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Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand “ThaiChana”

Author

Listed:
  • Ardvin Kester S. Ong

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Thanatorn Chuenyindee

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    Department of Industrial Engineering and Aviation Management, Navaminda Kasatriyadhiraj Royal Air Force Academy, Bangkok 10220, Thailand)

  • Yogi Tri Prasetyo

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Road, Taoyuan 32003, Taiwan)

  • Reny Nadlifatin

    (Department of Information Systems, Institute Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia)

  • Satria Fadil Persada

    (Entrepreneurship Department, BINUS Business School Undergraduate Program, Bina Nusantara University, Malang 65154, Indonesia)

  • Ma. Janice J. Gumasing

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Josephine D. German

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
    School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Kirstien Paola E. Robas

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Michael N. Young

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Thaninrat Sittiwatethanasiri

    (Department of Industrial Engineering and Aviation Management, Navaminda Kasatriyadhiraj Royal Air Force Academy, Bangkok 10220, Thailand)

Abstract

The continuous rise of the COVID-19 Omicron cases despite the vaccination program available has been progressing worldwide. To mitigate the COVID-19 contraction, different contact tracing applications have been utilized such as Thai Chana from Thailand. This study aimed to predict factors affecting the perceived usability of Thai Chana by integrating the Protection Motivation Theory and Technology Acceptance Theory considering the System Usability Scale, utilizing deep learning neural network and random forest classifier. A total of 800 respondents were collected through convenience sampling to measure different factors such as understanding COVID-19, perceived severity, perceived vulnerability, perceived ease of use, perceived usefulness, attitude towards using, intention to use, actual system use, and perceived usability. In total, 97.32% of the deep learning neural network showed that understanding COVID-19 presented the most significant factor affecting perceived usability. In addition, random forest classifier produced a 92% accuracy with a 0.00 standard deviation indicating that understanding COVID-19 and perceived vulnerability led to a very high perceived usability while perceived severity and perceived ease of use also led to a high perceived usability. The findings of this study could be considered by the government to promote the usage of contact tracing applications even in other countries. Finally, deep learning neural network and random forest classifier as machine learning algorithms may be utilized for predicting factors affecting human behavior in technology or system acceptance worldwide.

Suggested Citation

  • Ardvin Kester S. Ong & Thanatorn Chuenyindee & Yogi Tri Prasetyo & Reny Nadlifatin & Satria Fadil Persada & Ma. Janice J. Gumasing & Josephine D. German & Kirstien Paola E. Robas & Michael N. Young & , 2022. "Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand “ThaiChana”," IJERPH, MDPI, vol. 19(10), pages 1-24, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:6111-:d:817759
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    References listed on IDEAS

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    1. Pal, Debajyoti & Vanijja, Vajirasak, 2020. "Perceived usability evaluation of Microsoft Teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in India," Children and Youth Services Review, Elsevier, vol. 119(C).
    2. Essam A. Rashed & Akimasa Hirata, 2021. "Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling," IJERPH, MDPI, vol. 18(15), pages 1-15, July.
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    Cited by:

    1. Ardvin Kester S. Ong, 2022. "A Machine Learning Ensemble Approach for Predicting Factors Affecting STEM Students’ Future Intention to Enroll in Chemistry-Related Courses," Sustainability, MDPI, vol. 14(23), pages 1-17, December.
    2. Maela Madel L. Cahigas & Ardvin Kester S. Ong & Yogi Tri Prasetyo, 2023. "Super Typhoon Rai’s Impacts on Siargao Tourism: Deciphering Tourists’ Revisit Intentions through Machine-Learning Algorithms," Sustainability, MDPI, vol. 15(11), pages 1-29, May.
    3. Ma. Janice J. Gumasing & Yogi Tri Prasetyo & Ardvin Kester S. Ong & Reny Nadlifatin & Satria Fadil Persada, 2022. "Determining Factors Affecting the Perceived Preparedness of Super Typhoon: Three Broad Domains of Ergonomics Approach," Sustainability, MDPI, vol. 14(19), pages 1-29, September.
    4. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Nattakit Yuduang & Reny Nadlifatin & Satria Fadil Persada & Kirstien Paola E. Robas & Thanatorn Chuenyindee & Thapanat Buaphiban, 2022. "Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand," IJERPH, MDPI, vol. 19(13), pages 1-28, June.
    5. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Ralph Andre C. Roque & Jan Gabriel I. Garbo & Kirstien Paola E. Robas & Satria Fadil Persada & Reny Nadlifatin, 2022. "Determining the Factors Affecting a Career Shifter’s Use of Software Testing Tools amidst the COVID-19 Crisis in the Philippines: TTF-TAM Approach," Sustainability, MDPI, vol. 14(17), pages 1-24, September.
    6. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Godwin M. Bagon & Christian Hope S. Dadulo & Nathaniel O. Hortillosa & Morrissey A. Mercado & Thanatorn Chuenyindee & Reny Nadlifatin & Satria Fadil Persada, 2022. "Investigating Factors Affecting Behavioral Intention among Gym-Goers to Visit Fitness Centers during the COVID-19 Pandemic: Integrating Physical Activity Maintenance Theory and Social Cognitive Theory," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    7. Josephine D. German & Anak Agung Ngurah Perwira Redi & Ardvin Kester S. Ong & Yogi Tri Prasetyo & Vince Louis M. Sumera, 2022. "Predicting Factors Affecting Preparedness of Volcanic Eruption for a Sustainable Community: A Case Study in the Philippines," Sustainability, MDPI, vol. 14(18), pages 1-24, September.
    8. Nattakit Yuduang & Ardvin Kester S. Ong & Nicole B. Vista & Yogi Tri Prasetyo & Reny Nadlifatin & Satria Fadil Persada & Ma. Janice J. Gumasing & Josephine D. German & Kirstien Paola E. Robas & Thanat, 2022. "Utilizing Structural Equation Modeling–Artificial Neural Network Hybrid Approach in Determining Factors Affecting Perceived Usability of Mobile Mental Health Application in the Philippines," IJERPH, MDPI, vol. 19(11), pages 1-19, May.

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