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All Journal JURNAL ILMIAH MATEMATIKA DAN TERAPAN Jurnal Statistika dan Matematika (Statmat)
Muhamad Hilman Rizaldi
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Prediksi IHSG dengan Gerak Brown Geometrik Termodifikasi Kalman Filter dan Jump Diffusion Amigo, Royyan; Muhamad Hilman Rizaldi; Ahmad Farrel As Syahidani; Danang Wahyu Pamungkas
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 7 No 2 (2025)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v7i2.51051

Abstract

Indeks Harga Saham Gabungan (IHSG) memiliki peran penting sebagai indikator kondisi ekonomi dan pasar modal di Indonesia. Pergerakan nilai IHSG dipengaruhi oleh berbagai faktor, seperti jumlah uang beredar dan kinerja perusahaan di Indonesia. Ketidakstabilan akibat faktor tersebut menjadi tantangan dalam menghasilkan prediksi IHSG yang akurat. Untuk memodelkan pergerakan IHSG yang kompleks, Gerak Brown Geometrik (GBG) sering digunakan karena mampu merepresentasikan fluktuasi harga dengan unsur stokastik. Namun, GBG memiliki keterbatasan dalam menghadapi lonjakan harga mendadak dan memiliki error yang besar pada prediksi jangka panjang akibat parameternya yang konstan. Oleh karena itu, penelitian ini bertujuan mengembangkan GBG dengan pendekatan Jump Diffusion untuk menangkap lompatan harga secara tiba-tiba, serta mengkombinasikan GBG dengan Kalman Filter untuk mengestimasi parameter secara adaptif dan meningkatkan akurasi prediksi. Penelitian ini membandingkan akurasi dari metode GBG, GBG Kalman Filter, dan GBG Jump diffusion menggunakan nilai Mean Absolute Percentage Error (MAPE). Prediksi nilai IHSG menggunakan metode GBG, GBG Kalman Filter, dan GBG Jump Diffusion terbukti sangat akurat, ditunjukkan oleh nilai MAPE yang seluruhnya berada di bawah 10%. Hasil MAPE yang didapat dari setiap metode pada 500 iterasi sebagai berikut: GBG sebesar 1.0711%, GBG Kalman Filter sebesar 0.36%, dan GBG Jump Diffusion sebesar 0.9136 %. Hal ini menunjukkan bahwa metode GBG Kalman Filter dan GBG Jump Diffusion dapat memprediksi pergerakan nilai IHSG yang lebih baik dari GBG.
BBQ Weather Prediction in Basel Using Ensemble Machine Learning Royyan Amigo; Reyhan Ksatria Brahmacarya; Muhamad Hilman Rizaldi
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 23 No. 1 (2026)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2026.v23.i1.18187

Abstract

Weather-dependent decision making, such as planning an outdoor barbecue (BBQ), benefits from short-term forecasts that are both accurate and honestly evaluated. This study addresses two overlooked risks in applied weather classification: label leakage from same-day rule-based targets, and validation-set overfitting caused by repeated model-selection decisions. Using the ECA&D Basel daily weather records (2000–2010), the original BBQ-weather label was found to be fully determined by same-day precipitation, so the task was reframed as next-day forecasting through one-day lag features and target shifting. Data were split chronologically into training, validation, and test sets (60:20:20) to preserve temporal independence. Five heterogeneous classifiers (CatBoost, LightGBM, RUSBoost, Nearest Centroid, SGDClassifier) were compared, tuned with a Genetic Algorithm, and combined through three ensemble strategies: Weighted Voting via Dirichlet-distributed random search, Stacking, and Greedy Ensemble Selection. Weighted Voting achieved the best validation F1-score (0.6841), with RUSBoost receiving the largest weight (0.6700). A 7-feature subset, selected via SHAP, native feature importance, and linear coefficients, was statistically indistinguishable from the full 22-feature model (McNemar test, = 0.8388) and was adopted as the final model for parsimony. On the held-out test set, the final model achieved F1 = 0.6776, ROC-AUC = 0.9076, and PR-AUC = 0.6961, with only a 0.0065 gap from validation performance, confirming strong generalization. These results demonstrate that rigorous chronological splitting and formal statistical testing can materially change both the interpretation and the trustworthiness of ensemble classification results in weather-dependent decision support.
Co-Authors Ahmad Farrel As Syahidani Amigo, Royyan Danang Wahyu Pamungkas Reyhan Ksatria Brahmacarya Royyan Amigo