Clustering Data Remunerasi PNS Menggunakan Metode K-Means Clustering Dan Local Outlier Factor
Abstract
Remuneration is a work benefit in the form of salary, honorarium, fixed allowances, incentives, bonuses for achievement, severance pay, and / pension funds. Remuneration will eliminate the assumption that there is no positive correlation between performance and income. This means that employees / employees who are performing well will have unequal income. The government has tried to change the payroll system to be better through the remuneration system. Remuneration consists of basic salary plus benefits derived from pure rupiah and other benefits originating from Non-Tax State Revenues. Problem with remuneration is that the validation carried out by the direct supervisor of the employee concerned is still doubtful. Based on this, we need a system that can detect outlier data from civil servant remuneration data and classify the data using data mining techniques. This research aims to classify data on civil servant remuneration using the k-means clustering method with improvisation at the pre-processing stage and determining the optimum number of clusters. Local Outlier Factor method with a MinPts value of 150 can detect the most outlier data with 162 data outliers detected or 22,98%. Number of optimum clusters by the elbow method is 4 clusters with a Silhoutte value of 0,542 and Dunn of 0,040.
Keywords — Clustering, K-Means, LOF, Outlier
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References
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This work is licensed under a Creative Commons Attribution 4.0 International License
