UiTM research supervisor recommendation system / Ahmad Adam Ahmad Muzzlini

When a postgraduate student takes on a research paper, research supervision is a necessity. A qualified supervisor who is an expert in the topic of study is required for the student to understand or grasp the notion of their selected research title. The act of finding a potential supervisor is a has...

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Main Author:	Ahmad Muzzlini, Ahmad Adam
Format:	Thesis
Language:	English
Published:	2022
Subjects:	Electronic Computers Computer Science Application software
Online Access:	https://ir.uitm.edu.my/id/eprint/58951/1/58951.pdf
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id	my-uitm-ir.58951
record_format	uketd_dc
spelling	my-uitm-ir.589512022-06-28T05:16:39Z UiTM research supervisor recommendation system / Ahmad Adam Ahmad Muzzlini 2022-01 Ahmad Muzzlini, Ahmad Adam Electronic Computers. Computer Science Application software When a postgraduate student takes on a research paper, research supervision is a necessity. A qualified supervisor who is an expert in the topic of study is required for the student to understand or grasp the notion of their selected research title. The act of finding a potential supervisor is a hassle and takes a lot of time which is spent seeking the proper person for supervision. The amount of time it takes can better be spent doing the bulk of the research resulting in a more quality product. Thus, the goal of this project is to develop a system that can recommend potential supervisors from the UiTM list of experts that consists of the faculties available at UiTM Tapah for students that are looking to do research, based on their title and abstract of the research paper. By implementing machine learning content-based filtering to filter the similarities using cosine similarity algorithm and web scraping to obtain the needed supervisor dataset, the system can utilize this information and recommend the suitable potential supervisor for the student. The project uses the waterfall model in order to allow better control throughout the given time. Python is the main language used for the project. Python’s flask web framework is used to develop the interface of the system and selenium for the web automation used for scraping the supervisor data. The system was tested based on several factors that can affect the accuracy of the recommendation results using the dataset of research papers titles and abstracts. Based on the results, most of the recommendation were successful overall. By using this information, the most optimal approach can be used to provide the best recommendation based on the most influential factors tested. In terms of future work, an implementation of collaborative filtering can be used to learn data from past recommendation of users but would need a large dataset of users to work properly. 2022-01 Thesis https://ir.uitm.edu.my/id/eprint/58951/ https://ir.uitm.edu.my/id/eprint/58951/1/58951.pdf text en public degree Universiti Teknologi MARA, Perak Faculty of Computer and Mathematical Sciences Zulkifli, Zalikha
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Zulkifli, Zalikha
topic	Electronic Computers Computer Science Application software
spellingShingle	Electronic Computers Computer Science Application software Ahmad Muzzlini, Ahmad Adam UiTM research supervisor recommendation system / Ahmad Adam Ahmad Muzzlini
description	When a postgraduate student takes on a research paper, research supervision is a necessity. A qualified supervisor who is an expert in the topic of study is required for the student to understand or grasp the notion of their selected research title. The act of finding a potential supervisor is a hassle and takes a lot of time which is spent seeking the proper person for supervision. The amount of time it takes can better be spent doing the bulk of the research resulting in a more quality product. Thus, the goal of this project is to develop a system that can recommend potential supervisors from the UiTM list of experts that consists of the faculties available at UiTM Tapah for students that are looking to do research, based on their title and abstract of the research paper. By implementing machine learning content-based filtering to filter the similarities using cosine similarity algorithm and web scraping to obtain the needed supervisor dataset, the system can utilize this information and recommend the suitable potential supervisor for the student. The project uses the waterfall model in order to allow better control throughout the given time. Python is the main language used for the project. Python’s flask web framework is used to develop the interface of the system and selenium for the web automation used for scraping the supervisor data. The system was tested based on several factors that can affect the accuracy of the recommendation results using the dataset of research papers titles and abstracts. Based on the results, most of the recommendation were successful overall. By using this information, the most optimal approach can be used to provide the best recommendation based on the most influential factors tested. In terms of future work, an implementation of collaborative filtering can be used to learn data from past recommendation of users but would need a large dataset of users to work properly.
format	Thesis
qualification_level	Bachelor degree
author	Ahmad Muzzlini, Ahmad Adam
author_facet	Ahmad Muzzlini, Ahmad Adam
author_sort	Ahmad Muzzlini, Ahmad Adam
title	UiTM research supervisor recommendation system / Ahmad Adam Ahmad Muzzlini
title_short	UiTM research supervisor recommendation system / Ahmad Adam Ahmad Muzzlini
title_full	UiTM research supervisor recommendation system / Ahmad Adam Ahmad Muzzlini
title_fullStr	UiTM research supervisor recommendation system / Ahmad Adam Ahmad Muzzlini
title_full_unstemmed	UiTM research supervisor recommendation system / Ahmad Adam Ahmad Muzzlini
title_sort	uitm research supervisor recommendation system / ahmad adam ahmad muzzlini
granting_institution	Universiti Teknologi MARA, Perak
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2022
url	https://ir.uitm.edu.my/id/eprint/58951/1/58951.pdf
_version_	1783735007892733952

UiTM research supervisor recommendation system / Ahmad Adam Ahmad Muzzlini

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