Abstract
This study consists of an attempt to optimize the K-Means Clustering Algorithm and calculating the Full Time Equivalent (FTE) of each cluster based on intern's daily work log data. The optimization will be done by using some of K-Means Clustering’s validation method to estimate the best K clusters of the data. The validation methods that will be used to optimize the algorithm are Elbow Criterion Method and Silhouette Score Index. The initial k cluster will be formed and evaluated using Davies Bouldin Index analysis. The divided clusters are supposed to be classified by the rate of complexity of each project. The calculated FTE will be used to estimate the workload for the current workforce. This estimation is hoped to help companies decide in their hiring decision.
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