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All Journal Jurnal Sains dan Seni ITS Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement) BAREKENG: Jurnal Ilmu Matematika dan Terapan Variance : Journal of Statistics and Its Applications Jurnal Abdi Masyarakat Indonesia Jurnal Pemberdayaan Masyarakat Indonesia (JPMI) DEDIKASI : Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Mathematics and Applications Journal of Empowerment Indonesia Indonesian Actuarial Journal Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Mey Lista Tauryawati
Business Mathematics Department, School Of Applied STEM, Universitas Prasetiya Mulya
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MAPPING DISASTER-PRONE AREAS ON JAVA ISLAND USING THE K-PROTOTYPES ALGORITHM Mey Lista Tauryawati; Ahmad Fuad Zainuddin
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0179-0196

Abstract

Clustering in disaster areas is often implemented as a disaster mitigation effort with the aim of minimizing risk. Determining the appropriate clustering method based on the data set will influence the clustering results. K-Prototypes is a clustering method that is capable of handling mixed data, numerical and categorical data, so this method is suitable to clustering disaster prone area with mixed data of disaster factors such as incident intensity, type of disaster, population density, and level of infrastructure vulnerability. This research focuses on disaster prone areas on Java Island and clustering using K-Prototypes to group and map areas that have the highest to lowest levels of disaster vulnerability based on the number of incidents, number of victims, and the amount of damage to facilities and the type of disaster. The clustering results obtained mapping of cities in the province into cluster groups based on the level of vulnerability and calculated potential losses based on disasters in each province. Afterward, the clustering results are used to determine priority areas for disaster mitigation to minimize losses.
Forecasting Rupiah-to-US Dollar Exchange Rate 2020 - 2025 Using a Fuzzy Time Series Markov Chain Model Tiur Masayu Rahmadani; Rosa Maeni; Camelia Miftahur Rizki Kiem Hwa; Iftha Nikmatul Khasanah; Winner Yeo; Kgs. M. Rifat Alfarisi; Yohana Joevanca Kurniawan; Adriano Fadlan Juwono; Mey Lista Tauryawati
Indonesian Actuarial Journal Vol. 1 No. 1 (2025)
Publisher : Persatuan Aktuaris Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65689/iajvol01no1pp060-072

Abstract

The exchange rate of the Indonesian Rupiah against the US Dollar experiences frequent fluctuations, making economic forecasting and financial planning more difficult. This study aims to enhance exchange rate prediction accuracy by combining Fuzzy Time Series with Markov Chain probability transitions. The approach is grounded in the idea that probabilistic modeling of state changes improves the representation of dynamic currency behavior. Using daily IDR/USD data from April 2020 to March 2025, the methodology involves two main steps: fuzzifying historical exchange rate data into linguistic variables, and applying a Markov Chain to compute transition probabilities between these fuzzy states. The model’s forecasting accuracy is evaluated using mean absolute percentage error. Results show that the hybrid model achieves a lower error rate of 0.50%, compared to 0.61% using conventional Fuzzy Time Series alone. This demonstrates the hybrid model’s ability to capture both sudden market changes and stable patterns effectively. The findings suggest that the integration of Markov Chain transitions significantly improves the predictive performance of fuzzy-based models. In conclusion, this hybrid method provides a practical and reliable forecasting tool for financial analysts and policymakers. Future research could include additional economic indicators and explore alternative probability weighting methods to further enhance model accuracy.
Co-Authors Abadi Nugroho Adriano Fadlan Juwono Altion Markoli Arfittariah Arleta, Chelsea Bernardinus Realino Yudianto Camelia Miftahur Rizki Kiem Hwa Derick, Luigi Deven, Deven Dior, Daido Van Elvira Wongso Erica Faizah Sari Gandawijaya, Carissa Glorify Megumi T W Glorya Ichaisa G R Hansen Juni Lieus Iftha Nikmatul Khasanah Ilham Ramadhan Nur Ahmad Jessica Cheri T Palloan Keane, Roy Kgs. M. Rifat Alfarisi Kyla Kanyaka Purbosatrio Leffiana, Rachelle Viensisca Maria Zefanya Sampe Mathilda Mohammad Isa Irawan Nicholas Gilbert Sugianto Nickken Khinbie Setiawan Pandita, Dominicus Hanschen Phillip Fabian Rosa Maeni Sampe, Maria Z. Shabrina Amira Stefanny Putri Teja, Satya Dhira Alfa Tiur Masayu Rahmadani Turahyo William Stevanno Winner Yeo Yohana Joevanca Kurniawan Zainuddin, Ahmad Fuad