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Penerapan K-Means Clustering Kesiapan Siswa SMA Dalam Pemilihan Jurusan Kuliah Rara Revina; Fauriatun Helmiah; Abdul Karim Syahputra
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3323

Abstract

Choosing a college major is an important decision for high school students because it affects their future academic success and career. However, 12th-grade students at Kisaran Regional High School still struggle to determine a major that aligns with their interests and abilities. This study employs a quantitative approach using data mining methods via the K-Means Clustering algorithm to categorize students’ readiness levels in selecting a college major. Research data were obtained from questionnaires completed by 71 students based on five assessment aspects: interest in the major, alignment with academic performance, career information, environmental support, and personal maturity. The research instrument used a 1–5 Likert scale and was tested for reliability using Cronbach’s Alpha, which yielded a value of 0.87, indicating a good level of consistency. The research stages included data selection, transformation of qualitative data into numerical form, determination of the number of clusters (k=3), random initialization of the initial centroids, and calculation of distances using Euclidean Distance, performed iteratively until convergence was achieved. The results of the study indicate that out of 71 students, 23 students (32.39%) fall into the “very ready” category, 28 students (39.44%) into the “fairly ready” category, and 20 students (28.17%) into the “not yet ready” category regarding college major selection.
Co-Authors Abdul Karim Syahputra Fauriatun Helmiah