K-Nearest Neighbor and Naive Bayes Algorithm Approach for Online Sales Level Classification Optimization In South Tangerang - Raihan Fajar, Muh. Dava; Puspitasari, Diah; Nur Azizah, Qudsiah (Moneter: Jurnal Keuangan dan Perbankan, 2024)
Online sales are growing along with the increasing number and ease of access to E-commerce platforms, but in online transactions, product information is generally only presented in the form of specifications and images, the accuracy of which cannot always be ensured. Based on one of the journals obtained from the Central Statistics Agency (BPS), the number of Micro, Small and Medium Enterprises (MSMEs) in Indonesia reaches 64.2 million businesses. The purpose of this study is to determine the performance of the two methods, namely K-Nearest Neighbor and Naive Bayes in classifying the success of online sales in South Tangerang City, and comparing the results of the two methods using the Rapid Miner application. The dataset used in this research is an online sales business actor totaling 120 MSME actor data. The results of this study show that K-Neirest Neighbor has a performance of 89.26% while Naive Bayes is 90.91%, so the k-Neirest Neighbor method has the best performance and is able to classify the success of online sales and is able to help increase the economy of citizens besides that it also increases the attractiveness of young people to entrepreneurship and creates new jobs in South Tangerang City.