Publication:
Sentiment analysis and usability model for Mysejahtera’s contact tracing application

cris.virtual.department#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.department3d5ebfad-81a5-413e-8c88-1f4731e32d93
cris.virtualsource.orcid3d5ebfad-81a5-413e-8c88-1f4731e32d93
dc.contributor.authorRafidah Isa
dc.contributor.supervisorMohamad Fauzan Noordin
dc.contributor.supervisorRoslina Othman
dc.contributor.supervisorHazwani Mohd Mohadis
dc.date.accessioned2025-01-03T02:50:06Z
dc.date.available2025-01-03T02:50:06Z
dc.date.issued2024
dc.description.abstractInfectious disease crisis management relies heavily on contact tracing applications to curb transmission. With advancements in technology, smartphones, and mobile applications have revolutionized the process, enabling rapid identification and notification of individuals exposed to infectious diseases. These contact tracing applications, such as Malaysia's MySejahtera, provide real-time data to health authorities, facilitating targeted interventions and reducing disease transmission. Understanding user sentiments toward these applications is critical to improving their effectiveness and acceptance. However, there remains a significant research gap regarding sentiment analysis, influencing factors, and usability models specific to Malaysia. This research investigates user sentiment polarity toward contact tracing applications, identifies factors driving positive and negative sentiments, and proposes a usability model tailored to these factors. The methodology involves sentiment analysis using supervised binary classification algorithms and thematic analysis of user reviews of MySejahtera. Key factors influencing sentiment are identified and integrated into a usability model, developed deductively from Hoehle and Venkatesh’s (2015) framework and relevant literature. The findings indicate that the K-Nearest Neighbour (K-NN) algorithm achieves the best performance with an F-measure of 83.99%. The majority of users express positive sentiment, attributed to factors such as User Interface Input, Application Utility, and Application Design. Negative sentiment, however, is also linked to Application Utility and Design. The proposed usability model comprises seven higher-order constructs and fourteen lower-order constructs, which were validated statistically to confirm its robustness. This research contributes significantly to the fields of sentiment analysis and usability by providing actionable insights for developers and public health authorities. The usability model offers a comprehensive framework for improving contact tracing applications, addressing user concerns, and optimizing usability to enhance public health outcomes.
dc.description.abstractarabicFormat not supported
dc.description.callnumberet QA 76.9 S57 R13S 2024
dc.description.cpsemailcps2u@iium.edu.my
dc.description.degreelevelDoctoral
dc.description.emailrafidah.isa81@gmail.com
dc.description.identifierThesis : Sentiment analysis and usability model for Mysejahtera’s contact tracing application / by Rafidah Isa
dc.description.identityG1726284Rafidahisa
dc.description.kulliyahKulliyyah of Information and Communication Technology
dc.description.nationalityMALAYSIA
dc.description.notesThesis (Ph.D)--International Islamic University Malaysia, 2024.
dc.description.physicaldescription1 online resource (xv, 241 leaves) ; color illustrations.
dc.description.programmeDoctor of Philosophy (Information Technology)
dc.identifier.urihttps://studentrepo.iium.edu.my/handle/123456789/32611
dc.language.isoen
dc.publisherKuala Lumpur : Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, 2024
dc.rightsOWNED BY STUDENT
dc.subject.lcshSentiment analysis
dc.subject.lcshNatural language processing (Computer science)
dc.titleSentiment analysis and usability model for Mysejahtera’s contact tracing application
dc.typedoctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#

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