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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan East Java Economic Journal
Irsandy, Diego
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences 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.
Sentiment Analysis Towards Leading Tourism in Banyuwangi As A Policy Consideration to Improve Economic Stability and Resilience Darmanto, Darmanto; Irsandy, Diego; Abiyu Aqilah, Zaki; Hartanti Putri Yulianing Damayanti, Rismania
East Java Economic Journal Vol. 9 No. 1 (2025)
Publisher : Kantor Perwakilan Bank Indonesia Provinsi Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53572/ejavec.v9i1.152

Abstract

The tourism sector is one of the important aspects in improving the economy of a region, including Banyuwangi. The Banyuwangi Regency Government took steps to make tourism a leading sector in supporting the economy. Efforts to increase economic stability and resilience can be made by optimizing tourism, which includes tourist destinations, restaurants and inns. Utilizing social media to analyze tourist reviews is a strategy in optimizing tourism. The purpose of this research is to analyze the sentiment of tourists in Banyuwangi. Sentiment analysis approach with Lexicon-based method analyzed with Support Vector Machine algorithm is done to find out the opinion of tourists about tourist sites in Banyuwangi. The analysis results show that the SVM algorithm can provide a fairly good performance with an accuracy of more than 70%. Based on sentiment analysis, improving the online reservation system and regular training for staff will reduce visitor dissatisfaction regarding waiting time and service quality. In addition, maintaining natural beauty and aesthetics through environmental conservation programs can increase Banyuwangi’s tourism attractiveness and support the hospitality, restaurant and transportation sectors. The government can implement policies that are more responsive to the needs and desires of tourists, thereby increasing visitor satisfaction and strengthening the competitiveness of the tourism sector.
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.
Co-Authors Abiyu Aqilah, Zaki Damayanti, Rismania Hartanti Putri Yulianing Darmanto Darmanto Hartanti Putri Yulianing Damayanti, Rismania 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