KLASIFIKASI PENYAKIT JANTUNG MENGGUNAKAN LEARNING VECTOR QUANTIZATION DAN PARTICLE SWARM OPTIMIZATION

NOVIYANTI, ANI and Rini, Dian Palupi and Rodiah, Desty (2022) KLASIFIKASI PENYAKIT JANTUNG MENGGUNAKAN LEARNING VECTOR QUANTIZATION DAN PARTICLE SWARM OPTIMIZATION. Undergraduate thesis, Sriwijaya University.

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Abstract

The Learning Vector Quantization method can be used to classify heart disease data. However, the accuracy produced by the Learning Vector Quantization method is still less than optimal because the resulting accuracy depends on the initialization of the model and input parameters. This method can be developed by optimizing the weight of the input parameters using Particle Swarm Optimization to get better classification results. This test was carried out using 270 heart disease dataset resulting in an average accuracy of 83.46%, precision 88.29%, recall 72.57% and f-measure 90.06% using the best Particle Swarm Optimization parameter, namely the number of iterations is 30, the number of particles is 20, the value of c1 is 2 and c2 is 1 and the comparison of training data and test data used is 70: 30. While the average accuracy before optimization using Particle Swarm Optimization is 74.81%, precision is 77, 73%, recall 63.43% and f-measure 79.29%. These results prove that there is an increase in the average accuracy of the Learning Vector Quantization method after being optimized using the Particle Swarm Optimization method. Keywords: Classification, Heart Disease, Particle Swarm Optimization, Learning Vector Quantization.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, Penyakit Jantung, Particle Swarm Optimization, Learning Vector Quantization
Subjects:	R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics T Technology > T Technology (General) > T10.5-11.9 Communication of technical information
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Ani Noviyanti
Date Deposited:	27 Jul 2022 06:29
Last Modified:	27 Jul 2022 06:29
URI:	http://repository.unsri.ac.id/id/eprint/74876

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