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October 15, 2025Journal of Artificial Intelligence and Engineering Applications (JAIEA)Open Access

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

Louis Louis Octara Pribadi Feriani Astuti Tarigan

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Key Points

The developed web-based system enhanced efficiency in school information dissemination.
Random forest and support vector machine algorithms showed promising results in behavioral data outlier detection.
The waterfall method provided a structured approach to software development for the new information system.
The study addressed the manual methods hindering effective communication of school information.

Implication

Analysis reveals effectiveness of random forest and support vector machine in outlier detection, indicating improved information system design.

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Cite This Study

Louis et al. (2025) studied this question.

synapsesocial.com/papers/68f83319d24b29c96948199c https://doi.org/10.59934/jaiea.v5i1.1639

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