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Vol.15, No.3, August 2026. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Utilizing k-Nearest Neighbors and Support Vector Machine for Ticket Support System
Faqihah Binti Zakir, Su-Cheng Haw, Tong-Ern Tai, Kok-Why Ng, Maw Maw
© 2026 Su-Cheng Haw, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 15, Issue 3, Pages 2126-2137, ISSN 2217-8309, DOI: 10.18421/TEM153-07, August 2026.
Received: 07 August 2025.
Abstract:
Support tickets are vital for providing high-quality customer service. Ensuring very customer has a positive experience, regardless of their issue, is essential. This paper explores optimizing support ticket systems identifying common algorithms and predicting ticket resolution time. Leveraging Machine Learning, specifically k-Nearest Neighbours (KNN) and Support Vector Machines (SVM), the study aims to predict resolution times using historical data. These algorithms will be evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. The goal is to assess the effectiveness of KNN and SVM in improving support processes. Accurate time predictions can help businesses allocate resources proactively, minimize delays, and streamline ticket assignments. By applying these models, organizations can enhance response efficiency and reduce the impact of unresolved issues.
Keywords – Ticket system, machine learning, prediction, customer support, resolution time. |
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