Implementasi Naïve Bayes untuk Menentukan Menu Makanan Favorit (Studi Kasus: Pindang Umak KM 10)
Abstract
This study aims to implement the Naïve Bayes algorithm to classify food menus into "Favorite" and "Not Favorite" categories based on historical sales data at Pindang Umak KM10. The dataset consists of 275 sales records, divided into 80% (220 records) for training data and 20% (55 records) for testing data. The attributes used in the classification process include menu name, menu category, and quantity sold. The classification stages comprise data preprocessing, data splitting into training and testing sets, and the calculation of prior probability, likelihood, and posterior probability. The Naïve Bayes model was subsequently implemented in a web-based application developed using PHP and MySQL. The evaluation on 55 test records showed that 40 records were correctly classified, whereas 15 records were misclassified, achieving an accuracy of 72.73%, precision of 61.11%, recall of 95.65%, and an F1-score of 74.58%. These results indicate that the Naïve Bayes algorithm can classify food menus based on sales data patterns, thereby providing empirical support for menu management at Pindang Umak KM10.
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Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.
Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.
Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.
Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.
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