Deteksi Pola Ketidakaktifan Akademik Mahasiswa pada Mata Kuliah Pembelajaran Mesin Menggunakan DBSCAN

Mohamad Ardi; Endyk Noviyantono; Eviana Tjatur Putri

doi:10.54914/jit.v12i2.3074

Penulis

Mohamad Ardi STMIK PPKIA Tarakanita Rahmawati
Endyk Noviyantono STMIK PPKIA Tarakanita Rahmawati
Eviana Tjatur Putri STMIK PPKIA Tarakanita Rahmawati

DOI:

https://doi.org/10.54914/jit.v12i2.3074

Kata Kunci:

DBSCAN, Deteksi Anomali, Educational Data Mining, Mahasiswa Berisiko, Unsupervised Learning

Abstrak

Dosen sering baru mengenali mahasiswa yang berisiko memperoleh nilai E setelah ujian akhir. Penelitian ini mengeksplorasi apakah Density-Based Spatial Clustering of Applications with Noise (DBSCAN) dapat mengidentifikasi pola ketidakaktifan akademik yang berasosiasi dengan nilai E sebelum ujian akhir. Data terdiri atas 142 rekaman nilai mata kuliah Pembelajaran Mesin dari dua semester ganjil tahun akademik 2024/2025 dan 2025/2026, yang dianonimkan dan dideskripsikan oleh lima fitur pra-UAS: partisipasi, proyek, tugas, kuis, dan UTS. Pada data ini seluruh nilai E bertepatan dengan UAS bernilai nol, sehingga "berisiko" berarti tidak menyelesaikan ujian akhir. Parameter DBSCAN dipilih dengan indeks validitas internal tanpa memakai nilai akhir. DBSCAN membentuk dua klaster dan 17 rekaman noise. Sebanyak 25 rekaman di luar klaster utama mencakup 21 dari 28 rekaman bernilai E (precision 0,84, recall 0,75, F1-score 0,79, MCC 0,75) pada evaluasi retrospektif. Isolation Forest menghasilkan kinerja identik, dan validasi lintas semester menunjukkan generalisasi yang terbatas (F1-score 0,39–0,40). DBSCAN karenanya merupakan pendekatan eksploratif yang mudah diinterpretasi untuk mengidentifikasi pola ketidakaktifan, bukan sistem peringatan dini yang telah tervalidasi. Penelitian lanjutan perlu menggunakan validasi prospektif serta data kehadiran atau log pembelajaran.

Unduhan

Data unduhan belum tersedia.

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