Dampak SMOTE terhadap Kinerja Random Forest Classifier berdasarkan Data Tidak seimbang
Abstract
Dalam aplikasi machine learning sangat umum ditemukan kumpulan data dalam berbagai tingkat ketidakseimbangan mulai dari ketidakseimbangan kecil, sedang sampai ekstrim. Sebagian besar model machine learning yang dilatih pada data tidak seimbang akan memiliki bias dengan memberikan tingkat akurasi yang tinggi pada kelas mayoritas dan sebaliknya rendah pada kelas minoritas. Tujuan penelitian ini adalah untuk mengevaluasi dampak dari SMOTE (Synthetic Minority Oversampling Technique) pada pengklasifikasi Random Forest untuk memprediksi penyakit jantung. Data berjumlah 299 berasal dari UCI Machine learning Repository digunakan untuk membangun model prediksi berdasarkan 12 variabel independen dan 1 variabel dependen. Kelas minoritas dalam dataset pelatihan di oversampling menggunakan teknik SMOTE (Synthetic Minority Oversampling Technique). Model dievaluasi tidak hanya menggunakan ukuran kinerja Accuracy dan Precision saja, namun juga menggunakan alternatif ukuran kinerja lainnya seperti Sensitivity, F1-score, Specificity, G-Mean dan Youdens Index yang lebih baik digunakan untuk data yang tidak seimbang. Hasil penelitian menunjukkan bahwa teknik SMOTE (Synthetic Minority Oversampling Technique) mampu mengurangi overfitting sekaligus meningkatkan kinerja model Random Forest pada semua indikator. Peningkatan skor Accuracy sebesar 3.45%, Precision 4.8%, Sensitivity 7.1%, F1-score 4.8%, Specificity 2.1%, G-Mean 4.4%, dan Youdens Index 6.3%. Penelitian ini membuktikan bahwa dalam menentukan pengklasifikasi dengan algoritma machine learning seperti Random Forest, kemiringan kelas dalam data perlu diperhitungkan dan diseimbangkan untuk hasil kinerja yang lebih baik.
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