Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Inventa: Journal of Science, Technology, and Innovation
Farida Nafisa Siagian
Unknown Affiliation
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Perbandingan Kinerja K-Means dan K-Medoids dalam Klasifikasi Siswa Berprestasi di SMP Muhammadiyah 60 Medan Farida Nafisa Siagian; Halim Maulana
Journal of Science, Technology, and Innovation Vol 1 No 3 (2026): : April: Inventa: Journal of Science, Technology, and Innovation
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/tay7fq21

Abstract

This study investigates the comparative performance of the K-Means and K-Medoids clustering algorithms in classifying student achievement at SMP Muhammadiyah 60 Medan. The research applies a quantitative data mining approach using academic and non-academic variables, including Mathematics scores, Science scores, Bahasa Indonesia scores, attendance records, and extracurricular participation. Data preprocessing was conducted through cleaning, normalization using Min-Max Scaling, and feature selection to ensure data consistency and analytical reliability. The findings indicate that both algorithms successfully classified students into meaningful performance groups with consistent clustering structures. K-Means demonstrated superior computational efficiency and lower SSE values, making it suitable for homogeneous datasets. In contrast, K-Medoids exhibited greater robustness against outliers and produced more stable cluster distributions. The study concludes that K-Medoids provides more representative clustering results for educational datasets characterized by heterogeneous performance patterns.
Co-Authors Halim Maulana