OPTIMASI METODE K-NEAREST NEIGHBOR (KNN) MENGGUNAKAN PARTICLE SWARM OPTIMIZATION (PSO) UNTUK DIAGNOSIS PENYAKIT HATI

ZADLYKA, TARA and Samsuryadi, Samsuryadi and Buchari, Muhammad Ali (2021) OPTIMASI METODE K-NEAREST NEIGHBOR (KNN) MENGGUNAKAN PARTICLE SWARM OPTIMIZATION (PSO) UNTUK DIAGNOSIS PENYAKIT HATI. Undergraduate thesis, Sriwijaya University.

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Abstract

K-Nearest Neighbor (KNN) merupakan metode klasifikasi yang banyak digunakan dan cukup efektif dalam melakukan klasifikasi. Dalam klasifikasi KNN, penentuan nilai k yang kurang tepat dapat menurunkan kinerja KNN. Untuk mengatasi permasalahan tersebut, akan digunakan Particle Swarm Optimization (PSO) untuk menentukan nilai k yang optimal. Data yang digunakan pada penelitian ini adalah ILPD (Indian Liver Patient Dataset) yang diperoleh dari UCI Machine Learning Repository. Penelitian ini menggunakan metode KNN dengan nilai k yang optimal untuk diagnosis penyakit hati. Berdasarkan analisis hasil penelitan, dapat disimpulkan bahwa penggunaan PSO dalam mencari nilai k optimal pada metode KNN mampu meningkatkan akurasi klasifikasi. Nilai parameter PSO optimal yang didapat dari hasil penelitian adalah jumlah partikel = 30, jumlah iterasi = 100, dan bobot inersia = 0,4. Nilai akurasi yang didapat pada klasifikasi KNN-PSO adalah 71,69% dan peningkatan akurasi terhadap metode KNN sebesar 2,9%.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Diagnosis Penyakit Hati, K-Nearest Neighbor, Particle Swarm Optimization
Subjects: Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data.
T Technology > T Technology (General) > T1-995 Technology (General) > T14 Philosophy. Theory. Classification. Methodology Cf. CB478 Technology and civilization
Divisions: 09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User: Tara Zadlyka
Date Deposited: 21 May 2021 03:20
Last Modified: 21 May 2021 03:20
URI: http://repository.unsri.ac.id/id/eprint/46563

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