Random Forest dengan Hyperparameter Tuning untuk Sistem Prediksi Risiko Depresi Remaja
DOI:
https://doi.org/10.24114/cess.v11i2.74393Kata kunci:
prediksi risiko depresi, random forest, optimasi bayesian, machine learning, SMOTEAbstrak
Deteksi dini kerentanan depresi pada fase remaja memerlukan pendekatan komputasi yang presisi. Penelitian ini bertujuan merancang arsitektur sistem cerdas prediksi risiko depresi berbasis web dengan menganalisis parameter lifestyle indicators. Metodologi yang diusulkan mengimplementasikan algoritma klasifikasi Random Forest yang dipadukan dengan teknik penyeimbangan data Synthetic Minority Over-sampling Technique (SMOTE). Untuk memaksimalkan performa prediktif, studi ini menghadirkan intervensi kebaruan berupa hyperparameter tuning menggunakan pendekatan optimasi Bayesian. Hasil pengujian komputasi membuktikan bahwa metode Bayesian sukses mendongkrak tingkat akurasi model secara solid, dari 77,20% pada konfigurasi default menjadi 79,20%. Nilai orisinalitas sekaligus dampak paling krusial dari pemodelan ini adalah kemampuannya menekan kesalahan deteksi fatal secara signifikan, yang tercermin dari capaian metrik sensitivitas (Recall) sebesar 0.9643 khusus pada kelas risiko tinggi. Integrasi penuh dari algoritma pasca optimasi ini ke dalam antarmuka perangkat lunak interaktif mengonfirmasi bahwa penelitian ini tidak sekadar menyajikan luaran analitis di atas kertas, melainkan berhasil memproduksi instrumen skrining preventif yang fungsional.
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