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All Journal Jurnal Pendidikan Vokasi INOTEKS : Jurnal Inovasi Ilmu Pengetahuan, Teknologi, dan Seni Excellent PAWIYATAN Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) SMATIKA Jurnal Pendidikan Olah Raga INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Mekanika dan Sistem Termal Jurnal Review Pendidikan Dasar : Jurnal Kajian Pendidikan dan Hasil Penelitian Civics Education And Social Science Journal (CESSJ) Jurnal Media Ekonomi Journal of Software Engineering Ampera Jurnal Pendidikan Sains dan Komputer Pawiyatan Jurnal Pustaka Mitra : Pusat Akses Kajian Mengabdi Terhadap Masyarakat Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Education Journal: Journal Educational Research and Development Jurnal Biologi MANGGALI
Sukoco Sukoco
STMIK Amikom Yogyakarta
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Pengembangan dan Optimasi Kalman Filter Adaptif Multi-Timeframe dalam Kerangka State-Space untuk Prediksi Arah Harga Valuta Asing: Development and Optimization of Multi-Timeframe Adaptive Kalman Filter in State-Space Framework for Foreign Exchange Price Direction Prediction Baradja, Abdillah; Sukoco, Sukoco; Mukti, Bayu
Jurnal Pendidikan Sains dan Komputer Vol. 6 No. 01 (2026): Artikel Riset, February 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jpsk.v6i01.8007

Abstract

The foreign exchange market is non-stationary, noisy, and highly sensitive to changes in information, making short-term price direction prediction a significant challenge. Although the Kalman Filter has been widely used within a state-space framework for trend and dynamic parameter estimation, most previous research has focused on statistical accuracy without directly linking it to actual trading performance. This study aims to develop and evaluate an adaptive Kalman Filter-based forex price direction prediction model and empirically test its implications for the performance of algorithmic trading systems. The study uses a quantitative experimental approach, analyzing historical EUR/USD data across the H1, H2, H3, and H4 timeframes over 1 year. The state-space-based Kalman Filter model is implemented in a MetaTrader 5 Expert Advisor and compared with the Moving Average Crossover strategy and the classic Kalman Filter. Evaluations are conducted at the prediction and trading levels using Total Net Profit, Profit Factor, Sharpe Ratio, and Drawdown. The proposed model demonstrates consistently positive performance across all timeframes, with a Profit Factor of 1.39–2.13 and a Sharpe Ratio of up to 5.70, significantly outperforming the technical baseline and the classic Kalman Filter, which exhibit unstable results. The integration of the Kalman Filter approach into an algorithmic trading system improved signal quality and risk efficiency during testing.
Co-Authors -, Sutriyanto Agus Prasetyo Utomo Ahlusi Sunnah Alhafiz, Fatia Anis Khoirunnisak Annisaul Khasanah Wulandari Khasanah Wulandari, Annisaul Khasanah Wulandari Khasanah Azizah, Siti Alfiyana Baradja, Abdillah Barnbang Subali, Barnbang Burhaein, Erick Candra, Wahyu Ema Utami Ghautama, Wisnu Satria Hadi, Agus Sutriyanto Hadi, Agustinus Sutriyanto I Dewa Bagas Suryajaya, I Dewa Bagas Irdyansah, Andry Irnawati Juriah Mulyanti Kadek Sukiyasa KHOIRUNNISAK, ANIS Kusuma, Ahmad Fauzi Anggi Ariesta M. Suyanto, M. Mansur . Moyo Hady Poernomo Mukti, Bayu Ramadhian Agus Triono Sudalyo Rama Rosita, Eliana Setyaningsih, Sri Sholichah, Thusma Sidhiq Andriyanto siti Nurindah Sari Sri Muryati Sri Sayekti Sunyoto Sunyoto Susanto, Yohanes Suyanto, M Titik Susiatik Tri Irianto TJ Yuli, Risvi Revita