<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The ability to identify the entrepreneurial potential of students enables higher education institutions to contribute to the economic and social development of a country. Current research trends regarding the detection of student entrepreneurial potential have the greatest challenge in the unequal ratio of datasets. This study proposes a rule-generation model in an imbalanced situation to classify student entrepreneurship based on the Theory of Planned Behavior (TPB). The result is a ruleset that is used for the early detection of student entrepreneurial potential. The proposed method consists of three main stages, namely preprocessing data to classify data based on TPB variables, generating a dataset by clustering and selecting attributes by sampling to balance the data, and finally generating a ruleset. Furthermore, the results of the detecting ruleset have been evaluated with actual data from the student tracer study as ground truth. The evaluation results show high accuracy so that the ruleset can be applied to the higher education environment in the future.</p>
</abstract>

The ability to identify the entrepreneurial potential of students enables higher education institutions to contribute to the economic and social development of a country. Current research trends regarding the detection of student entrepreneurial potential have the greatest challenge in the unequal ratio of datasets. This study proposes a rule-generation model in an imbalanced situation to classify student entrepreneurship based on the Theory of Planned Behavior (TPB). The result is a ruleset that is used for the early detection of student entrepreneurial potential. The proposed method consists of three main stages, namely preprocessing data to classify data based on TPB variables, generating a dataset by clustering and selecting attributes by sampling to balance the data, and finally generating a ruleset. Furthermore, the results of the detecting ruleset have been evaluated with actual data from the student tracer study as ground truth. The evaluation results show high accuracy so that the ruleset can be applied to the higher education environment in the future.

A Rule-Generation Model for Class Imbalances to Detect Student Entrepreneurship Based on the Theory of Planned Behavior

Department of Informatic Engineerings, Faculty of Computer Science

Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology

Department of Computer Engineering, Faculty of Intelligent Electrical and Informatics Technology

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The ability to identify the entrepreneurial potential of students enables higher education institutions to contribute to the economic and social development of...

A Rule-Generation Model for Class Imbalances to Detect Student Entrepreneurship Based on the Theory of Planned Behavior

Publié en ligne: 23 juin 2022

Pages: 160 - 178

Reçu: 19 oct. 2021

Accepté: 12 mars 2022

DOI: https://doi.org/10.2478/cait-2022-0023

Mots clés
Rule generating model, student entrepreneurial potential detection, imbalanced data, theory of planned behavior

© 2022 Nova Rijati et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Rule-Generation Model for Class Imbalances to Detect Student Entrepreneurship Based on the Theory of Planned Behavior

Publié en ligne: 23 juin 2022

Pages: 160 - 178

Reçu: 19 oct. 2021

Accepté: 12 mars 2022

DOI: https://doi.org/10.2478/cait-2022-0023

Mots clésRule generating model, student entrepreneurial potential detection, imbalanced data, theory of planned behavior

© 2022 Nova Rijati et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Rule generating model, student entrepreneurial potential detection, imbalanced data, theory of planned behavior