PENINGKATAN AKURASI KLASIFIKASI PENYAKIT JANTUNG MENGGUNAKAN BACKPROPAGATION DENGAN SELEKSI FITUR ABC DAN K-NN

PUTRA, PANDITO DEWA and Sukemi, Sukemi and Rini, Dian Palupi (2021) PENINGKATAN AKURASI KLASIFIKASI PENYAKIT JANTUNG MENGGUNAKAN BACKPROPAGATION DENGAN SELEKSI FITUR ABC DAN K-NN. Master thesis, Sriwijaya University.

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Abstract

One of the non-communicable diseases that is susceptible to occur, especially when an individual is of a productive age, is heart disease. Death cases caused by heart disease based on data obtained from the World Health Organization (WHO) experienced more than 17 million victims in the world, a large enough number so that is the basis for researchers to process medical data on heart disease. The method to be applied for classification data mining was using the backpropagation algorithm because this algorithm had a simplicity and a fairly good performance. The backpropagation algorithm worked by adjusting the interconnected weights of neurons to achieve the minimum error between the output of the classification results and the real output. However, the Backpropagation Algorithm in achieving convergence tended to be slow when it got optimal accuracy, so the artificial bee colony algorithm was applied which usually converges to the global optimal efficiently to be used as feature selection from the applied Cleveland heart disease dataset. Then the k-nearest neighbor algorithm would be used to get the fitness value during the feature selection process because it was effective in reducing data dimensions while maintaining high classification accuracy. The results of the classification experiment with backpropagation without a combination resulted in an accuracy of 92.88%, while the combination classification experiment with backpropagation with artificial bee colony and k-nearest neighbor experienced an increase in accuracy by 95.93%.

Item Type:	Thesis (Master)
Uncontrolled Keywords:	Penyakit jantung, cleveland, backpropagation, ABC, k-NN
Subjects:	H Social Sciences > HF Commerce > HF5410-5417.5 Marketing. Distribution of products > HF5415.126.R38 Database marketing--Statistical methods. Data mining--Statistical methods. Big data--Statistical methods. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data. Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Users 10820 not found.
Date Deposited:	10 Feb 2021 04:09
Last Modified:	10 Feb 2021 04:09
URI:	http://repository.unsri.ac.id/id/eprint/42554

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