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All Journal ComEngApp : Computer Engineering and Applications Journal JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jurnal Varian ESTIMASI: Journal of Statistics and Its Application Jurnal Diferensial HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Enthusiastic : International Journal of Applied Statistics and Data Science MATHunesa: Jurnal Ilmiah Matematika Buletin Sistem Informasi dan Teknologi Islam Journal of Information Systems and Technology Research Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Journal of Data Mining and Information Systems Kohesi: Jurnal Sains dan Teknologi Indonesian Journal on Data Science Parameter: Jurnal Matematika, Statistika dan Terapannya Emerging Statistics and Data Science Journal
Shindi Shella May Wara
Universitas Pembangunan Nasional Veteran Jawa Timur
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Public Health Social Sciences Other

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Prediksi Kecepatan Angin Menggunakan Gated Recurrent Unit (GRU) dengan Estimasi Ketidakpastian Monte Carlo Dropout pada Data BMKG Tanjung Perak Alvino Hadiyan Pradipta; Muhammad Rafli Feandika Nugroho; Maretta Fairuz Luthfia Winoto Putri; Alfan Rizaldy Pratama; Shindi Shella May Wara; Muhammad Nasrudin
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.491

Abstract

Abstrak: Keterbatasan metode prediksi konvensional dalam memodelkan dependensi temporal dan ketidakpastian prediksi mendorong pengembangan pendekatan berbasis deep learning. Penelitian ini bertujuan mengembangkan model prediksi kecepatan angin menggunakan metode Gated Recurrent Unit (GRU) pada data meteorologi yang berasal dari stasiun pengamatan BMKG Tanjung Perak. Penelitian ini dilakukan karena metode prediksi sebelumnya masih memiliki keterbatasan dalam menangkap pola temporal dan dependensi jangka panjang pada data time series, serta umumnya belum mengakomodasi ketidakpastian hasil prediksi. GRU dipilih karena mampu memodelkan dependensi temporal secara efisien, sedangkan simulasi Monte Carlo digunakan untuk menghasilkan beberapa skenario prediksi dan mengestimasi interval kepercayaan. Data yang digunakan mencakup parameter kecepatan angin dengan interval waktu tertentu. Hasil evaluasi menunjukkan bahwa model menunjukkan performa yang baik untuk memprediksi kecepatan angin secara akurat, dengan nilai MAE sebesar 0,37, RMSE sebesar 0,50, MAPE sebesar 5,78%, dan R² sebesar 0,986. Dengan demikian, model yang dikembangkan dapat menjadi solusi dalam analisis dan peramalan data time series meteorologi secara komprehensif.Kata kunci: Prediksi Kecepatan Angin, Analisis Time Series, Gated Recurrent Unit (GRU), Simulasi Monte Carlo, Data MeteorologiAbstract: The limitations of conventional forecasting methods in modeling temporal dependencies and forecast uncertainty have driven the development of deep learning-based approaches. This study aims to develop a wind speed forecasting model using the Gated Recurrent Unit (GRU) method on meteorological data from the BMKG Tanjung Perak observation station. This study was conducted because previous prediction methods still have limitations in capturing temporal patterns and long-term dependencies in time series data, and generally do not accommodate the uncertainty of prediction results. GRU was chosen because it is capable of modeling temporal dependencies efficiently, while Monte Carlo simulation was used to generate several prediction scenarios and estimate confidence intervals. The data used includes wind speed parameters at specific time intervals. The evaluation results show that the model shows good performance in predicting wind speed accurately, with an MAE of 0.37, an RMSE of 0.50, a MAPE of 5.78%, and an R² of 0.986. Thus, the developed model can serve as a solution for comprehensive analysis and forecasting of meteorological time series data.Keywords: Wind Speed Prediction, Time Series Analysis, Gated Recurrent Unit (GRU), Monte Carlo Simulation, Meteorological Data 
Price Dynamics and Financial Risk Analysis A Neural Hierarchical Time-Series Forecasting Approach Vannesa Nathania; Aviolla Terza Damaliana; Shindi Shella May Wara
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

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Abstract

The highly volatile nature of cryptocurrency prices often causes conventional predictive models to fail in capturing complex nonlinear patterns. This study integrates the Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS) deep learning model with nonparametric Historical Simulation Value-at-Risk (VaR) method for price forecasting and risk analysis. Using univariate data on daily Ethereum closing prices from January 1, 2021, to January 31, 2025 (N = 1,491 observations), the out-of-sample evaluation was executed using a rolling cross-validation scheme initiated testing from a cut-off point in April 2024 through December 2024, where each evaluation window was set for the next 30 days. The research results show that the N-HiTS model can predict price dynamics with high accuracy, achieving an MAPE of 3.25%, an MAE of 107.825, an RMSE of 136.83, and directional accuracy of 48.28%. Risk analysis using historical simulation yielded a VaR of -6.23% at a 95% confidence level.
Co-Authors AA Sudharmawan, AA Abda Abda Abdillah, Indah Rahma Adziima, Andri Fauzan Ahmad, Davin Anezta Alfan Rizaldy Pratama Alvino Hadiyan Pradipta Alzam, Muhammad Arsyad Amellia Harmaimun Hidayah Amri Muhaimin Ananta, Aditya Putra Andri Faudzan Adziima Andri Fauzan Adziima Anugrah, Muhammad Cahya Raka Aviolla Terza Damaliana Aviolla Terza Damaliana Damaliana, Aviolla Terza Diana Novitasari Dwi Mahardhika, Rivaldi Elmaliyasari, Shifa Endah Setyowati Fauzan Adziima, Andri Fernando, Moch. Firman Firqi Nashrullah, Ahmad Indira Kaffi, Laisal Karina Auralia Kartika Maulida Hindrayani Khairunisa, Adenda Kresna Wira Yudha, I Nyoman Kurniawan, Muhammad Erlangga Mahardhika, Rivaldi Dwi Maretta Fairuz Luthfia Winoto Putri Marthalia Kusumarima Maulana, Mohammad Hikmal Maulida Hindrayani, Kartika Mega Rizkya Arfiana Muhammad Nasrudin Muhammad Nasrudin Muhammad Rafli Feandika Nugroho Nabila Yudhitya Larasati Nashrullah, Ahmad Firqi Nugroho, Muhammad Rafli Feandika Nur Indah Nirmalasari Pradipta, Alvino Hadiyan Pratiwi, Nanda Aulia Putri, Maretta Fairuz Luthfia Winoto Putri, Milla Akbarany Bakhtiar rachmanto, Nugroho Fajar Ramadhani, Dafinah Rhomaningtias, Lina Rizkiyah, Selly Rusdiyanto, Nur Rahmat Saputra, Wahyu Syaifullah Jauharis Terza Damaliana, Aviolla Vannesa Nathania Wahyu Syaifullah Jauharis Saputra Wiyono, Farhan Syah Putra Zahwa, Aniq Farichatus