Peningkatan Minat Digital Skill Menggunakan Algoritma K-Medoids Clustering Pada Karyawan
- Rizka Zulfiana Universitas Buana Perjuangan Karawang, Karawang, Indonesia
-
Shofa Shofiah Hilabi
*
Universitas Buana Perjuangan Karawang, Karawang,
Indonesia
- Fitria Nurapriani Universitas Buana Perjuangan Karawang, Karawang, Indonesia
- Baenil Huda Universitas Buana Perjuangan Karawang, Karawang, Indonesia (*) Corresponding Author
Abstract
General Company for Printing Money of the Republic of Indonesia is one of the state-owned enterprises that prints banknotes and other official documents. Perum Peruri also wishes to gain more insight into the technology implemented in the company. The demand for a workforce skilled in the use of technology in the work environment continues to increase over time. Perum Peruri has 16 Digital Skill categories, each of these categories has a high, medium to the lowest interest. In this problem, the data taken has not been grouped, so there is a lack of information about the number of categories that have the highest to lowest interest. By analyzing the specialization data, it will help determine which categories need improvement. The categories in Digital Skills specialization can then be improved by using this information as a reference for designing improvement strategies. Research was conducted using clustering to determine the number of categories that Perum Peruri personnel are interested in. In this study, sales data in Excel format was analyzed, and clusters based on product sales data were created using the K-Medoids approach. Sales information obtained from secondary data that manages employee specialization. Using RapidMiner, the accuracy for the three clusters designated as highest, middle, and lowest based on the clustering results was ascertained. The first cluster of 16 items analyzed consisted of 7 items with the highest ranking, the second cluster had 5 items categorized as medium, and the third cluster had 4 items classified as the lowest. Based on the results, 4 items were categorized as low, indicating the need for a socialization approach to increase interest in the Digital Skill.
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