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All Journal International Journal of Artificial Intelligence International Journal of Recent Technology and Applied Science (IJORTAS)
Ahmad Fadli Saad
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Student Expense Tracking System Using OCR Ahmad Fadli Saad; Muhammad Hairil Shaharudin; Achmad Yani; Abdi Manaf; Andi Almeira Zocha Ismail; Andi Regina Acacia Ismail
International Journal of Artificial Intelligence Vol 12 No 2: December 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-01202.929

Abstract

Nowadays student schedules are packed with their academic and curricular activities. Therefore, Students are no longer tracking their expenses because it is so hard to keep track with their expenses when they a have busy life. The aim of this research is to help students easily track their expenses by automating the process of extracting information from receipts. This research presents a student tracking expenses system using Optical Character Recognition (OCR) technology. The method that was used to develop the system was Website Development Life Cycle (WDLC). The system also uses Image Processing that implements OCR into the system. The system has been tested with a set of sample receipts, and the results show that it is able to accurately extract the relevant information with a high level of efficiency. The initial of this research involved designing the system, which was achieved through the creation of a detailed mockup and wireframe to establish a clear vision for its design. Then, it focused on developing the system, incorporating OCR technology to extract text from receipts. Thorough functional testing ensured that all system features, including user identification, image upload and OCR processing, expenditure management, budget setting, and data visualization, functioned as intended. The system offers users accurate and dependable capabilities for spending pattern analysis, budget management, and expense monitoring. Furthermore, the usability testing was conducted using the Post-Study System Usability Questionnaire (PSSUQ) from 30 students. The mean score of the System Usefulness, Information Quality and Overall Satisfaction is above 4 which indicates that it was appreciated by the students or respondents. Therefore, this system can be a valuable tool for students to manage their finances and make informed decisions about their spending.
Predicting Customer Purchase Intent in E-Commerce Platforms Using Session-Based Logistic Regression Analytics Nor Anisah Salamah Mohd Rosli; Ahmad Fadli Saad
International Journal of Recent Technology and Applied Science (IJORTAS) Vol 8 No 2: September 2026
Publisher : Lamintang Education and Training (LET) Centre

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijortas-0802.1093

Abstract

The rapid acceleration of global e-commerce has fundamentally transformed retail operations, yet digital merchants continue to confront substantial financial losses resulting from inefficient inventory planning, cart abandonment, and imprecise consumer behavior forecasting. This research develops ShopPredict, a session-based predictive analytics framework designed to forecast online shopping purchase completion by modeling user interaction dynamics including product viewing frequency, cart additions, session price averages, and unique item explorations using a Logistic Regression classification approach. Utilizing an empirical multi-category e-commerce dataset comprising millions of user session logs, the classification model was trained and evaluated across structured behavioral features. The experimental results demonstrated a high overall accuracy of 93.48%; however, rigorous metric decomposition revealed a critical accuracy paradox, wherein the model achieved a high precision of 0.94 for non-purchase sessions but exhibited a low recall of 0.16 and an F1-score of 0.25 for purchase events due to severe underlying class imbalance. System usability and functional architecture evaluations yielded positive user acceptance scores averaging above 4.0 on a 5-point Likert scale. This study underscores the utility of session-based linear probability modeling while demonstrating that high nominal accuracy in e-commerce prediction can be misleading without rigorous class-rebalancing strategies. Future research must incorporate advanced ensemble architectures, SMOTE oversampling, and dynamic session features to optimize purchase intent detection in real-time retail environments
Co-Authors Abdi Manaf Achmad Yani Andi Almeira Zocha Ismail Andi Regina Acacia Ismail Muhammad Hairil Shaharudin Nor Anisah Salamah Mohd Rosli