Prediction of New Customer Segmentation Classification Using Artificial Intelligence Project Cycle Orange Data Mining - Kosat , Fransiska Febriyanti; Rema, Yasinta Oktaviana Legu; Ullu, Hevi Herlina (Association for Scientific Computing, Electronics, and Engineering (ASCEE), 2024)
This research aims to predict the right segmentation group or classification of new customers to become a classification comparison data carried out by the sales team to determine the strategy used to enter the market, whether it can be said to be feasible or not. This article discusses the basis of the method used, i.e., Machine Learning, discussed in detail about Artificial Intelligence (AI). Also discusses what is Classification, Segmentation, Data Mining, Neural Networks, Naive Bayes, Decision Trees, Random Forest (RF), and Support Vector Machine (SVM). This article discusses comprehensively the method used, and the development of Modeling, in the results and analysis section, comprehensively shows the prediction analysis of new customer segmentation classification, algorithm performance results of several methods, and distributions analysis. With the percentage prediction of new potential customer segmentation using the Neural Network method, the percentage prediction of Segmentation A is 25.21%, the percentage prediction of Segmentation B is 21.77%, the percentage prediction of Segmentation C is 23.49%, the percentage prediction of Segmentation D is 29.53%. The percentage of segmentation that has been calculated by the company is the percentage of Segmentation A of 32.13%, the percentage of Segmentation B of 20.89%, the percentage of Segmentation C of 17.69%, and the percentage of prediction of Segmentation D of 29.29%.