Studi Komparasi antara Metode k-Means, k-Means++, dan k-Medoids untuk Klasterisasi Tingkat Kesejahteraan Penduduk (Studi Kasus Dinas PPKB dan P3A Kab. Wonogiri)

PRASDIKA FBS, NIM. 18206050013 (2020) Studi Komparasi antara Metode k-Means, k-Means++, dan k-Medoids untuk Klasterisasi Tingkat Kesejahteraan Penduduk (Studi Kasus Dinas PPKB dan P3A Kab. Wonogiri). Masters thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Along the rapid growth of data growth in the real world and with the data that have little or no background, clustering has an important role in the exploration of the data to determine patterns and benefit from a large number of datasets. This study did clustering with some of unsupervised learning algorithms are k-Means, k-Means++ and k-Medoids. The data used is data from official population survey results PPKB and P3A Wonogiri 2018. In this study, a feature extraction stage was carried out using the principal component analysis (PCA) method and data pre-processing using a standard scaler and a min-max scaler. With the implementation of the three algorithms clustering it showed that the algorithm k-means ++ has an average value of inertia smallest with 15.954711235353362 on data from standardization to the standard scaler and 1.13967411934335812 on outcome data standardization with the min-max scaler, while k-Medoids produce an average time 0.0145999434 seconds with the fastest processing on the data standardization results with standard scaler and 0.0142000198 seconds on the result data standardization with the min-max scaler

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