Analisis Pengelompokan Tingkat Pemahaman Materi Siswa Berdasarkan Nilai Ujian Menggunakan Algoritma K-Means

Authors

  • Cahaya Muzaddidah Universitas Teknologi Yogyakarta
  • Arief Hermawan Universitas Teknologi Yogyakarta
  • Donny Avianto Universitas Teknologi Yogyakarta

DOI:

https://doi.org/10.70340/jirsi.v5i2.353

Keywords:

Sentiment analysis, Data mining, K-Means, Clustering, Student Understanding, Google Colab, Value Analysis.

Abstract

Student understanding of learning content is an important indicator of educational success. This study aims to group student understanding based on their exam results in mathematics, English, science, social studies, and Arabic using the K-means Clustering algorithm. The data used consisted of 60 rows of student performance data, which were processed and standardized using Google Colab. The number of clusters was limited to two groups (K=2) to categorize students as having a high level of understanding or a basic level of understanding. The results showed that the K-means algorithm successfully identified groups of students with different levels of understanding based on their average exam scores. The group with a high level of understanding achieved an average score of more than 87.4. Teachers can use these Clustering results as a basis for developing more individualized and effective learning strategies for each group of students.

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Author Biography

Cahaya Muzaddidah, Universitas Teknologi Yogyakarta

Cahaya Muzaddidah is a Master's student in Information Systems at Universitas Teknologi Yogyakarta. Her research interests include IT Governance (COBIT 2019),Machine Learning, and Decision Support Systems. She is actively involved in academic research focusing on data-driven decision-making and AI policy.

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Published

2026-05-30

Issue

Vol. 5 No. 2 (2025): Mei 2026

Section

Articles

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Copyright (c) 2026 Cahaya Muzaddidah, Arief Hermawan, Donny Avianto

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