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Journal Bulletin of Information Technology (BIT)Bulletin of Information Technology (BIT) Abstract

This study aims to identify learning groups based on student academic data at SMP Muhammadiyah 5 Samarinda. The data used includes exact and non-exact subject scores, exam results, assignment scores, attendance, and parents educational backgrounds. The stages of the research include data collection, data preprocessing through cleaning, feature engineering, and transformation, data processing to determine the optimization values of the DBSCAN parameters, namely eps and minpts, and evaluation of the results using the Silhouette Score. The optimal parameters obtained were eps = 1.3 and min_samples = 3, resulting in three main clusters and some noise. The analysis results showed three main clusters, namely cluster 0 with 89 students (medium achievement), cluster 1 with 50 students (high achievement), and cluster 2 with 5 students (low achievement), as well as 14 students identified as noise. A Silhouette Score value of 0.217 indicates relatively weak cluster separation quality, but DBSCAN is able to detect noise that may not be detected by other algorithms. These findings indicate that even though the quality of the clusters is not yet optimal, the algorithm used is still useful for exploring student learning patterns and can serve as the basis for more targeted learning interventions.

Conclusion

This study applied the DBSCAN algorithm to cluster the academic data of students at Muhammadiyah 5 Junior High School in Samarinda, resulting in three main groups and some students identified as noise.Although the Silhouette Score indicated relatively weak cluster separation, the findings provide an initial overview of variations in student learning patterns.These results can serve as a basis for schools to implement more targeted learning interventions.

Future Research

Penelitian lebih lanjut dapat dilakukan dengan mengintegrasikan data non-akademik seperti motivasi belajar, dukungan keluarga, dan partisipasi dalam kegiatan ekstrakurikuler untuk meningkatkan akurasi pengelompokan siswa dan memberikan pemahaman yang lebih komprehensif tentang pola belajar. Selain itu, eksplorasi algoritma clustering lain seperti hierarchical clustering atau Gaussian Mixture Models (GMM) dapat dibandingkan dengan DBSCAN untuk mengidentifikasi metode yang paling efektif dalam mengelompokkan data akademik siswa di tingkat SMP. Terakhir, penelitian dapat difokuskan pada pengembangan sistem rekomendasi pembelajaran personalisasi berdasarkan hasil pengelompokan, yang dapat memberikan saran materi pembelajaran atau strategi belajar yang disesuaikan dengan kebutuhan masing-masing kelompok siswa, sehingga meningkatkan efektivitas proses pembelajaran secara keseluruhan. Penelitian-penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam pengembangan strategi pembelajaran yang lebih adaptif dan berpusat pada siswa.

References

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