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All Journal Griya Journal of Mathematics Education and Application Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Muhammad Budi Akbar
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Computer Science & IT Education Mathematics

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Pemodelan dan Simulasi Stokastik Fluktuasi Suhu Harian Menggunakan Model Geometric Brownian Motion Alfin Syahri; Muhammad Budi Akbar; Ruth Amelia Vega S. Meliala; Suvriadi Panggabean
Griya Journal of Mathematics Education and Application Vol. 5 No. 4 (2025): Desember 2025
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v5i4.931

Abstract

Penelitian ini membahas penerapan model Geometric Brownian Motion (GBM) untuk memodelkan dan mensimulasikan fluktuasi suhu harian di Kota New York secara stokastik. Data suhu diperoleh dari dataset Global Weather Repository (Kaggle) dan diolah untuk menghitung parameter drift (μ) dan volatilitas (σ) yang merepresentasikan tren serta tingkat ketidakpastian perubahan suhu. Simulasi dilakukan menggunakan metode Monte Carlo untuk menghasilkan beberapa lintasan kemungkinan perubahan suhu. Hasil penelitian menunjukkan bahwa model GBM mampu mengikuti pola suhu aktual dengan baik dan memberikan nilai Mean Absolute Percentage Error (MAPE) di bawah 10%, yaitu 6,47% untuk New York, 5,11% untuk Los Angeles, dan 4,81% untuk Chicago. Temuan ini membuktikan bahwa GBM efektif dalam menangkap dinamika stokastik suhu harian dan dapat digunakan sebagai alat prediksi iklim jangka pendek yang akurat, terutama di wilayah urban dengan fluktuasi suhu yang tinggi.
Optimasi Parameter Model LightGBM Menggunakan Algoritma Grey Wolf Optimizer untuk Prediksi Penyakit Ginjal Kronis Muhammad Alfin; Alvin Hafiz; Muhammad Budi Akbar; Adidtya Perdana
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1263

Abstract

Chronic kidney disease is an increasingly prevalent health issue that requires more precise clinical data-based early detection methods to enable timely and appropriate treatment. This study focuses on developing a predictive model for chronic kidney disease using the Light Gradient Boosting Machine (LightGBM) algorithm and enhancing its performance through hyperparameter optimization with the Grey Wolf Optimizer (GWO). The dataset used originates from public sources and undergoes several preprocessing steps, including missing value imputation, categorical feature encoding, outlier handling, initial feature selection, and stratified data splitting to maintain model quality. Three modeling approaches were evaluated: LightGBM with default parameters, LightGBM enhanced using Random Search, and LightGBM optimized with GWO. The experimental results indicate that the baseline model already performs well, Random Search improves accuracy and F1-score, and GWO achieves the highest AUC-ROC value despite requiring longer computation time. Significance testing through cross-validation shows that the performance differences among the three models are not statistically significant, suggesting that the observed improvements are not strong enough to determine a definitively superior optimization method. The feature importance analysis highlights that clinical indicators such as creatinine levels, glomerular filtration rate, blood pressure, and urine protein contribute most prominently to the prediction. Overall, the study demonstrates that LightGBM is a reliable model for early detection of chronic kidney disease, and hyperparameter optimization still offers added value that can support the development of AI-based clinical decision-support systems
Co-Authors Adidtya Perdana, Adidtya Alfin Syahri Alvin Hafiz Muhammad Alfin Ruth Amelia Vega S. Meliala Suvriadi Panggabean