KLASIFIKASI DATA PENYAKIT DIABETES MENGGUNAKAN ALGORITMA DECISION TREE DAN PARTICLE SWARM OPTIMIZATION

PATRICIA, PUTRI and Rini, Dian Palupi (2024) KLASIFIKASI DATA PENYAKIT DIABETES MENGGUNAKAN ALGORITMA DECISION TREE DAN PARTICLE SWARM OPTIMIZATION. Undergraduate thesis, Sriwijaya University.

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Abstract

The prevalence of diabetes is very high in Indonesia so diabetes classification is crucial for early diagnosis in detecting this disease. The classification of diabetes data carried out in this study used an algorithm Decision Tree C4.5. However, the algorithm decision tree has weaknesses when handling large datasets, where not all features are relevant for the classification process, which can reduce the level of accuracy. One of the algorithms that can be integrated with the C4.5 decision tree algorithm to select relevant features is Particle Swarm Optimization (PSO). This research also uses a data balancing method to obtain more accurate results. The data balancing method used is Synthetic Minority Over-sampling Technique (SMOTE) day Random Undersampling (RUS). The research results show that the combination of the C4.5 algorithm with PSO in feature selection and the data balancing method significantly increases the accuracy of diabetes classification, producing the highest accuracy of 83.33% and an increase in accuracy of 16.33% compared to using C4.5 without PSO. The optimal PSO parameters are number of particles=10, C1 value=2, C2 value=2, and maximum iteration=10.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Diabetes, Classification, Decision Tree, C4.5, Feature Selection, PSO, SMOTE, RUS
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Putri Patricia
Date Deposited:	23 Aug 2024 02:03
Last Modified:	23 Aug 2024 02:03
URI:	http://repository.unsri.ac.id/id/eprint/155867

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