Utilizing Datamining to Predict Sales Trends Based on Historical Data

Authors

  • Alby Afifuddin Junda Universitas Widyagama Malang
  • Maria Rosalina Trisna Universitas Widyagama Malang
  • Yustino Prami Genohon Universitas Widyagama Malang
  • Farrel Muhammad Raihan Akhdan Universitas Widyagama Malang
  • Imam Auwal Salisu Centre for Management Development

Keywords:

Naive Bayes, SVM, Apriori Algorithm, Prediction Accuracy

Abstract

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.

Downloads

Published

2025-05-07

Issue

Vol. 1 No. 3 (2024): October

Section

Articles

License

Copyright (c) 2025 Journal of Information Technology application in Education, Economy, Health and Agriculture

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.