LOMOSUGANDI PARDOSI, NPM 2209500182 (2026) ANALISIS PEMBAGIAN KELOMPOK PRAKTEK BELAJAR SISWA SMK NEGERI 2 KUALUH SELATAN BERDASARKAN MINAT DAN NILAI DENGAN METODE K MEANS. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Pembentukan kelompok praktik belajar di SMK Negeri 2 Kualuh Selatan masih dilakukan secara konvensional berdasarkan pertimbangan subjektif sehingga belum memperhatikan kesamaan karakteristik siswa berdasarkan minat belajar dan nilai akademik. Kondisi tersebut menyebabkan pembagian kelompok kurang efektif dalam mendukung proses pembelajaran praktik. Penelitian ini bertujuan untuk menerapkan algoritma K-Means dalam pembagian kelompok praktik belajar siswa berdasarkan minat belajar dan nilai akademik, mengetahui hasil pengelompokan yang terbentuk, serta mendeskripsikan karakteristik setiap kelompok. Penelitian ini menggunakan metode data mining dengan algoritma K-Means Clustering. Data penelitian berupa data primer yang diperoleh melalui kuesioner minat belajar dan data sekunder berupa nilai akademik 27 siswa kelas XI TKJ 1 SMK Negeri 2 Kualuh Selatan Tahun Ajaran 2025/2026. Tahapan penelitian meliputi preprocessing data, normalisasi menggunakan Min-Max Normalization, perhitungan jarak dengan Euclidean Distance, proses clustering hingga konvergen, implementasi menggunakan RapidMiner, dan evaluasi hasil clustering. Hasil penelitian menunjukkan bahwa algoritma K-Means berhasil mengelompokkan siswa ke dalam tiga cluster, yaitu kelompok dengan minat dan nilai tinggi, kelompok dengan minat dan nilai sedang, serta kelompok dengan minat dan nilai rendah. Hasil implementasi RapidMiner menunjukkan pengelompokan yang sama dengan perhitungan manual sehingga membuktikan bahwa metode K-Means mampu menghasilkan pengelompokan yang konsisten dan objektif. Dengan demikian, algoritma K-Means dapat digunakan sebagai alternatif dalam mendukung pembagian kelompok praktik belajar yang lebih efektif, seimbang, dan sesuai dengan karakteristik siswa. Kata kunci: K-Means, Clustering, Minat Belajar, Nilai Akademik, RapidMiner =============================================================================== The formation of practical learning groups at SMK Negeri 2 Kualuh Selatan has traditionally been carried out using conventional and subjective approaches without considering students' learning interest and academic achievement. As a result, the grouping process has been less effective in supporting practical learning activities. This study aims to implement the K-Means algorithm in grouping students for practical learning based on their learning interest and academic achievement, identify the resulting clusters, and describe the characteristics of each group. This research employed a data mining approach using the K-Means Clustering algorithm. The data consisted of primary data collected through learning interest questionnaires and secondary data in the form of academic scores of 27 students from Class XI TKJ 1 at SMK Negeri 2 Kualuh Selatan in the 2025/2026 academic year. The research stages included data preprocessing, Min Max Normalization, distance calculation using Euclidean Distance, clustering until convergence, implementation using RapidMiner, and clustering evaluation. The results showed that the K-Means algorithm successfully classified students into three clusters: students with high learning interest and academic achievement, students with moderate learning interest and academic achievement, and students with low learning interest and academic achievement. The RapidMiner implementation produced the same clustering results as the manual calculation, indicating that the K-Means algorithm provides consistent and objective grouping. Therefore, the K-Means algorithm can be used as an alternative approach to support a more effective, balanced, and objective practical learning group formation based on students' characteristics. Keywords: K-Means, Clustering, Learning Interest, Academic Achievement, RapidMiner
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | K-Means, Clustering, Minat Belajar, Nilai Akademik, RapidMiner===============K-Means, Clustering, Learning Interest, Academic Achievement, RapidMiner |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 13 Aug 2026 03:53 |
| Last Modified: | 13 Aug 2026 03:53 |
| URI: | http://repository.ulb.ac.id/id/eprint/2661 |
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