Development of “VoksConnect” as a Student Complaint Application with NLP Chatbot
- DOI
- 10.2991/978-94-6463-626-0_24How to use a DOI?
- Keywords
- Natural Language Processing (NLP); Chatbot; Software Development Life Cycle (SDLC); System Usability Scale (SUS); Complaint Application
- Abstract
This study aims to develop a web-based application for collecting complaints from the academic community at the Faculty of Vocational Studies. The Research and Development (R&D) method was employed, involving need analysis, application design, implementation, and evaluation. The application is designed to facilitate reporting and responding to complaints related to various aspects of academic life for students, lecturers, and administrative staff. The development utilized the SDLC method and integrated ChatBot with NLP systems. User testing and feedback collection ensured the application’s suitability and quality. The System Usability Scale (SUS) evaluation yielded an average score of 64.47, indicating user acceptance. The comprehensive usability testing results support the application’s effectiveness in addressing student complaints efficiently and supportively.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Tegar Eka Pambudi El Akhsan AU - Hafizhuddin Zul Fahmi AU - Arfian Putra Pratama AU - Mohammad Avie Siena AU - Dodik Arwin Dermawan PY - 2024 DA - 2024/12/31 TI - Development of “VoksConnect” as a Student Complaint Application with NLP Chatbot BT - Proceedings of the International Joint Conference on Science and Engineering 2024 (IJCSE 2024) PB - Atlantis Press SP - 233 EP - 250 SN - 2352-5401 UR - https://doi.org/10.2991/978-94-6463-626-0_24 DO - 10.2991/978-94-6463-626-0_24 ID - Akhsan2024 ER -