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All Journal International Journal of Science and Environment
Alisa Nuraini
Department of Informatics Engineering, STMIK Widya Cipta Dharma, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Mathematics Physics

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Laboratory Assistant Performance Analysis At STMIK Widya Cipta Dharma Using K-Means Alisa Nuraini; Heny Pratiwi; Ahmad Fajri
International Journal of Science and Environment (IJSE) Vol. 6 No. 4 (2026): November 2026
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v6i4.817

Abstract

Laboratory assistants play a crucial role in supporting computer science practicum learning; however, conventional evaluation methods based on agregate average scores are unable to identify hidden performance patterns and characteristics. This study aims to analyze the patterns and characteristics of laboratory assistant performance at STMIK Widya Cipta Dharma using an Educational Data Mining approach based on the K-Means clustering algorithm. This study employed a quantitative research method using questionnaire data collected from 178 student respo ndents with ten performance indicators measured using a five-point Likert scale. The analysis process consisted of several stages, including data collection, data preprocessing through data cleaning and attribute selection, instrument testing using Pearson Product Moment validity and Cronbach’s Alpha reliability analysis, Z-Score normalization to standardize indicator values, K-Means clustering using Euclidean distance calculations, evaluation of optimal cluster numbers using the Elbow Method, Silhouette Score, and Davies-Bouldin Index (DBI), and Principal Component Analysis (PCA) visualization for cluster interpretation. The results showed that the instrument was valid and reliable, and the K-Means algorithm produced three performance clusters consisting of Low Performance (20.8%), Medium Performance (55.1%), and High Performance (24.1%) groups.
Co-Authors Ahmad Fajri Heny Pratiwi