Designing a Recommender System based on the Application of Decision Tree Algorithm in Data Mining with KNIME (For Recommending the Topic of Undergraduate’s Thesis)

Sari, Yenny and Prasetyo, Vincentius Riandaru and Liyansah, Kevin (2022) Designing a Recommender System based on the Application of Decision Tree Algorithm in Data Mining with KNIME (For Recommending the Topic of Undergraduate’s Thesis). In: INTERNATIONAL CONFERENCE ON INFORMATICS, TECHNOLOGY, AND ENGINEERING 2021 (InCITE 2021): Leveraging Smart Engineering, 25 - 26 August 2021, Surabaya, Indonesia (Virtual/Online).

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Official URL / DOI: https://doi.org/10.1063/5.0081214

Abstract

Data mining has many algorithms; one of the most frequently used is the decision tree algorithm. This article described the result of the research of a data mining approach that used the decision tree algorithm; it aimed to create and provide a recommendation system for university students to choose and decide the topic for their final projects. The final project (so-called as thesis) is one of the graduation requirements for undergraduate students; many university students are confused in choosing and deciding the right topic for their thesis, and this results in not completing it in the required time. The impact of overdue thesis is the extension of their period of studies. In this study, data mining was carried out based on 9 attribute data sets which were reduced from 21 derived factors. The data processing of the research has used the free software so-called KNIME. It was started with attribute correlation testing; it was continued by formulating KNIME workflow and resulted in the decision tree. Then, the tree diagrams were translated into the 65 IF-THEN rules. Based on these 65 IF-THEN rules, this study developed a recommender system using C# language; the recommendation system was tested on 45 students which gave its accuracy more than 70%.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: data mining, decision tree algorithm, recommender system, KNIME workflow
Subjects: Q Science > Q Science (General)
Divisions: Faculty of Engineering > Department of Industrial Engineering
Depositing User: Yenny Sari 61147
Date Deposited: 12 May 2022 04:39
Last Modified: 12 May 2022 04:39
URI: http://repository.ubaya.ac.id/id/eprint/41829

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