Model Prediksi Jumlah Penjualan Pelumas Mesin Di PT. X Dengan Algoritma Naïve Bayes - Purnama, Nilam; Fitri Insani; Elin Haerani; Iis Afrianty (Computer Science and Information Technology, 2024)
Machine lubricants are essential materials used to reduce friction between two moving surfaces, improve machine efficiency, and extend the lifespan of components. This study aims to predict the sales volume of machine lubricants at PT. X using the Naïve Bayes algorithm. The data used includes attributes such as year, month, material description, total allocation, realization, and remaining allocation, with a total of 3,006 data points obtained from PT. X's Warehouse Management System (WMS). The model was tested using the 10-Fold Cross Validation method and testsing without such validation. The test results show an accuracy of 71% with 10-Fold Cross Validation, compared to 14% without validation. Additional testing showed an accuracy of 5%, with RMSE of 124.71 and MAPE of 0.95. Based on these results, it is recommended to optimize data preprocessing, such as handling data imbalance and feature normalization, to improve prediction accuracy. Furthermore, using more diverse validation techniques, such as stratified cross-validation, can provide more stable evaluations. Given that predictions are influenced solely by historical data, it is recommended to periodically update the data to keep the model relevant and accurate. This research is expected to assist PT. X in planning sales strategies and managing lubricant stock more effectively.