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Hendra, Hendra Kurniawan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics 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.
Penerapan Metode Klasifikasi Decision Tree dalam Prediksi Kanker Payudara Menggunakan Algoritma C4.5 Nurjoko, Nurjoko; Hendra, Hendra Kurniawan; Cici Cahyati; Elvira Uthia Rustanti; Hiya Cahya Mujahidah; Amanda Putri Maharani; Rosita; Agus Rahardi
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

Breast cancer is a deadly disease that require early detection and accurate prediction to improve recovery chance. This research aims to predict breast cancer using Data Mining technique with Decision Tree C4.5 algorithm. The dataset includes attributes such as tumor size, estrogen status, progesterone status, Progesterone Status, Survival Month, and status. These attributes were selected based on their clinical relevance and predictive potential in the context of breast cancer. The classification results showed a high level of accuracy with a prediction history of 658 surviving breast cancer patients and a precision class of 91.90%. This study has an accuracy rate of 89,81%. These findings have the potential to be developed int a medical decision support system to assist in more objective and efficient.   Keywords—Breast Cancer, Data Mining, Decision Tree, C4.5, Prediction, Accuracy
Co-Authors - Nurjoko Agus Rahardi Amanda Putri Maharani Asmaul Dwi Akbar Cici Cahyati Elvira Uthia Rustanti Hiya Cahya Mujahidah Nicholas Svensons Rosita Safitri, Egi Sri Karnila Yandi Jaya Antonio