AJI, SENA MELIANTA (2022) KOMPARASI ALGORITMA NAÏVE BAYES DAN K-NEAREST NEIGHBOR UNTUK MENENTUKAN KLASIFIKASI PRODUK TERLARIS (STUDI KASUS: PERUSAHAAN FROZEN FOOD XYZ). S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Sales of frozen food among the public are expanding because people's lives prefer practical and fast food. One of the things we can use is technology in extracting useful information from the company's data warehouse selling frozen food. The strategy that can be done is to study consumer behavior patterns. This pattern can be identified by utilizing sales transaction data in the company PT. Frozen Food XYZ. Such is the amount of daily transactions that continue to grow. These many transactions will make it difficult for company actors to process their data. The purpose of this study is to try to apply the naive Bayes algorithm and k-nearest neighbors algorithm techniques to provide information in the form of sales classification of frozen food products which are the most in demand among the public and not in demand among the public (selling and not selling). From the average accuracy, it can be seen that the average accuracy value of the Naïve Bayes algorithm is 77%, while the K-Nearest Neighbor algorithm has an average accuracy of 99%.. Key words: Classification, Naïve Bayes. KNN Penjualan pada frozen food dikalangan 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 belanja. 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%. Kata kunci: Klasifikasi, Naïve Bayes. KNN
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