PENGELOMPOKAN PENYAKIT KANKER SERVIKS BERDASARKAN GEJALA DENGAN MENGGUNAKAN FUZZY C-MEANS DAN PARTICLE SWARM OPTIMIZATION

JASMINE, NADYA PARAMESWARI and Rini, Dian Palupi and Oktadini, Nabila Rizky (2020) PENGELOMPOKAN PENYAKIT KANKER SERVIKS BERDASARKAN GEJALA DENGAN MENGGUNAKAN FUZZY C-MEANS DAN PARTICLE SWARM OPTIMIZATION. Undergraduate thesis, Sriwijaya University.

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Abstract

Penelitian ini dikembangkan untuk menghasilkan perangkat lunak untuk mengelompokan penyakit kanker serviks dengan metode Fuzzy C-Means dan Particle Swarm Optimization. Pada Fuzzy C-Means melakukan pembangkitan nilai random, menghitung pusat cluster, menghitung fungsi objektif, dan menghitung perubahan matriks partisi. Pada Particle Swarm Optimization melakukan inisialisasi partikel, menentukan nilai constanta pbest dan gbest, membangkitkan nilai kecepatan awal pbest dan gbest nilai random. Pengujian dilakukan dengan cara melihat perubahan jumlah cluster, jumlah partikel, jumlah iterasi. Pengukuran validasi cluster dilakukan dengan menggunakan Davies Bouldin Index. Hasil dari pengukuran untuk Pengelompokan penyakit kanker serviks dengan menggunakan Fuzzy C-Means-Particle Swarm Optimization didapatkan nilai yaitu 0.0018244269263245 dan untuk metode Fuzzy C-Means didapatkan nilai yaitu 0.007945219909104092, dengan pengujian nilai jumlah partikel = 3, nilai cluster = 6, dan jumlah iterasi = 500.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Kanker Serviks, Fuzzy C-Means, Particle Swarm Optimization, Davies Bouldin Index (DBI)
Subjects: H Social Sciences > HG Finance > HG1709 Data processing
Q Science > QA Mathematics > QA299.6-433 Analysis > Q337.3 Swarm intelligence. Big data -- Social aspects. Information technology -- Economic aspects.
Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics.
Divisions: 09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User: Users 9151 not found.
Date Deposited: 29 Dec 2020 04:51
Last Modified: 29 Dec 2020 04:51
URI: http://repository.unsri.ac.id/id/eprint/39103

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