Laboratory Assistant Performance Analysis At STMIK Widya Cipta Dharma Using K-Means
Downloads
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.
References
[1] M. Gagolewski, M. Bartoszuk, and A. Cena, “Are cluster validity measures (in)valid?,” Inf. Sci. (Ny)., vol. 581, pp. 620–636, 2021.
[2] I. Arfyanti, “Implementasi Data Mining dengan Menerapkan Algoritma K-Means Clustering untuk Memberikan Rekomendasi Jurusan Kuliah Bagi Mahasiswa Baru,” J. Media Inform. Budidarma, 2024.
[3] M. F. Azmi, “K-Means Clustering with Elbow Method and Validity Indices for Classifying Student Academic Achievement,” J. Tek. Inform., 2026.
[4] S. P. Rangkuti and M. Fakhriza, “Implementasi Algoritma K-Means Clustering untuk Penilaian Kinerja Anggota Organisasi,” 2025.
[5] I. Darmayanti, “Improving K-Means Clustering Accuracy for Academic Success Investigation with Extreme Gradient Boosting Algorithm,” 2024.
[6] E. Kartikadarma, “Educational Data Mining to Improve Decision Support on the Ratio of Students and Study Groups in Indonesia Using K-Means Method,” Elem. Educ. Online, 2021.
[7] Y. Elda, “Klasterisasi Penempatan Siswa yang Optimal untuk Meningkatkan Nilai Rata-Rata Kelas Menggunakan K-Means,” J. Inf. dan Teknol., 2021.
[8] N. Yati, “Performance Analysis of The K-Means Algorithm on Intensity of Science Laboratory Use and Science Skills,” 2024.
[9] N. Sitepu, “K-Means Clustering of Student Mid-Term and Final Exam Data,” J. Komput. Indones., 2024.
[10] A. Setiawan, “Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa,” J. Comput. Syst. Informatics, 2024.
[11] A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,” Inf. Sci. (Ny)., vol. 622, pp. 178–210, 2023.
[12] C. Romero and S. Ventura, “Educational data mining and learning analytics: An updated survey,” WIREs Data Min. Knowl. Discov., 2024.
[13] E. Schubert, “Stop using the elbow criterion for k-means and how to choose the number of clusters instead,” ACM SIGKDD Explor. Newsl., 2023.
[14] B. Albreiki, N. Zaki, and H. Alashwal, “A systematic literature review of student performance prediction using machine learning techniques,” Educ. Sci., 2021.
[15] I. T. Utami, “K-Means cluster count optimization with Silhouette Index validation and Davies Bouldin Index,” BAREKENG, 2023.
[16] A. R. Almasri, “Educational data mining for predicting student performance: A systematic review,” Educ. Inf. Technol., 2022.
[17] M. Auva, “Klasifikasi Kepuasan Mahasiswa Terhadap Evaluasi Kinerja Asisten Laboratorium di STMIK Kaputama Menggunakan Metode K-Means,” PRINSIP, 2025.
[18] M. Wu, “K-Means Clustering-Based Feature Generation for Student Performance Prediction,” ICCK Trans. Educ. Data Min., 2026.
[19] R. Liu, “Data Analysis of Educational Evaluation Using K-Means Clustering Method,” Comput. Intell. Neurosci., 2022.
[20] B. Harpad, M. Fahmi, P. Pahrudin, and R. Andrea, “Pengelompokan Siswa Layak Penerima Beasiswa dengan Menerapkan Algoritma K-Means Clustering Data Mining,” 2024.
License
Copyright (c) 2026 Alisa Nuraini, Heny Pratiwi, Ahmad Fajri
This work is licensed under a Creative Commons Attribution 4.0 International License.
Downloads
How to Cite
