Utilizing Datamining to Predict Sales Trends Based on Historical Data - Junda, Alby Afifuddin; Trisna, Maria Rosalina; Genohon, Yustino Prami; Akhdan, Farrel Muhammad Raihan; Salisu, Imam Auwal (Lumina Infinity Academy Foundation, 2024)
This study aims to compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in predicting sales trends based on historical data. The results of the study show that SVM is more effective than Naïve Bayes with an accuracy of 34.74% compared to 15.49%. This study helps companies in making strategic decisions and improving operational efficiency. Data Mining is an important tool in predicting sales trends and improving prediction accuracy.