Penerapan Algoritma Naive Bayes Untuk Prediksi Penjualan Produk Terlaris Pada CV Akusara Jaya Abadi - I Made Adrian Astalina Pramana; I Wayan Sudiarsa; Putu Gede Surya Cipta Nugraha (Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP, 2023)
CV Akusara Jaya Abadi is a company engaged in procuring goods and services. This company is experiencing difficulties in product stock management. For this reason, we need a method that can predict sales of best-selling products using the Naive Bayes algorithm, where this algorithm calculates the likelihood probability value of each general attribute to get a pattern on the label/class based on the highest value of the posterior probability. In addition, the application Naive Bayes algorithm applied using the CRISP-DM framework. The dataset obtained through the SIPLah platform has 3 years, that is 2021-2023 with a total data of 750 records. Implementation data mining with RapidMiner and Bayes' theorem formulation in product sales predictions, there are 5 products that produce the highest probability value namely the best-selling products are the HVS F4 SIDU 70 Gram Paper, Tissue Hand Towel, Tissue Nice 180 Sheets, Kabel NYM Eterna size 2 x 1,5 mm, and Refil Spidol. The process of model analysis and performance of the Naive Bayes algorithm in this research uses a confusion matrix with an accuracy level of 89.33%, precision of 73.21%, and recall of 97.61%. Thus, could be concluded that the implementation of the Naive Bayes algorithm produces sales predictions for best-selling products with good performance, so it can help CV Akusara Jaya Abadi determine its product stock management.