KOMPARASI ALGORITMA CLUSTERING DENGAN DATASET PENYEBARAN COVID-19 DI INDONESIA PERIODE MARET-MEI 2020 - Tamtelahitu, Trientje Marlein (Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta, 2020)
In data mining, there is a predictive model, namely predicting the value of different sample data sets, and testing into three types such as classification, regression and time series. While descriptive models allow us to determine patterns in sample data and divide them into groups, summaries and association rules. Report on the results of experiments on algorithms that are quite widely used in the field of machine learning. This experiment aims to measure performance on commonly used datasets in machine learning studies. The main performance factor to be compared in this experiment is the level of accuracy of the independent experiments on the dataset used. This research uses clustering algorithm method to compare various clustering algorithms using Weka Tools to find out which algorithm will be more convenient for users to do clustering algorithm using the Covid-10 distribution map dataset in Indonesia from March-May 2020. K-means taking the points closest to the center whereas Farthest-First picks the furthest points. Farthest-First can complete the clustering process but with a lower quality than K-Means. And other experiments, on the method of Making Based on Clusterd Density and EM (Expectation-Maximization) prove the same accuracy. The EM grouping method proves low (less than 50%) of the results comparing the Clusterd Based Densitity Making Method, with a percentage reaching 74%.