Implementasi Data Mining untuk Analisis Data Penjualan dengan Menggunakan Algoritma Naïve Bayes : (Studi Kasus : KPRI Kokarnaba Baturraden) - Lifa Sholiah; Ito Setiawan; Abdillah Teguh Permana; Iqbal Yusuf Azhari; Wakhid Sayudha Rendra Graha Alrashid (Asosiasi Riset Teknik Elektro dan Informatika Indonesia, 2024)
KPRI KOKARNABA Baturraden faces challenges in managing increasingly complex sales data, particularly in identifying the most in-demand products to maximize profit. This study aims to analyze sales patterns using the Naïve Bayes algorithm as a probability-based classification method. The collected sales data were analyzed to identify categories of best-selling and less popular products within the cooperative. The results indicate that the Naïve Bayes algorithm has an accuracy rate of 77.56% in predicting product categories. This research is expected to assist the cooperative in optimizing stock management and improving member satisfaction.