The primary objective of this study is to develop and implement a web-based client satisfaction system with machine learning to automate data consolidation and improve reporting efficiency at the National Aviation Academy of the Philippines. It aims to provide a functional platform for users and administrators while assessing the system based on ISO 25010
standards, including aspects such as functionality, reliability, usability, security, and performance. The study also seeks to determine the
cost-benefit of implementing the system, compare its effectiveness to traditional methods, and create a user manual.
Lastly, it will gather recommendations to help future researchers enhance the system for NAAP.

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  Our System

  The Web-Based Client Satisfaction System with Machine Learning is a campus-wide platform built for the National Aviation Academy of the Philippines (NAAP) to collect, analyze, and report student feedback—fast, securely, and at scale.

• 

  Our Commitment

  We follow the ISO/IEC 25010 software quality model—focusing on functionality, reliability, usability, security, performance, compatibility, maintainability, and portability—to ensure a dependable experience for students and administrators alike.

• 

  What we do

  We streamline the entire feedback cycle—from QR code survey access to real-time consolidation and reporting. A built-in logistic regression model turns survey data into clear, predictive insights on satisfaction trends so decision-makers can act before small problems become big ones.

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  Why it matters

  Student experience is central to NAAP's mission. By automating data collection and applying machine learning, the system reduces errors and delays, strengthens transparency through audit trails, and supports data-driven improvements across offices and services.

• Turning
  Feedback
  Into Action