Comparison of Random Forest and Support Vector Machine Algorithms in Behavioral Data Outlier Detection Customer Purchases

Louis; Octara Pribadi; Feriani Astuti Tarigan

doi:10.59934/jaiea.v5i1.1639

Authors

Louis STMIK TIME
Octara Pribadi STMIK TIME
Feriani Astuti Tarigan STMIK TIME

DOI:

https://doi.org/10.59934/jaiea.v5i1.1639

Keywords:

Outliers, Customer Purchasing Behavior Data, Random Forest, Support Vector Machine (SVM), Machine Learning

Abstract

The development of information technology, particularly the internet, has brought significant changes in various fields, including education. Mestika Abadi School (Chong Ren School) in Medan still uses manual methods in delivering school information. This creates several obstacles such as limited information reach, slow information dissemination process to parents and the general public, and lack of efficiency in promoting the school's profile and excellence to prospective new students. This study aims to design and build a web-based school company profile information system that can replace the manual process in promoting school information, using the Waterfall method in its software development. This method was chosen because it has clear and structured stages, including needs analysis, system design, implementation, testing, and maintenance. The results of this study indicate that the developed system is able to increase efficiency in delivering school information through an attractive and informative company profile page.

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