PERBANDINGAN PERFORMA ALGORITMA DECISION TREE C4.5 DAN RANDOM FOREST UNTUK DETEKSI PENYAKIT DIABETES

QURRATU'AIN, MARSA and Utami, Alvi Syahrini (2025) PERBANDINGAN PERFORMA ALGORITMA DECISION TREE C4.5 DAN RANDOM FOREST UNTUK DETEKSI PENYAKIT DIABETES. Undergraduate thesis, Sriwijaya University.

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Abstract

Diabetes is a chronic disease with a continuously increasing global prevalence. Early detection poses a major challenge because symptoms often appear only when the condition is already severe. The classification of diabetes plays a crucial role in recognizing the disease early to allow for faster and more accurate interventions. According to data from the World Health Organization in 2020, over the past three decades, the prevalence of diabetes has significantly increased in various countries. This research is conducted to compare the performance of two classification methods, that are Decision Tree C4.5 and Random Forest, in classifying diabetes disease based on patients’ health data records. This research includes the design stage of classification system, software implementation, and the performance evaluation of each method. the dataset used is from processed health data records, even though the quality is considered less than ideal. The test results show that the Random Forest algorithm achieved the highest accuracy of 89.61%, while the C4.5 Decision Tree algorithm reached an accuracy of 88.63%. In addition, the Random Forest algorithm also demonstrated a higher precision compared to the C4.5 Decision Tree. This is due to Random Forest using an ensemble approach that employs many Decision Trees, resulting in more accurate predictions.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Diabetes Classification, Decision Tree C4.5, Random Forest, Data mining, Random under sampling, Min-Max Scaling
Subjects:	T Technology > T Technology (General) > T173.2-174.5 Technological change T Technology > T Technology (General) > T60-60.8 Work measurement. Methods engineering > T60.A3-Z General works Work measurement
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Marsa Qurratu'ain
Date Deposited:	07 Jul 2025 07:02
Last Modified:	07 Jul 2025 07:02
URI:	http://repository.unsri.ac.id/id/eprint/176905

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