Sistem Rekomendasi Pemilihan Laptop Menggunakan Metode Content Based Filtering Dan K-Nearest Neighbor
Abstract
Sistem rekomendasi memiliki peran penting dalam membantu pengguna menemukan produk yang tepat di antara banyaknya pilihan. Penelitian ini bertujuan mengembangkan sistem rekomendasi laptop menggunakan Metode Content-Based Filtering dan K-Nearest Neighbors (KNN). Sistem ini dirancang untuk memberikan saran laptop berdasarkan spesifikasi dan harga. Dataset yang digunakan mencakup atribut penting seperti RAM, SSD, HDD, sistem operasi, dan prosesor. Penelitian ini menggunakan TF-IDF (Term Frequency-Inverse Document Frequency) untuk mengukur bobot atribut setiap laptop dan cosine similarity untuk menilai kesamaan antar laptop. Metode KNN digunakan untuk menemukan laptop yang paling mirip berdasarkan atribut harga yang dipilih pengguna. Dataset diambil dari Kaggle dan diproses menggunakan berbagai pustaka Python seperti pandas, numpy, dan scikit-learn. Hasil penelitian menunjukkan bahwa metode content-based filtering dan KNN efektif dalam memberikan rekomendasi laptop yang relevan dan sesuai dengan kebutuhan pengguna. Pengujian sistem menunjukkan akurasi yang tinggi dalam merekomendasikan laptop yang sesuai dengan preferensi spesifik pengguna, sehingga membantu mereka membuat keputusan pembelian yang lebih baik dan efisien.
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DOI: https://doi.org/10.32528/jasie.v6i2.22775
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