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Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand

Author

Listed:
  • Ardvin Kester S. Ong

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

  • 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, Chung-Li 32003, Taiwan)

  • Nattakit Yuduang

    (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)

  • Reny Nadlifatin

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

  • Satria Fadil Persada

    (Entrepreneurship Department, BINUS Business School Undergraduate Program, Bina Nusantara University, Jakarta 11480, Indonesia)

  • Kirstien Paola E. Robas

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

  • Thanatorn Chuenyindee

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

  • Thapanat Buaphiban

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

Abstract

With the constant mutation of COVID-19 variants, the need to reduce the spread should be explored. MorChana is a mobile application utilized in Thailand to help mitigate the spread of the virus. This study aimed to explore factors affecting the actual use (AU) of the application through the use of machine learning algorithms (MLA) such as Random Forest Classifier (RFC) and Artificial Neural Network (ANN). An integrated Protection Motivation Theory (PMT) and the Unified Theory of Acceptance and Use of Technology (UTAUT) were considered. Using convenience sampling, a total of 907 valid responses from those who answered the online survey were voluntarily gathered. With 93.00% and 98.12% accuracy from RFC and ANN, it was seen that hedonic motivation and facilitating conditions were seen to be factors affecting very high AU; while habit and understanding led to high AU. It was seen that when people understand the impact and causes of the COVID-19 pandemic’s aftermath, its severity, and also see a way to reduce it, it would lead to the actual usage of a system. The findings of this study could be used by developers, the government, and stakeholders to capitalize on using the health-related applications with the intention of increasing actual usage. The framework and methodology used presented a way to evaluate health-related technologies. Moreover, the developing trends of using MLA for evaluating human behavior-related studies were further justified in this study. It is suggested that MLA could be utilized to assess factors affecting human behavior and technology used worldwide.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:7979-:d:851555
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    References listed on IDEAS

    as
    1. Paul M. Garrett & Yu-Wen Wang & Joshua P. White & Yoshihsa Kashima & Simon Dennis & Cheng-Ta Yang, 2022. "High Acceptance of COVID-19 Tracing Technologies in Taiwan: A Nationally Representative Survey Analysis," IJERPH, MDPI, vol. 19(6), pages 1-15, March.
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    3. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Kerr Lorenzo Picazo & Kim Aaron Salvador & Bobby Ardiansyah Miraja & Yoshiki B. Kurata & Thanatorn Chuenyindee & Reny Nadlifatin & Anak Agung Ngurah Perwira , 2021. "Gym-Goers Preference Analysis of Fitness Centers during the COVID-19 Pandemic: A Conjoint Analysis Approach for Business Sustainability," Sustainability, MDPI, vol. 13(18), pages 1-17, September.
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    6. Nattakit Yuduang & Ardvin Kester S. Ong & Yogi Tri Prasetyo & Thanatorn Chuenyindee & Poonyawat Kusonwattana & Waranya Limpasart & Thaninrat Sittiwatethanasiri & Ma. Janice J. Gumasing & Josephine D. , 2022. "Factors Influencing the Perceived Effectiveness of COVID-19 Risk Assessment Mobile Application “MorChana” in Thailand: UTAUT2 Approach," IJERPH, MDPI, vol. 19(9), pages 1-19, May.
    7. Alam, Mohammad Zahedul & Hu, Wang & Kaium, Md Abdul & Hoque, Md Rakibul & Alam, Mirza Mohammad Didarul, 2020. "Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach," Technology in Society, Elsevier, vol. 61(C).
    8. 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.
    9. Ramon Palau-Saumell & Santiago Forgas-Coll & Javier Sánchez-García & Emilio Robres, 2019. "User Acceptance of Mobile Apps for Restaurants: An Expanded and Extended UTAUT-2," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    10. Thanatorn Chuenyindee & Ardvin Kester S. Ong & Yogi Tri Prasetyo & Satria Fadil Persada & Reny Nadlifatin & Thaninrat Sittiwatethanasiri, 2022. "Factors Affecting the Perceived Usability of the COVID-19 Contact-Tracing Application “Thai Chana” during the Early COVID-19 Omicron Period," IJERPH, MDPI, vol. 19(7), pages 1-16, April.
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    Cited by:

    1. 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.
    2. Yoshiki B. Kurata & Ardvin Kester S. Ong & Christienne Joie C. Andrada & Mariela Nicole S. Manalo & Errol John Aldrie U. Sunga & Alvin Racks Martin A. Uy, 2022. "Factors Affecting Perceived Effectiveness of Multigenerational Management Leadership and Metacognition among Service Industry Companies," Sustainability, MDPI, vol. 14(21), pages 1-23, October.
    3. Ardvin Kester S. Ong & Jelline C. Cuales & Jose Pablo F. Custodio & Eisley Yuanne J. Gumasing & Paula Norlene A. Pascual & Ma. Janice J. Gumasing, 2023. "Investigating Preceding Determinants Affecting Primary School Students Online Learning Experience Utilizing Deep Learning Neural Network," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    4. Dana Rad & Lavinia Denisia Cuc & Ramona Lile & Valentina E. Balas & Cornel Barna & Mioara Florina Pantea & Graziella Corina Bâtcă-Dumitru & Silviu Gabriel Szentesi & Gavril Rad, 2022. "A Cognitive Systems Engineering Approach Using Unsupervised Fuzzy C-Means Technique, Exploratory Factor Analysis and Network Analysis—A Preliminary Statistical Investigation of the Bean Counter Profil," IJERPH, MDPI, vol. 19(19), pages 1-19, October.
    5. Poonyawat Kusonwattana & Ardvin Kester S. Ong & Yogi Tri Prasetyo & Klint Allen Mariñas & Nattakit Yuduang & Thanatorn Chuenyindee & Kriengkrai Thana & Satria Fadil Persada & Reny Nadlifatin & Kirstie, 2022. "Predicting Factors Affecting the Intention to Prepare for Mitigation of Man-Made Fire Disasters in Chonburi Province, Thailand: An Integration of Structural Equation Modeling and Artificial Neural Net," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    6. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Kate Nicole M. Tayao & Klint Allen Mariñas & Irene Dyah Ayuwati & Reny Nadlifatin & Satria Fadil Persada, 2022. "Socio-Economic Factors Affecting Member’s Satisfaction towards National Health Insurance: An Evidence from the Philippines," IJERPH, MDPI, vol. 19(22), pages 1-24, November.

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