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All Journal JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer)
Ani Dijah Rahajoe
Universitas Pembangunan Nasional “Veteran” Jawa Timur, Jl. Rungkut Madya No. 1, Gn. Anyar, Surabaya, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics

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Optimasi Hyperparameter CatBoost dengan Particle Swarm Optimization untuk Klasifikasi Hipertensi Muhammad Iqbal Al Afgany; Ani Dijah Rahajoe; Henni Endah Wahanani
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.16292

Abstract

Hypertension is a cardiovascular disease affecting 11,952,694 residents aged ≥15 years in East Java in 2019, yet only 40.1% received healthcare services. This study aims to analyze the effect of Particle Swarm Optimization (PSO) on CatBoost algorithm performance in hypertension level classification. The research dataset combined data from Puskesmas Kepatihan Gresik (191 data) and Kaggle (12,500 data) divided with an 80:10:10 ratio. PSO was used for CatBoost hyperparameter optimization including iterations, depth, learning_rate, and l2_leaf_reg. Model evaluation utilized accuracy, precision, recall, and F1-score metrics. Results show that CatBoost with PSO optimization achieved 96% accuracy with optimal configuration of iterations=100, depth=3, learning_rate=0.055, and l2_leaf_reg=3, 2% higher than without optimization (94%). This study proves the effectiveness of PSO in optimizing CatBoost hyperparameters for more accurate early hypertension detection
Co-Authors Henni Endah Wahanani Muhammad Iqbal Al Afgany