Sales Prediction at Caffe Resto Omah Lawas Using the Naive Bayes Method

Putra Vira Madya; Munif

doi:10.59934/jaiea.v6i1.2647

Authors

Putra Vira Madya Universitas Islam Lamongan
Munif Universitas Islam Lamongan

DOI:

https://doi.org/10.59934/jaiea.v6i1.2647

Keywords:

Data Mining, Naive Bayes, Sales prediction, Information System

Abstract

This study discusses the design and implementation of a sales prediction system for Caffe Resto Omah Lawas using the Naïve Bayes method. The objective of this research is to assist business owners in anticipating fluctuations in customer demand, optimizing product inventory, and improving data-driven marketing strategies. The research process includes sales data collection, data preprocessing to clean and prepare the dataset, transformation of numerical attributes into categorical attributes, and dataset splitting into 80% training data and 20% testing data. The system was developed using the PHP programming language and MySQL database and implemented as a web-based application to facilitate accessibility and ease of use. The experimental results show that the proposed system achieved an accuracy of 90%, with excellent performance in the "Best-selling" and "Unsold" classes, while the performance for the "Normal" class was relatively lower. Furthermore, the system provides strategic recommendations based on prediction results, including inventory adjustment, promotional planning, and product development according to customer preferences. Therefore, the implementation of the Naïve Bayes method has proven to be effective as a data-driven decision support tool for sales and marketing management.

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