Pendeteksian Kesehatan Mental Mahasiswa Berdasarkan Aktivitas Digital Menggunakan Algoritma Machine Learning
DOI:
https://doi.org/10.32493/joaiia.v7i3.62496Keywords:
Support Vector Machine, DASS-21, Activities Digital, Machine Learning, Mental Health, Aktivitas Digital, Kesehatan MentalAbstract
Perkembangan teknologi digital menyebabkan meningkatnya intensitas penggunaan perangkat digital pada mahasiswa yang berpotensi memengaruhi kondisi kesehatan mental. Penelitian ini bertujuan mengimplementasikan algoritma Support Vector Machine (SVM) untuk mengklasifikasikan tingkat depresi, kecemasan, dan stres berdasarkan integrasi data aktivitas digital dan skor DASS-21, serta merancang sistem skrining kesehatan mental berbasis website. Penelitian menggunakan data dari 151 responden yang diperoleh melalui kuesioner aktivitas digital dan DASS-21. Tahapan penelitian meliputi preprocessing data, pembentukan label DASS-21, pelatihan model menggunakan Stratified 5-Fold Cross Validation, optimasi parameter dengan GridSearchCV, serta implementasi sistem menggunakan framework Flask. Hasil pengujian menunjukkan bahwa model terbaik menggunakan Kernel Radial Basis Function (RBF) pada klasifikasi depresi dengan akurasi 78,77%, Kernel Linear pada klasifikasi kecemasan dengan akurasi 80,13%, dan Kernel RBF pada klasifikasi stres dengan akurasi 84,82%. Pengujian Black Box dan White Box menunjukkan seluruh fungsi sistem berjalan sesuai rancangan. Pengujian User Response memperoleh nilai rata-rata 4,41 atau 88,2% dengan kategori sangat baik. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu mendukung proses skrining kesehatan mental mahasiswa secara mandiri berbasis web.
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