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All Journal International Journal of Artificial Intelligence International Journal of Education, Science, Technology, and Engineering (IJESTE) International Journal of Recent Technology and Applied Science (IJORTAS) Journal of Engineering, Technology, and Applied Science (JETAS)
Saad, Ahmad Fadli
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics

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Development of a Student Expense Tracking System Using Optical Character Recognition Shaharudin, Muhammad Hairil; Saad, Ahmad Fadli; Yani, Achmad; Manaf, Abdi
International Journal of Artificial Intelligence Vol 12 No 1: June 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01201.741

Abstract

Personal financial literacy is a vital skill for university students, yet many struggle to track their daily expenses due to time constraints and low awareness. This study aims to design and develop a web-based Student Expense Tracking System using Optical Character Recognition (OCR) technology to address this issue. The system allows users to automatically extract and record spending information from receipt images, reducing manual input and improving financial awareness. The development followed the Web Development Life Cycle (WDLC) using the Waterfall model, comprising planning, design, development, and testing phases. Visual Studio Code, Python 3, and Tesseract OCR were employed in system implementation. Wireframes and mockups guided the interface design, while backend development focused on data storage and OCR integration. Functionality testing showed a 100% pass rate across ten scenarios, validating the system's performance in image processing, budget management, and spending visualization. Usability testing using the Post-Study System Usability Questionnaire (PSSUQ) with 30 participants yielded a mean score of 4.45 out of 5, indicating a high level of user satisfaction. The system scored highest on ease of use (4.6), visual design (4.7), and recommendation likelihood (4.8), confirming its intuitive interface and appeal. Slightly lower scores in user confidence (4.1) and data organization (4.2) point to opportunities for interface refinement and improved user guidance. This research concludes that OCR can effectively support financial tracking for students. Future enhancements with NLP and machine learning are recommended to automate expense categorization and improve analytical capabilities.
Co-Authors Abdul Latiff, Muhammad Nazirul Mubin Amir Hamzah, Fikri Nur Izzudin Haris, Muhammad Akmal Husaini Manaf, Abdi Mat Baseri, Mohamad Faiz Mohamad Ghazali, Nor Diyana Syafiqah Mohamad Roslan, Aini Khadijah Muhaiyuddin, Arina Syakirah Nur Iksan Panessai, Ismail Yusuf Rahman, Hanif Ikmal Roslan, Naim Najmi Shaharudin, Muhammad Hairil Yani, Achmad Zainal, Nisa Athirah