Optimasi K-Means Menggunakan Algoritma Genetika pada Metode User-based Collaborative Filtering
DOI:
https://doi.org/10.30865/json.v7i2.9382Keywords:
Algorima Genetika, Algorithms K-Means, User-Based Collaborative Filtering, Sistem Rekomendasi, Jaccard Similarity Coefficient, Sørensen–Dice Coefficient, Hamming CoefficientAbstract
Collaborative filtering merupakan teknik sistem rekomendasi yang menggunakan informasi rating dari beberapa pengguna untuk memprediksi rating suatu item bagi pengguna tertentu. Namun, tidak semua pengguna memberikan rating pada seluruh item. Hal ini menyebabkan ketidakmampuan sistem dalam menentukan nearest neighborhood, sehingga rekomendasi yang dihasilkan menjadi lemah. Penelitian ini mengusulkan penggunaan Algoritma K-Means untuk mengelompokkan neighborhood yang sesuai. Penentuan awal titik pusat klaster pada Algoritma K-Means dioptimalkan menggunakan Algoritma Genetika. Evaluasi dilakukan dengan memvariasikan jumlah klaster optimal pada beberapa metode pengukuran yang digunakan, yaitu Jaccard Similarity Coefficient, Sørensen–Dice Coefficient, dan Hamming Coefficient. Hasil pengujian menggunakan pengukuran Jaccard Similarity Coefficient, Sørensen–Dice Coefficient, dan Hamming Coefficient memperoleh nilai fitness masing-masing sebesar 4.490, 4.979, dan 4.964 untuk jumlah klaster optimal 4 dan 6. Sementara itu, nilai MAPE rata-rata untuk ketiga metode pengukuran kemiripan tersebut sebesar 60%.
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