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All Journal MEDIA PETERNAKAN - Journal of Animal Science and Technology Agromet FORUM STATISTIKA DAN KOMPUTASI Forum Pasca Sarjana Media Statistika IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Scientific Journal of Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan Zero : Jurnal Sains, Matematika, dan Terapan ComTech: Computer, Mathematics and Engineering Applications Inferensi Jurnal Tanah dan Iklim Jurnal Natural Jurnal Teknik Informatika (JUTIF) Jurnal Natural Xplore: Journal of Statistics STATISTIKA Jurnal Veteriner dan Biomedis PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Indonesian Journal of Statistics and Its Applications
Khairil Anwar Notodiputro
Department Of Statistics, IPB University
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Social Sciences Transportation Veterinary Other

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Stacking Ensemble RNN-LSTM Models for Forecasting the IDR/USD Exchange Rate with Nonlinear Volatility Pratiwi, Windy Ayu; Sumertajaya , I Made; Notodiputro , Khairil Anwar
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5057

Abstract

Abstract - Predicting exchange rates with high volatility and nonlinear patterns presents a critical challenge in financial analysis. Deep learning models such as RNN and LSTM are widely used for their ability to capture temporal dependencies, yet each has limitations when applied individually. This study aims to enhance the prediction accuracy of the Indonesian Rupiah (IDR) to US Dollar (USD) exchange rate by implementing a stacking ensemble approach that combines RNN and LSTM models. The dataset consists of 522 weekly observations from January 2015 to December 2024, sourced from the official website of Bank Indonesia (bi.go.id). In the proposed framework, RNN and LSTM serve as base learners, while linear regression acts as the meta-learner. Model performance is evaluated using RMSE, MAPE, and MSE. The results indicate that the stacking ensemble consistently outperforms the individual models, achieving an RMSE of 117.91, a MAPE of 0.01, and an MSE of 13,901.67. The model effectively captures historical patterns and delivers stable and accurate predictions. In conclusion, the stacking ensemble approach developed in this study contributes to the advancement of ensemble learning techniques in computer science and offers practical value for financial decision-makers, particularly in managing complex and dynamic exchange rate scenarios.
Co-Authors . Barizi . SUTIKNO A. Bey Adlina Khairunnisa Agus Mohamad Soleh Aji H. Wigena Anang Kurnia Annisa Putri Utami ASEP SAEFUDDIN Asfar Aulya Permatasari Ayunda Pratiwi Azlam Nas Bagus Sartono Bayu Suseno Budi Susetyo Bukhari, Ari Shobri Cahyani Oktarina Christiana Anggraeni Putri D Diapari Dani Al Mahkya Dwi Jayanti Erfiani, Erfiani Etis Sunandi FAHREZAL ZUBEDI FARDILLA RAHMAWATI Farit Mochamad Afendi Fitrianto, Anwar H. M H Bintoro Hari Wijayanto Hari Wijayanto Hari Wijayanto Herdina Kuswari I Las I Made Sumertajaya I. LAS Indahwati Indahwati, Indahwati Intan Nirmala J Jachja Jajah K. Wagiono Kurnia, Anang Kusman Sadik Laily Nissa Atul Mualifah M S Saeni Muhammad Ilham Abidin Muhammad Nur Aidi Nurfadilah, Khalilah Pardomuan Robinson Sihombing Pratiwi, Windy Ayu R Boer R. BOER Rahardiantoro, Septian Rini Irfani Rizky Zulkarnain Sachnaz Desta Oktarina Septian P Palupi Sri Wahyuni Stephan Chen Suseno Bayu Tri Listianingrum Ulfah Sulistyowati Utami Dyah Syafitri Yekti Widyaningsih