Development of a Web-based Event

Management System with Feedback Module for University Student Activities

Project Overview

Overview : I developed this full-stack web application to modernize how universities handle events. Previously, organizers relied on disconnected spreadsheets, leading to data errors and ignored student feedback.

The Solution : I built a centralized platform using Next.js and Supabase (PostgreSQL) to ensure strict data integrity. Beyond just managing events, the system features a custom Lexicon-Based Sentiment Analysis engine. This module automatically scans student feedback and converts text into quantitative "Satisfaction Scores," turning raw data into actionable insights for university management.

This Data-Driven Event Management System begins with a secure Login Interface powered by Supabase Authentication, which strictly enforces role-based access to ensure students and organizers are routed to their appropriate portals. The core of the application is the Analytics Dashboard, a decision-support tool that visualizes real-time event performance through dynamic charts driven by the system's custom Lexicon-Based Sentiment Analysis algorithm. You can experience the full functionality of this responsive web application by scanning the QR Code, which links directly to the live production build deployed on Vercel.

Objective

This project aims to develop a real-time event dashboard using Next.js and Supabase that leverages a normalized database schema to ensure data integrity and integrates automated sentiment analysis to facilitate evidence-based decision support.

Methodology

1. Architectural Framework (Jamstack)

"Adopted a modern Jamstack Architecture to prioritize performance and scalability. The system utilizes Next.js for a responsive, component-based frontend, connected to Supabase (Backend-as-a-Service) via secure REST APIs. The entire application is deployed on Vercel's edge network, ensuring continuous integration (CI/CD) and high availability."

2. Database Engineering (Data Integrity)

"Designed a Normalized Relational Database schema (up to Third Normal Form) using PostgreSQL. The implementation focuses on strict Referential Integrity through Foreign Key constraints, effectively acting as a 'Single Source of Truth' to eliminate the data redundancy errors common in manual spreadsheet methods."

3. Decision Science Engine (Automated Analytics)

"Integrated a Lexicon-Based Sentiment Analysis algorithm to drive decision support. Unlike black-box models, this transparent, rule-based engine scans unstructured student feedback against a weighted keyword dictionary. This process automatically converts qualitative text into quantitative metrics (Positive, Neutral, Negative) for immediate visualization."

Feedback Logic :

Input : A student submits a star rating (1-5) and a written comment.
Analysis : The system automatically reads the text using a simple logic script.
Tagging : It tags the feedback as Positive, Neutral, or Negative.
Storage : This data is saved in a structured format (jsonb) for easy analysis.

Significance of Study

The system drives operational efficiency by automating registration tracking while empowering strategic decision-making through quantitative sentiment scores.

Discussion & Conclusion

This project highlighted the critical intersection between database management and decision science. By transitioning the university’s workflow from manual spreadsheets to a normalized relational database, I successfully eliminated "double-inputting" errors and ensured strict data integrity. Beyond operational efficiency, the integration of automated sentiment analysis transformed the platform into a strategic decision-support tool. This empowers university management to move beyond intuition, allowing them to instantly identify underperforming events—such as 'Lawatan SUKI'—and utilize quantitative data to drive continuous improvement for future activities.

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