Komparasi Algoritma Machine Learning Untuk Klasifikasi Risiko Depresi Remaja Berbasis Perilaku Digital Menggunakan SMOTE

Sarwadi Sarwadi; Muhammad Khaibar Putra Adithia

doi:10.70340/jirsi.v5i2.582

Penulis

Sarwadi Sarwadi Universitas Nahdaltul Ulama Sumatera Utara
Muhammad Khaibar Putra Adithia Universitas Nahdlatul Ulama Sumatera Utara

DOI:

https://doi.org/10.70340/jirsi.v5i2.582

Kata Kunci:

machine learning, depresi remaja, SMOTE, XGBoost, Imbalanced dataset

Abstrak

Depresi pada remaja merupakan masalah kesehatan mental global yang semakin mengkhawatirkan, terutama di era percepatan adopsi platform media sosial. Penelitian ini membandingkan performa empat algoritma machine learning, Random Forest, XGBoost, Support Vector Machine (SVM), dan Logistic Regression, dalam mengklasifikasikan risiko depresi remaja berbasis 12 fitur perilaku digital dan gaya hidup. Dataset yang digunakan terdiri dari 1.200 rekaman dengan ketidakseimbangan kelas yang ekstrem (97,4% vs 2,6%), ditangani melalui penerapan Synthetic Minority Over-sampling Technique (SMOTE) dengan k=5. Delapan eksperimen dilakukan (empat algoritma × dua kondisi) dan dievaluasi menggunakan Precision, Recall, F1-Macro, AUC-ROC, serta 5-fold cross-validation. Hasil menunjukkan SMOTE secara konsisten meningkatkan performa semua model, dengan peningkatan F1-Macro rata-rata sebesar 25,7%. XGBoost unggul dengan F1-Macro dan AUC-ROC sempurna (1,0000) pada kedua kondisi, sedangkan pengaruh SMOTE paling besar dirasakan oleh SVM (+67,9%) dan Logistic Regression (+63,7%). Analisis feature importance mengidentifikasi jam tidur, tingkat stres, tingkat kecemasan, dan penggunaan media sosial harian sebagai prediktor dominan, sementara prestasi akademik memiliki kontribusi yang dapat diabaikan. Temuan ini menegaskan pentingnya penanganan imbalanced data dan memberikan kerangka metodologis untuk pengembangan sistem deteksi dini depresi berbasis perilaku digital.

Unduhan

Data unduhan belum tersedia.

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