Clustering Analysis of Results of Students' Industrial Work Practice Activities Using the K-Means Method

Yulian Ansori; Masud Hermansyah; M. Faiz Firdausi; Abdul  Wahid; Iqbal Sabilirrasyad

doi:10.31967/inside.v2i1.1136

Yulian Ansori Universitas Primagraha
Masud Hermansyah Institut Teknologi dan Sains Mandala
M. Faiz Firdausi Institut Teknologi dan Sains Mandala
Abdul Wahid Institut Teknologi dan Sains Mandala
Iqbal Sabilirrasyad Institut Teknologi dan Sains Mandala

DOI: https://doi.org/10.31967/inside.v2i1.1136

Keywords: Clustering, K-Means, Davies Bouldin Index

Abstract

Praktik Kerja Industri (PRAKERIN) is an important program in the Vocational High School curriculum that provides students with real work experience before they enter the industrial world. Apart from technical skills, this program also develops soft skills such as teamwork, communication, and problem solving. However, evaluating PRAKERIN results often faces challenges in identifying and classifying student performance objectively and systematically. With data mining technology, it is possible to analyze the results of students' abilities after participating in PRAKERIN activities. In conducting this research, the K-Means clustering method was used to group students' competency abilities. With the K-Means clustering technique, it is hoped that teachers can adjust the learning model according to students' abilities. Based on the grouping results, it was found that grouping with 3 clusters was the most optimal grouping result with the smallest Davies Bouldin Index (DBI) value, namely 0.160. The application of the K-Means method in grouping student data based on English language ability scores can produce 3 groups of students who are competent, quite competent, and less competent.