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All Journal Jurnal Media Infotama JFMR (Journal of Fisheries and Marine Research) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF SCIENCE AND SOCIAL RESEARCH JSAI (Journal Scientific and Applied Informatics) International Journal of Engineering, Science and Information Technology Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat
Julita, Rina
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Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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COMPARATIVE ANALYSIS OF HYPERPARAMETER OPTIMIZATION TECHNIQUES ON LIGHTGBM FOR ASTHMA PREDICTION Azmi, Zulfian; Julita, Rina; Irawati, Novica; Pariyasto, Sofyan; Purwawijaya, Ellanda
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7369

Abstract

This study presents a comparative study of hyperparameter optimization methods applied to the Light Gradient Boosting Machine (LightGBM) algorithm for asthma prediction. Traditional machine learning models often face limitations in accuracy and generalization capabilities due to suboptimal hyperparameter configurations. To address these challenges, this study evaluates and compares four approaches: Default LightGBM, RandomizedSearchCV, Optuna Optimization, and Bayesian Optimization. Experimental results show that Bayesian Optimization provides the best performance with an accuracy of 78%, a precision of 0.7778, a recall of 0.7778, an F1-score of 0.7778, and an ROC-AUC of 0.975. These findings emphasize the importance of selecting an appropriate optimization strategy to improve model performance in clinical prediction tasks. Overall, this study confirms the effectiveness of Bayesian Optimization in improving the predictive capabilities of LightGBM and provides an important contribution to the development of decision support systems in healthcare, particularly in the diagnosis and management of asthma
Co-Authors Agus, Raja Tama Andri Andri Agus, Raja Tama Anisa Mardhyath Azmi, Zulfian Ellanda Purwawijaya Eva Darnila Herlina Latipa Sari Irawati, Novica Jusuf Wahyudi Kurniawansyah, Arius Satoni Muhammad Ardiansyah Sembiring Mujiono, Mr. Mustika Fitri Larasati Sibuea Pariyasto, Sofyan Priyulida, Fitria Ramadhani, Andrew Risawandi, Risawandi Safira, Gina Sinuraya, Khairul Abdi