Focused on research planning by outlining the problem statement regarding data redundancy in event management. Defined the project objectives and selected the Jamstack architecture (Next.js & Supabase) as the core technology stack. 

Conducted a requirement gathering phase to identify the functional differences between "Student" and "Organizer" roles. Researched existing Lexicon-based sentiment analysis methods to guide the decision science component of the project. 

Met with the supervisor to discuss the proposed system architecture and database schema. Received feedback on the "Data Integrity" scope and refined the methodology to focus on Third Normal Form (3NF) normalization.

Worked on reporting Chapter 1, focusing on the introduction and significance of study. Designed the Entity Relationship Diagram (ERD) to map out the relationships between Events, Users, and Feedback tables. 

Focused on the environment setup. Initialized the Next.js project repository on GitHub and linked it to Vercel for continuous deployment. Configured the Supabase PostgreSQL database to prepare for backend development. 

Began reporting on Chapter 2 (Literature Review). Analyzed similar event management systems to identify gaps in decision support features. Sketched low-fidelity wireframes for the dashboard interface. 

Started the backend implementation. Created the necessary tables in Supabase and applied Foreign Key constraints to ensure data integrity. Implemented Row Level Security (RLS) policies to secure user data. 

Focused on frontend development. Built the User Authentication interfaces (Login/Signup) and the "Create Event" forms for organizers. Successfully tested the connection between the frontend forms and the backend database. 

Met with the supervisor to demonstrate the initial prototype. Developed the "Student Feed" interface and the feedback submission forms. Ensured that data submitted by students is correctly linked to specific events. 

Focused on the Decision Science module. Wrote the JavaScript logic for the Lexicon-Based Sentiment Analysis. Created a weighted dictionary of keywords to automatically classify feedback text into positive, neutral, or negative scores.

Conducted functional testing of the system. Verified that the sentiment analysis engine correctly updates the database in real-time. Communicated with the supervisor via Telegram to discuss progress on Chapter 3 (Methodology). 

Analyzed the test data and developed the "Analytics Dashboard" using charting libraries. Worked on reporting Chapter 4 (Results), visualizing the sentiment scores to prove the system's decision-support capabilities. Designed the presentation poster using Canva. 

Focused on reporting Chapter 5, summarizing the impact of the database design on operational efficiency. Finalized the full FYP report and prepared the script for the final defense presentation. 

Successfully presented the project to the panel of examiners. Demonstrated the live system and the sentiment analysis feature. Submitted all final documentation and the "Future Work" roadmap based on examiner feedback.