Implementation of the ANN and TOPSIS Methods for Recommending Topics and Thesis Supervisors

Prasetyo, Vincentius Riandaru and Averina, Anasthasya and Asmawati, Endah (2024) Implementation of the ANN and TOPSIS Methods for Recommending Topics and Thesis Supervisors. In: 2024 International Seminar on Intelligent Technology and Its Applications (ISITIA 2024), 10 – 12 July 2024, Mataram.

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Abstract

A thesis is a scientific work that determines a student's graduation from university. The thesis title is based on specific learning topics according to the field of science pursued by the student. Most students in the Informatics Engineering Study Program at the University of Surabaya take 2.5 months to find an initial thesis topic idea and 1.5 months to find a supervisor. To avoid delays in the thesis writing process, a thesis topic and supervisor lecturer recommendation system were created to help students find thesis topics and supervisors more quickly. This research also aims to evaluate the combination of Artificial Neural Network (ANN) and TOPSIS methods implemented in the proposed recommendation system. The ANN method model training process uses a 2015-2020 graduate student dataset comprising 22 compulsory course grades, a list of 22 elective courses, and labeling 14 general thesis topics. The thesis topic recommendation output will be used to calculate supervisor recommendations using the TOPSIS method with the criteria of lecturer guidance quota, lecturer program, lecturer topic expertise, and students' personal preferences. The ANN model validation resulted in an average accuracy of 88%. Meanwhile, for evaluating the thesis supervisor's recommendations, the calculations produced by the system are the results of the manual calculations carried out.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	ANN, recommendations, thesis supervisor, thesis topic, TOPSIS
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	VINCENTIUS RIANDARU PRASETYO
Date Deposited:	18 Nov 2024 05:03
Last Modified:	18 Nov 2024 05:03
URI:	http://repository.ubaya.ac.id/id/eprint/47410

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