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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.
Analisis Perbandingan Algoritma Klasifikasi Decision Tree, K-Nearest Neighbors, Naive Bayes, dan Random Forest pada Data Pemilihan Legislatif KPU Menggunakan Kurva ROC Naura Fayza I; Nicholas Svensons; Sri Asni Fatmawati; Pricillia Rotua S; Khanaya Erviona
Journal of Data Science Methods and Applications Vol. 1 No. 1 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

In the context of the digital information era, analysis of general election data is crucial for understanding political dynamics. Legislative election data from the Indonesian General Election Commission (KPU) provides insight into voter behavior and election results. Selection of an appropriate classification algorithm is the main challenge in producing accurate predictions. This study compares four classification algorithms: Decision Tree, K-Nearest Neighbors (KNN), Naive Bayes, and Random Forest, using Receiver Operating Characteristic (ROC) curves as the main evaluation. The results show Random Forest performs best in handling legislative election data, providing important insights for future policy and research.
Co-Authors - Nurjoko Asmaul Dwi Akbar Hendra, Hendra Kurniawan Khanaya Erviona Naura Fayza I Pricillia Rotua S Safitri, Egi Sri Asni Fatmawati Sri Karnila Yandi Jaya Antonio