Clustering Student Competencies Using the K-Means Algorithm
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| Název: | Clustering Student Competencies Using the K-Means Algorithm |
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| Autoři: | Ratih Friska Dwi Andini, Febri Liantoni, Aris Budianto |
| Zdroj: | Ultimatics : Jurnal Teknik Informatika. 17:99-106 |
| Informace o vydavateli: | Universitas Multimedia Nusantara, 2025. |
| Rok vydání: | 2025 |
| Popis: | This study aims to evaluate the effectiveness of the K-Means algorithm in clustering student competencies. The subject of the study is students of the Informatics and Computer Engineering Education study program at a public university in Indonesia, with course score data representing various areas of competence as features. The K-Means algorithm is used to group student data into several clusters based on academic grade patterns. The results show that the K-Means algorithm is quite effective in identifying the initial pattern of student competence, with a Silhouette Score of 0.3489, which falls into the medium category. This study concludes that the use of the K-Means algorithm alone is sufficient to support the analysis of student areas of competence, with potential applications as a recommendation system for students in choosing elective courses and as an evaluation tool for study programs to identify areas of competence that need improvement. |
| Druh dokumentu: | Article |
| ISSN: | 2581-186X 2085-4552 |
| DOI: | 10.31937/ti.v17i1.4071 |
| Rights: | CC BY SA |
| Přístupové číslo: | edsair.doi...........3a79061e08496480f8c78fa0cef54cc5 |
| Databáze: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | This study aims to evaluate the effectiveness of the K-Means algorithm in clustering student competencies. The subject of the study is students of the Informatics and Computer Engineering Education study program at a public university in Indonesia, with course score data representing various areas of competence as features. The K-Means algorithm is used to group student data into several clusters based on academic grade patterns. The results show that the K-Means algorithm is quite effective in identifying the initial pattern of student competence, with a Silhouette Score of 0.3489, which falls into the medium category. This study concludes that the use of the K-Means algorithm alone is sufficient to support the analysis of student areas of competence, with potential applications as a recommendation system for students in choosing elective courses and as an evaluation tool for study programs to identify areas of competence that need improvement. |
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| ISSN: | 2581186X 20854552 |
| DOI: | 10.31937/ti.v17i1.4071 |