Komparasi Algoritma Naïve Bayes Dan K-Nearest Neighbor Untuk Menentukan Klasifikasi Produk Terlaris (Studi Kasus: Perusahaan Frozen Food XYZ)
DOI:
https://doi.org/10.22441/jitkom.v9i1.009Kata Kunci:
Frozen Food, Klasifikasi, KNN, Naïve Bayes, TransaksiAbstrak
Penjualan pada frozen food di kalangan masyarakat semakin meluas karena kehidupan masyarakat saat lebih memilih makanan yang praktis dan cepat saji. Salah satu yang dapat kita manfaatkan adalah teknologi dalam menggali informasi yang bermanfaat dari gudang data perusahaan penjualan frozen food. Strategis yang dapat dilakukan adalah dengan mempelajari pola perilaku konsumen. Pola tersebut dapat diketahui dengan memanfaatkan data transaksi penjualan di perusahaan PT. Frozen Food XYZ. Demikian besar transaksi harian yang terus bertambah. Transaksi yang banyak tersebut akan mempersulit pelaku perusahaan dalam mengolah data mereka. Tujuan penelitian ini adalah mencoba menerapkan teknik metode algoritma naïve bayes dan k-nearest neighbors memberikan informasi berupa klasifikasi penjualan produk frozen food yang paling laris dikalangan masyarakat dan tidak laris dikalangan masyarakat (laris dan tidak laris). Dari rata – rata accuracy yang ada dapat dilihat bahwa nilai rata rata accuracy algoritma Naïve Bayes adalah 77%, sedangkan algoritma K-Nearest Neighbor nilai rata – rata accuracy sebesar 99%.
Referensi
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