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Patria, Gaffy
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Computer Science & IT

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Optimasi Support Vector Mechine (SVM) Menggunakan K-Means dan K-Medoids untuk Klasterisasi Tema Tugas Akhir Patria, Gaffy
Journal Innovations Computer Science Vol. 1 No. 2 (2022): November 2022
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v1i2.72

Abstract

The large amount of final project document data from study programs at the Sekolah Tinggi Manajemen Informatika dan Komputer (STMIK) Abulyatama can make a major contribution to the difficulty of the process of grouping a student's final project theme. The clustering process that has been carried out manually so far has been very ineffective and inefficient, so a data mining application is needed to manage the data, especially for clustering the data. The goal to be achieved from writing this thesis is to implement the Support Vector Machine with K-Means and K-Medoids to optimize the final assignment clustering. the results of the Optimization Support Vector Machine (SVM) analysis using K-Means and K-Medoids for Grouping Student Final Project Themes can be concluded in a number of ways, namely; with the K-Means Clustering method it can be seen that there are 23 data mining, 10 networks, 26 artificial intelligence, and 21 websites, and website 11 items.
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