Analisis Performa Algoritma Smote-Bagging Dalam Klasifikasi Data Tidak Seimbang Dengan Metode Chi-Square Automatic Interaction Detection (CHAID)
DOI:
https://doi.org/10.26555/jim.v10i1.30873Keywords:
CHAID,, Data tidak seimbang,, Klasifikasi,, SMOTE-Bagging,Abstract
Klasifikasi data tidak seimbang sering menghadapi tantangan dalam mencapai keseimbangan antara sensitivitas dan spesifisitas. Penelitian ini menganalisis performa algoritma SMOTE-Bagging pada klasifikasi data tidak seimbang menggunakan metode Chi-Square Automatic Interaction Detection (CHAID), dengan studi kasus stunting pada balita tahun 2022 di Bojongsoang. SMOTE (Synthetic Minority Over-sampling Technique) digunakan untuk meningkatkan representasi kelas minoritas dalam dataset, kemudian digabungkan dengan teknik Bagging untuk meningkatkan kinerja klasifikasi. Hasil penelitian menunjukkan bahwa algoritma SMOTE-Bagging CHAID meningkatkan performa dalam klasifikasi data tidak seimbang, dengan peningkatan sensitivitas sebesar 65%, Area Under Curve (AUC) sebesar 42%, dan keseimbangan antara sensitivitas dan spesifisitas (G-Mean) sebesar 71%. Implementasi SMOTE-Bagging meningkatkan sensitivitas dan memberikan keseimbangan yang lebih baik antara sensitivitas dan spesifisitas.
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