Komparasi Kinerja Algoritma Random Forest, Decision Tree, Naïve Bayes, dan KNN dalam Prediksi Tingkat Depresi Mahasiswa menggunakan Student Depression Dataset
DOI:
https://doi.org/10.35960/ikomti.v6i1.1756Keywords:
depresi mahasiswa, algoritma klasifikasi, Random Forest, Decision Tree, Naïve Bayes, K-Nearest NeighborsAbstract
Kesehatan mental, khususnya depresi pada mahasiswa, menjadi isu penting yang membutuhkan perhatian serius. Penelitian ini bertujuan untuk membandingkan kinerja beberapa algoritma klasifikasi dalam memprediksi tingkat depresi mahasiswa menggunakan Student Depression Dataset (SDD). Algoritma yang dianalisis meliputi Random Forest, Decision Tree, Naïve Bayes, dan K-Nearest Neighbors (KNN). Metode yang digunakan mencakup evaluasi performa model berdasarkan metrik akurasi, Precision, Recall, dan F1-score. Hasil penelitian menunjukkan bahwa Random Forest memiliki performa terbaik dalam menyeimbangkan antara Precision dan Recall, sementara Naïve Bayes lebih cocok untuk aplikasi yang memprioritaskan Precision. Temuan ini memberikan wawasan baru dalam penerapan algoritma klasifikasi untuk data kesehatan mental dan menjadi dasar rekomendasi bagi institusi pendidikan dalam menyusun kebijakan yang lebih efektif untuk mendukung kesehatan mental mahasiswa.
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