressive (BST-CAR) model with the Integrated Nested Laplace Approximation (INLA) approach to account for spatial dependence among neighboring regions and temporal variation over time. The results show that stunting cases in East Java exhibit significant spatial and temporal dependence, supported by significant positive spatial autocorrelation across all observation years. Model evaluation yields a Deviance Information Criterion (DIC) value of 1477,267 and a Watanabe-Akaike Information Criterion (WAIC) value of 1442,479. The estimation results indicate that all examined covariates, including low birth weight, complete basic immunization, exclusive breastfeeding, proportion of poor population, access to improved drinking water, and access to improved sanitation, are statistically significant in explaining variations in stunting cases after controlling for spatial and temporal effects. Relative risk mapping reveals clear spatial heterogeneity, with higher-risk clusters concentrated in districts such as Jember, Lumajang, and Probolinggo, while lower-risk areas are mainly observed in urban regions such as Surabaya, Mojokerto, and Madiun. Overall, the findings suggest that stunting distribution in East Java is shaped by both spatial and temporal structures, highlighting the importance of geographically targeted intervention strategies at the district/city level.
Stock Price Prediction and Loss Risk Analysis of PT Sawit Sumbermas Sarana Tbk Using a Hybrid TCN-GAN Model Nabilah Selayanti; Trimono; Dwi Arman Prasetya
Journal of Advances in Information and Industrial Technology Vol. 8 No. 1 (2026): May
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v8i1.764

Abstract

The Crude Palm Oil (CPO) industry is a strategic sector for the Indonesian economy. Yet, stock prices of companies in this sector tend to be highly volatile due to global market dynamics and export policies, increasing investment risk. Conventional models, such as ARIMA, rely on linearity assumptions that limit their ability to capture nonlinear dynamics, while deep learning models, such as RNN, GRU, and LSTM, still suffer from vanishing-gradient problems. Therefore, this study proposes a hybrid Temporal Convolutional Network–Generative Adversarial Network (TCN-GAN) model for stock price prediction and investment risk analysis using the Value-at-Risk (VaR) method with Historical Simulation. The TCN-GAN combines TCN's ability to capture long-term temporal patterns with the adversarial mechanism of GAN to improve prediction accuracy. The data consist of daily closing prices of PT Sawit Sumbermas Sarana Tbk (SSMS.JK) from Yahoo Finance, covering January 1, 2020, to September 30, 2025. A sensitivity analysis on sliding window lengths of 10, 20, and 30 days was conducted to validate model robustness, with window 20 identified as optimal. The TCN-GAN model significantly outperforms the ARIMA baseline, which yielded a MAPE of 18.12% and RMSE of 368.68, by achieving a MAPE of 3.22% and RMSE of 84.23. The model was further used to predict stock prices for the next five periods, yielding an average of IDR 1,647.82. The VaR analysis at a 95% confidence level with a five-day holding period indicates a maximum potential loss of IDR 146,204.
Co-Authors Ardia Eva Ardiani Dwi Arman Prasetya Kartika Maulida Hindrayani Nabilah Selayanti