Penerapan Data Mining dalam Klasifikasi Penjualan Top Up Game Menggunakan Metode K-Nearest Neighbor - M. Bimo Prasetyo; Dwi Oktarina (Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika, 2025)
The online gaming industry continues to grow rapidly in Indonesia, with many users purchasing digital items through 3rd party top up services such as Pitopup.com. One of the main challenges faced by Pitopup.com is the difficulty in classifying the sales of each available game item. This research aims to apply the K-Nearest Neighbor (KNN) method to predict the sales classification of game items in order to find out the sales category for each game item and hopefully help increase stock efficiency. The dataset used was obtained from historical sales data on Pitopup.com from June to September 2024. The research stages include data processing, normalization using Min-Max Scaling, data transformation using label encoding, separating test and training data using a ratio of 80:20, and using confusion matrix as a model evaluation. The test results show that KNN algorithm is able to classify game item sales on the Pitopup.com website with a level of accuracy in several categories: marketable category at 100%, the moderately sellable category at 100% and the not sellable category at 100%.