Optimasi Strategi Penjualan Produk Zaskia Melalui Integrasi Algoritma K-Nearest Neighbors (KNN) dan Regresi Linier - Ermatita; Belly Nagustria Belly (Fakultas Teknik Universitas Muhammadiyah Bengkulu, 2025)
This study aims to evaluate the model's performance in classifying and predicting the number of product sales based on several attributes. The K-Nearest Neighbors (KNN) algorithm was used for the classification task and showed good performance with an accuracy of 91.22%, recall of 92.96%, and precision of 90.18%. These results indicate that the model has a high generalization ability in recognizing sales patterns. For quantitative prediction of the number of sales, a linear regression model is used with independent variables such as regular price, selling price, rating, number of ratings, and number of favorites. The regression model yielded a coefficient of determination (R²) of 0.78, indicating that 78% of the variability of the sales amount can be explained by these variables. The coefficient analysis results show that the rating, number of ratings, and number of favorites have a positive influence on the sales amount, while the selling price and normal price have no significant effect. The Root Mean Squared Error (RMSE) value of 33.09 indicates a fairly low level of prediction error. These findings indicate that the model used is effective in helping analyze and forecast product sales.