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

JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Vol. 1 No. 3 (2024): October

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.