Home⇛Isabela State University Linker: Journal of Engineering, Computing and Technology⇛vol. 3 no. 1 (2026)

Development of an Automated Image and Data Processing System for Client Feedback Analysis: A Case Study of the Laguna State Polytechnic University

Jhon Mark G. Chavez | Aljon Dela Cruz | Jonwayne A. Unisa | Ronnel A. Dela Cruz | John Ryan S. Ual | Joesua R. Manzanero | Joanna E. De Torres

Discipline: Information Technology

 

Abstract:

The use of a manual, paper-based process for gathering and analyzing client feedback requires the Management Information Systems (MIS) office to sort, encode, and interpret each form, which often leads to delays, inconsistencies, and a high risk of human error. As feedback volume increases, the manual workflow becomes less efficient and slows an institution’s ability to respond and make timely decisions. To address this, the study developed an automated image and data processing system for client feedback analysis at Laguna State Polytechnic University – San Pablo City Campus (LSPU-SPCC) to improve accuracy and efficiency. Using the Agile methodology, the system was developed through requirement gathering, design, development, and continuous testing. Technologies such as Flutter SDK, Dart SDK, Android Studio/Android SDK, Java, Kotlin, Gradle, Google MLKit, TensorFlow Lite, SyncFusion Widget, Camera Plugin, Image Processing Library, Permission Handler, Path Provider, Share plus OpenFile X, and CSV Library enabled mobile scanning, automated data extraction, sentiment analysis, and Excel report generation. The evaluation involved eight (8) respondents and a dataset of 150 scanned test forms. Quantitative results confirmed reliable system performance, achieving a 96.4% OCR character accuracy rate, a sentiment classification F1-score of 0.91, and an average processing time of 2.5 seconds per form. Furthermore, the ISO/IEC 25010 evaluation yielded an overall weighted mean score of 4.39, indicating a very good software quality. Overall, the system served as a highly effective prototype that enhanced feedback processing, reduced errors, and supported faster decision making.



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