Klasifikasi Data Tak Seimbang Menggunakan Algoritma Random Forest dengan SMOTE dan SMOTE-ENN (Studi Kasus pada Data Stunting)
(Studi Kasus pada Data Stunting)
DOI:
https://doi.org/10.30989/teknomatika.v17i2.1530Keywords:
Klasifikasi, Imbalanced Class, Random Forest, SMOTE, SMOTE-ENNAbstract
Algoritma random forest merupakan salah satu metode klasifikasi pembelajaran mesin yang banyak digunakan karena memiliki keunggulan dalam mengurangi resiko overfitting sekaligus meningkatkan kinerja prediksi secara umum. Namun untuk data dengan kelas tidak seimbang, algoritma ini tidak mampu mencapai performa maksimal khususnya dalam memprediksi data pada kelas minoritas. Untuk itu artikel ini menawarkan dua metode resampling untuk menyeimbangkan data, yaitu Synthetic Minority Oversampling Technique (SMOTE) dan Synthetic Minority Oversampling Technique with Edited Nearest Neighbors (SMOTE-ENN). Untuk klasifikasi data diterapkan algoritma random forest terhadap data asli dan hasil resampling baik menggunakan SMOTE maupun SMOTE-ENN. Studi kasus diterapkan pada data stunting yang berjumlah 421 pada kelas mayoritas dan 79 pada kelas minoritas. Diperoleh akurasi 89% pada data asli, 90% pada data hasil resampling dengan SMOTE-ENN, dan 91% pada data resampling dengan SMOTE. Walaupun tidak terlalu signifikan, teknik resampling dengan SMOTE memberikan akurasi terbaik.
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