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All Journal Plantropica: Journal of Agricultural Science BAREKENG: Jurnal Ilmu Matematika dan Terapan
Irsandy, Diego
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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STRENGTHENING SYARIAH FINANCIAL MARKETS WITH GARCH-BASED STOCK PRICE FORECASTING AND VAR-RISK ASSESSMENT Darmanto, Darmanto; Darti, Isnani; Astutik, Suci; Nurjannah, Nurjannah; Lee, Muhammad Hisyam; Damayanti, Rismania Hartanti Putri Yulianing; Irsandy, Diego
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1217-1236

Abstract

Indonesia, as the largest Muslim-majority country, has significant potential to enhance its Shariah financial sector, which has been growing rapidly, around 7.43% from 2023 to 2024, and contributing to the national economy. However, political and natural disasters have influenced the economy and Shariah-compliant stocks. This study focuses on forecasting Shariah-compliant stock prices using Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models and estimating investment risks via Value at Risk (VaR) for four Islamic banks listed in IDX: BRIS, BTPS, BANK, and PNBS. The findings indicate that GARCH models effectively capture stock price dynamics and provide accurate 10-day forecasts. Additionally, the models reliably predict VaR, validated through backtesting at various confidence levels. These insights are valuable for financial regulators and risk managers, aiding in policy design to ensure market stability by enabling the implementation of measures such as stricter capital reserve requirements for institutions with high-risk exposure and mandatory adoption of advanced risk management techniques like dynamic stress testing. Such policies not only mitigate systemic risks during periods of financial volatility but also enhance the overall resilience and robustness of the financial system. For investors, accurate risk predictions support informed decision-making, enhance portfolio protection, and optimize risk management.
BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA Astutik, Suci; Rahmi, Nur Silviyah; Irsandy, Diego; Saniyawati, Fang You Dwi Ayu Shalu; Mashfia, Fidia Raaihatul; Lusiana, Evelin Dewi; Risda, Intan Fadhila; Susanto, Mohammad Hilmi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1105-1116

Abstract

Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming, and other factors. Therefore, this research aims to model daily rainfall using the Bayesian Neural Network (BNN) approach, combining the Bayesian Method and Artificial Neural Network (ANN). ANN is suitable for rainfall models that have intermittent characteristics. Meanwhile, the Bayesian method provides advantages in producing model parameter inferences that provide uncertainty measurements in predictions. BNN is expected to deliver better daily rainfall predictions than ANN. This research used daily rainfall data in East Jawa, and the results show that the Bayesian Neural Network produces better rainfall predictions when describing rainfall in East Java. These predictions will be very useful for the government and the people of East Java province to prevent flooding. Also, with rainfall predictions, people will know more about what crops should be planted during the rains.
Flood Prediction Using Modeling Extreme Rainfall in East Java, Indonesia Irsandy, Diego; Astutik, Suci; Astuti, Ani Budi
Plantropica: Journal of Agricultural Science Vol. 11 No. 1 (2026): Februari
Publisher : Department of Agronomy, Faculty of Agriculture, Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jpt.2026.011.1.2

Abstract

Extreme value theory (EVT) is a statistical method that is concerned with the analysis of the extreme values of a distribution. EVT is often used to model the behavior of rare and extreme events, such as floods caused by extreme rainfall phenomena. There are two methods for identifying the movement of extreme values, namely Block Maxima (BM) and Peaks over Threshold (POT). The Generalized Extreme Value (GEV) distribution has three parameters and is used to model the distribution of extreme values using the BM method. On the other hand, the classic method of EVT does not capture uncertainty in the data. The Bayesian method is one of the statistical methods that can use information from data and prior knowledge. This research aims to model EVT-BM using a Bayesian approach for rainfall data at eleven weather stations in Jawa Timur. The result shows that all rainfall distributions at different weather conditions have a value of the parameter shape equal to 0, which implies a Weibull distribution. This paper also provides return level of 6 months, 2, 5, and 10 years respectively.
Co-Authors Ani Budi Astuti Damayanti, Rismania Hartanti Putri Yulianing Darmanto Darmanto Isnani Darti Lee, Muhammad Hisyam Lusiana, Evelin Dewi Mashfia, Fidia Raaihatul Nurjannah Nurjannah Rahmi, Nur Silviyah Risda, Intan Fadhila Saniyawati, Fang You Dwi Ayu Shalu Suci Astutik Susanto, Mohammad Hilmi