Sistem Rekomendasi Minat Akademik Mahasiswa Berbasis Machine Learning Menggunakan Metode K-Means dan Decision Tree

Meizapuspa Octakurnia Nurilawati

Abstract


Penelitian ini mengembangkan sistem rekomendasi peminatan akademik bagi mahasiswa Program Studi Sistem Informasi menggunakan pendekatan hybrid machine learning yang memadukan algoritma K-Means dan Decision Tree CART. Data primer dikumpulkan dari 83 responden melalui instrumen kuesioner yang mencakup 13 indikator perilaku belajar, motivasi, gaya belajar, pengalaman proyek TI, aktivitas freelance, keorganisasian, serta preferensi mata kuliah. Penelitian ini mengatasi permasalahan ketiadaan data berlabel melalui skema Weak Supervision dengan aturan heuristik berbobot untuk menghasilkan pseudo-label sebagai variabel target klasifikasi. Proses klasterisasi K-Means menghasilkan tiga profil karakteristik mahasiswa, yaitu Active Learner, Moderate Learner, dan Passive Learner. Model klasifikasi Decision Tree menunjukkan kinerja dengan akurasi 70,59% dan nilai weighted average F1-Score 0,71. Sistem direalisasikan dalam aplikasi web berbasis PHP, Bootstrap, MySQL, dan Flask. Pengujian fungsional membuktikan seluruh fitur beroperasi optimal dengan tingkat keberhasilan 100%. 


Keywords


Decision Tree; hybrid machine learning; K-Means; sistem rekomendasi; weak supervisiont

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