ANALISIS PEMBERIAN INSENTIF TENAGA MEDIS MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING - Dwi Cahya Prana Ginting; Jonggi Samuel Parluhutan Sihombing; Nia Natalia Aritonang; Ribka Patricia Sinaga; Winda Nia Purba (Politeknik Bisnis Indonesia, 2023)
Intensive funds are very important for health workers in caring for Covid-19 patients. Researchers conducted research using a dataset from a list of names of health workers at the puskesmas who were proposed to get intensive handling of Covid-19 in the city of Medan. One of the stages for preprocessing the data set is carried out using the application of the linear regression method. The researcher uses several k means clustering algorithms so that from this process the results can be obtained for anyone who deserves intensive handling of the Covid-19 pandemic. The algorithms used include Decision Tree C4.5, K-Nearest Neighbor, Naive Bayes, C4.5 Algorithm, K-Means clustering, Online Analytical Processing. The researcher conducted a test using a data mining tool, namely with RapidMiner version 9.0 using the K-means Clustering Algorithm method, data results from RapidMiner that have been connected to the K-Means Clustering method and obtained predictive results from data obtained from health workers 2019-2022. In this study using a dataset from a list of names of health workers at the puskesmas who were proposed to get incentives for handling the Covid-19 disease pandemic in Medan City. The data was obtained from the results of the list of names of health workers at the puskesmas from 2019-2022. The dataset preprocessing stage is carried out using the application of the Linear Regression Method. Based on the results of Cluster officers, the total number of data is 279, there are 5 clusters, which consist of Cluster 0, Cluster 1, Cluster 2, Cluster 3 results. There are 6 officers who get incentives of Rp. 3,000,000, 44 officers get incentives of Rp. 4,000,000 and 229 officers who received Rp. 5,000,000. The results of this analysis obtained Cluster 0: 93 items, Cluster 1: 83 items, Cluster 2: 91 items, Cluster 3: 2 items, Cluster 4: 10 items and a total number of times 279.