Evaluasi Dan Komparasi Algoritma Naïve Bayes, Support Vector Machine, Dan K-Nearest Neighbor Pada Data Mining Untuk Prediksi Perilaku Konsumen
Keywords:
Prediksi perilaku konsumen, Data mining, RFM, Naïve Bayes, Support Vector Machine, K-Nearest NeighborsAbstract
Pertumbuhan industri ritel online menghasilkan volume data transaksi yang besar dan kompleks, yang dapat dimanfaatkan untuk memahami perilaku konsumen secara lebih akurat. Penelitian ini bertujuan untuk mengevaluasi dan membandingkan performa tiga algoritma klasifikasi Naïve Bayes, Support Vector Machine (SVM), dan K-Nearest Neighbors (KNN)—dalam memprediksi loyalitas pelanggan berdasarkan pendekatan Recency, Frequency, dan Monetary (RFM). Dataset yang digunakan diambil dari UCI Machine Learning Repository, yang mencakup lebih dari 540.000 transaksi pelanggan. Data diproses melalui tahapan pembersihan, transformasi RFM, labeling loyalitas, dan normalisasi sebelum dilakukan pelatihan model. Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, F1-score, dan ROC-AUC. Hasil penelitian menunjukkan bahwa algoritma SVM memberikan performa terbaik dengan akurasi sebesar 84,50% dan ROC-AUC 0,90, diikuti oleh KNN dan Naïve Bayes. Meskipun demikian, Naïve Bayes unggul dalam efisiensi pemrosesan. Temuan ini menegaskan pentingnya pemilihan algoritma yang disesuaikan dengan konteks dan kebutuhan spesifik dalam penerapan sistem prediksi perilaku konsumen berbasis data miningReferences
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