Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal Software Engineering and Computer Science (IJSECS)
Maulidia Tuzahra
Politeknik Piksi Ganesha
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Student Aspiration Processing Information System with Sentiment Analysis at Piksi Ganesha Polytechnic Maulidia Tuzahra; Johni S Pasaribu
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5719

Abstract

Student aspirations play an important role as a means of two-way communication to improve the quality of academic and non-academic services in higher education. However, manual aspiration submission systems often result in delays in follow-up and a lack of documentation. This study aims to design and implement a web-based student aspiration processing information system integrated with sentiment analysis. The development method used is Waterfall, with stages of requirements analysis, design, implementation, testing, and maintenance. The implementation was carried out using the PHP programming language and MySQL database. The main features of the system include registration, login, feedback form, feedback list, admin replies, and lexicon-based sentiment analysis. Testing using Black Box Testing showed that all functions ran according to user requirements (100% success rate), with an average system response time of 2.7 seconds and a user satisfaction rate of 92%. This system is capable of classifying aspirations into positive (46%), negative (38%), and neutral (16%) categories, thereby facilitating the evaluation of campus services. This research proves that the system is capable of accelerating the handling of aspirations by up to 40% compared to manual mechanisms and supports decision-making based on sentiment data.
Co-Authors Pasaribu, Johni S