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Nicholas Svensons
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science

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Evaluasi Performa Random Forest, XGBoost, dan LightGBM dalam Diagnosis Dini Diabetes Mellitus Hendra, Hendra Kurniawan; Asmaul Dwi Akbar; Nicholas Svensons; Yandi Jaya Antonio; Karnila, Sri; Safitri, Egi; Nurjoko, Nurjoko
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 2 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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Abstract

Diabetes mellitus is a long-term condition marked by elevated blood sugar levels, which can lead to serious complications such as heart disease, kidney failure, and vision impairment. Early detection plays a vital role in minimizing these risks and enhancing patients' quality of life. This research focuses on assessing the performance of three machine learning algorithms—Random Forest, XGBoost, and LightGBM—in predicting diabetes risk. The dataset utilized originates from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), comprising 768 samples with 9 key features. The research methodology involves multiple stages, including data collection, preprocessing, addressing data imbalance using SMOTE, data splitting for training and testing, algorithm implementation, and model evaluation through accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) metrics. Findings reveal that Random Forest delivers the highest performance with an AUC score of 86%, followed by XGBoost (83%) and LightGBM (82%). With its strong accuracy, this model holds potential as a valuable tool for early diabetes diagnosis, contributing to faster and more precise medical decision-making.
Evaluasi dan Perbandingan Metode XGBoost dan LightGBM Dalam Deteksi Dini Penyakit Alzheimer Muhammad Rezky Adytama; Egi Safitri; Asmaul Dwi Akbar; Nicholas Svensons; Raka Sebastian Musin
Journal of Data Science Methods and Applications Vol. 2 No. 1 (2026)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

Early detection of Alzheimer’s disease is a key step in slowing disease progression and improving patients’ quality of life. This study evaluates and compares the performance of the XGBoost and LightGBM algorithms in diagnosing Alzheimer’s disease, using a longitudinal dataset comprising 2.149 subjects. The dataset underwent meticulous preprocessing, including handling missing data, feature selection, and duplicate data removal, to ensure data reliability. Model evaluation was conducted based on accuracy, precision, recall, F1-score, and Mean Squared Error (MSE) metrics. The results demonstrate that the LightGBM algorithm outperforms XGBoost, achieving an accuracy of 87%, precision of 87%, recall of 85%, F1-score of 85%, and an MSE of 0.12. The advantages of LightGBM include computational efficiency and the ability to handle large-scale data, making it more effective than XGBoost. This study provides guidance on selecting the optimal algorithm for clinical applications, enabling more effective early interventions to mitigate the adverse impacts of Alzheimer’s disease.
Co-Authors - Nurjoko Asmaul Dwi Akbar EGI SAFITRI Hendra, Hendra Kurniawan Muhammad Rezky Adytama Raka Sebastian Musin Safitri, Egi Sri Karnila Yandi Jaya Antonio