Klasifikasi Omset ATK menggunakan Algoritma Naïve Bayes
Abstract
Small businesses play a role in absorbing work energy, as a source of innovation, providing economic services to the community and in the process of equalizing and increasing community profit. One of the factors that influences the dynamics of modern business is technological progress. Evolving technology has opened the door to rapid innovation. Profit classification is a popular and effective approach for businesses that can tailor marketing strategies to each group more effectively which will impact business profit. The stock condition of an item greatly influences sales revenue. The increasing demand for goods will result in large profit. Product availability to meet consumer needs is a problem that must be overcome. The stock condition of an item greatly influences sales profit. This research is related to the classification of profit for each item sold in a shop, whether it is sold a lot or not enough to maximize the stock of goods per time using the Naive Bayes algorithm. From data research, attribute grouping, preprocessing, data transformation and modeling were carried out using the Naïve Bayes algorithm in the Python programming language. Testing the Naïve Bayes algorithm obtained 90% accuracy results for the classification of stationery profit. The system can determine the classes of goods that are sold a lot and goods that are not sold enough, becoming a solution for shop leaders to more easily take business strategies quickly and optimally which of course will affect profit.
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