Application of the Naïve Bayes Algorithm in Determining Sales Of The Month

Abstract

One important factor for creating a healthy and growing company is the existence of sales rewards for employees to achieve sales targets every month. Assessing employees is not an easy thing when there are so many employees. This will make the assessment team have to look at the criteria carefully and carefully. Data manipulation can occur because it is difficult to make decisions with such large criteria and data without automated data mining. As a result, the company will not get competitive human resources. Sales targets are one of the keys to sales success because with sales targets, the sales prediction value can be used as a guide as a reference in determining product sales. One way to make better sales predictions is by utilizing data mining processing using the Naive Bayes algorithm. The Naive Bayes algorithm calculates the probability value of each of the attributes examined including attendance, sales targets and sales returns. Research with employee absence criteria, monthly sales and monthly sales invoice returns. From the results of the research that has been done, it can be concluded that the application of the Naive Bayes classifier method to the target data set for sales of goods achieves an optimization level of 95.78%, with attendance criteria greatly affecting employee performance so that product sales targets each month can be achieved

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