PERBANDINGAN ALGORITMA SVM DAN RANDOM FOREST PADA KLASIFIKASI KESEHATAH MENTAL BERDASARKAN SLEEP DISORDERS
Abstract
Kesehatan mental semakin menjadi isu global yang mendesak, dengan meningkatnya prevalensi gangguan mental yang mempengaruhi jutaan individu di seluruh dunia. Penelitian ini bertujuan untuk mengevaluasi kinerja dua algoritma pembelajaran mesin, yaitu Support Vector Machine (SVM) dan Random Forest, dalam mendeteksi gangguan kesehatan mental melalui analisis pola tidur. Data yang digunakan berasal dari dataset "Stress Level Detection" di Kaggle, yang telah mengalami augmentasi menjadi 1.375 sampel. Dataset dibagi menjadi 80% untuk pelatihan model dan 20% untuk pengujian. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki kinerja yang lebih baik dibandingkan SVM, dengan akurasi mencapai 91% dan F1-score rata-rata 89%. Sementara itu, SVM memperoleh akurasi 83% dan F1-score rata-rata 81%. Secara spesifik, Random Forest lebih efektif dalam mendeteksi pola tidur normal serta gangguan seperti insomnia. Temuan ini menunjukkan potensi pembelajaran mesin, terutama Random Forest, sebagai alat yang efektif untuk deteksi dini gangguan kesehatan mental melalui analisis pola tidur, yang dapat digunakan untuk mendukung diagnosis lebih cepat dan akurat.
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