OPTIMASI PARAMETER LVQ MENGGUNAKAN ALGORITMA PSO UNTUK KLASIFIKASI PENYAKIT DIABETES

MARSHELINE, NADIA WISYA and Rini, Dian Palupi and Alfarissi, Alfarissi (2023) OPTIMASI PARAMETER LVQ MENGGUNAKAN ALGORITMA PSO UNTUK KLASIFIKASI PENYAKIT DIABETES. Undergraduate thesis, Sriwijaya University.

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Abstract

Classification of diabetes data can be implemented using several different methods, one of which is Learning Vector Quantization. Nevertheless, the output obtained from this method could not always achieve the optimal results, because Learning Vector Quantization classification process relies on the weight values being used for the system. Therefore, modification was made to further enhance Learning Vector Quantization method in the case of diabetes classification, by utilizing the Particle Swarm Optimization algorithm. This research was conducted using as many as 768 diabetes data from a public resource. The testing of Learning Vector Quantization method without Particle Swarm Optimization managed to yield the average accuracy, precision, recall and f-measure respectively as follows: 74,14%, 61,85%, 61,76%, and 66,31%. However, diabetes data classification using Learning Vector Quantization which was later optimized using the Particle Swarm Optimization algorithm was able to yield the average accuracy, precision, recall and f-measure as much as 78,22%, 73,17%, 67,31%, and 74,63% respectively. The results that have been obtained prove that Particle Swarm Optimization algorithm is capable of providing an increase in accuracy for classification system based on Learning Vector Quantization.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Learning Vector Quantization, Particle Swarm Optimization, Klasifikasi, Penyakit Diabetes
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:	Nadia Wisya Marsheline
Date Deposited:	20 Jul 2023 08:04
Last Modified:	20 Jul 2023 08:04
URI:	http://repository.unsri.ac.id/id/eprint/118453

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