OPTIMASI FUZZY TSUKAMOTO DALAM MENENTUKAN PREDIKSI HASIL TINGKAT RISIKO PENYAKIT JANTUNG MENGGUNAKAN ALGORITMA ARTIFICIAL BEE COLONY

PRATAMA, BENEDIKTUS GALIH and Rini, Dian Palupi (2025) OPTIMASI FUZZY TSUKAMOTO DALAM MENENTUKAN PREDIKSI HASIL TINGKAT RISIKO PENYAKIT JANTUNG MENGGUNAKAN ALGORITMA ARTIFICIAL BEE COLONY. Undergraduate thesis, Sriwijaya University.

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Abstract

Heart disease is a common health problem. If not treated promptly, heart disease can have various impacts on a person's health, even causing death. To be able to prevent heart disease early, a method is needed that can determine whether a person is at risk of heart disease or not. This study can provide information about the risk of heart disease by determining predictions using the Fuzzy Tsukamoto method with Artificial Bee Colony optimization. The data studied amounted to 40 data, originating from the Heart Disease Dataset taken from Kaggle. To determine the prediction, 5 risk factor variables were used, namely age, cholesterol, blood sugar, blood pressure, and maximum heart rate. The results of the study showed a decrease in error value using Artificial Bee Colony optimization, namely with a MAPE (Mean Absolute Percentage Error) value from 42.43% to 36.28%. These results indicate that the Fuzzy Tsukamoto method optimized with Artificial Bee Colony can determine the prediction of the level of heart disease risk well.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Penyakit Jantung, Fuzzy Inference System Tsukamoto, Artificial Bee Colony
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Benediktus Galih Pratama
Date Deposited:	04 Aug 2025 05:24
Last Modified:	04 Aug 2025 05:24
URI:	http://repository.unsri.ac.id/id/eprint/182205

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