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All Journal KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer)
Suhada Suhada
STIKOM Tunas Bangsa,Pematangsiantar
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Pengklasteran Gaji Karyawan Pada Pt. Erba Primas Bogor Menggunakan Algoritma K-Medoids Theresia Siburian; Suhada Suhada; Ilham Syahputra Saragih; Irfan Sudahri Damanik; Dedi Suhendro
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 4, No 1 (2020): The Liberty of Thinking and Innovation
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v4i1.2852

Abstract

PT. Erba Primas is located in Bogor, West Java and is part of the steel production industry. Inside PT. Erba Primas Bogor, there are several sections, among others, namely: Accounting Section, Administration Section, Human Resource Department (HRD), Logistics Section, Production Section Penjualan Sales and Purchasing Parts. In this study the authors found problems at PT. Erba Primas is specifically the accounting section where in classifying salaries to employees. So the authors use Datamining with the K-Medoids algorithm to facilitate decision making. K-Medoids Clustering Algorithm or also known as Partitioning Around Mendoid (PAM) Algorithm is an algorithm that uses partitioning clustering method to group n sets of objects into a number of k clusters. This algorithm uses objects in a group of objects to represent clusters. In this study aims to determine the salary clusters at high and low levels. The data source used was taken from PT. Erba Primas Bogor in 1 year. The number of records used by 100 employees and divided into two clusters, namely high and low. Based on calculations using the k-medoids algorithm the results of a high cluster of 6 employees and a low cluster of 94 employees. This research can be used as input to the company in an effort to help regulate the salary funds budget for employees and besides that in classifying employee salary data is done more  more effectively and efficiently.
Co-Authors Dedi Suhendro Ilham Syahputra Saragih Irfan Sudahri Damani, Irfan Sudahri Theresia Siburian