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All Journal International Journal of Electrical and Computer Engineering MEDIA KONSERVASI
Yap, Timothy Tzen Vun
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Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering

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Perceived Impact of Music Tourism and Support for Music Tourism among Local Communities: The Moderating Effect of Psychological Egoism Choong, Yuen Onn; Kuek, Thiam Yong; Chai, Bobby Boon Hui; Khor, Saw Chin; Low, Mei Peng; Yap, Timothy Tzen Vun
Media Konservasi Vol. 29 No. 3 (2024): Issue topic: Conservation of Nature and Culture Through Responsible Tourism
Publisher : Department of Forest Resources Conservation and Ecotourism - IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/medkon.29.3.355

Abstract

Music tourism, a growing segment of the tourism industry, has significant socio-economic benefits but also poses environmental challenges. This study investigates the relationship between the perceived impact of music tourism and local community support in Kuala Lumpur, Malaysia, with a particular focus on the moderating effect of psychological egoism. Using Partial Least Square Structural Equation Modeling (PLS-SEM) and survey data from 134 local residents, we found that positive perceived impacts of tourism correlate with higher community support. However, psychological egoism influences this relationship, as individuals with higher egoism levels still support music tourism despite recognizing its negative impacts. These findings highlight the need for targeted educational campaigns to promote environmental sustainability and community well-being. Local governments and event organizers should focus on enhancing the positive impacts and mitigating the negative ones to foster sustainable tourism practices that align with conservation goals and contribute to UNESCO Sustainable Development Goal 8.
Handling class imbalance in education using data-level and deep learning methods Kannan, Rithesh; Ng, Hu; Yap, Timothy Tzen Vun; Wong, Lai Kuan; Chua, Fang Fang; Goh, Vik Tor; Lee, Yee Lien; Wong, Hwee Ling
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp741-754

Abstract

In the current field of education, universities must be highly competitive to thrive and grow. Education data mining has helped universities in bringing in new students and retaining old ones. However, there is a major issue in this task, which is the class imbalance between the successful students and at-risk students that causes inaccurate predictions. To address this issue, 12 methods from data-level sampling techniques and 2 methods from deep learning synthesizers were compared against each other and an ideal class balancing method for the dataset was identified. The evaluation was done using the light gradient boosting machine ensemble model, and the metrics included receiver operating characteristic curve, precision, recall and F1 score. The two best methods were Tomek links and neighbourhood cleaning rule from undersampling technique with a F1 score of 0.72 and 0.71 respectively. The results of this paper identified the best class balancing method between the two approaches and identified the limitations of the deep learning approach.
Co-Authors Chai, Bobby Boon Hui Choong, Yuen Onn Chua, Fang Fang Goh, Vik Tor Hu Ng Kannan, Rithesh Khor, Saw Chin Kuek, Thiam Yong Lee, Yee Lien Low, Mei Peng Wong, Hwee Ling Wong, Lai Kuan