E-Prosiding Persidangan Serantau Manasik Haji 2025 (MANASIK2025)

MANASIK2025 Putrajaya, 7-8 Oktober 2025 424 10.0 KESIMPULAN Kajian ini menekankan keperluan Tabung Haji untuk memanfaatkan kecerdasan buatan sebagai alat strategik dalam memahami persepsi dan kepuasan jemaah. Walaupun berbentuk konseptual, model yang dicadangkan membuka ruang penyelidikan lanjutan untuk menguji keberkesanan analisis sentimen berasaskan AI dalam konteks sebenar. Integrasi teknologi ini bukan sahaja membantu Tabung Haji meningkatkan kualiti perkhidmatan, malah memastikan pengurusan haji di Malaysia terus menjadi model unggul dunia Islam — selari dengan aspirasi Digital Ummah dan prinsip maqasid al-shariah . Penghargaan: Saya Mohd Farihal Osman Pekta ingin merakamkan ucapan terima kasih kepada Tabung Haji atas memberi peluang untuk membentangkan kertas konsep ini. RUJUKAN 1. Ahmad, N., & Hashim, R. (2021). Customer satisfaction towards Hajj management services: A study of Malaysian pilgrims . Journal of Islamic Economics and Business , 8(2), 112 – 127. 2. Abu, A. B., & Zakaria, B. (2013). Title of the manuscript in the journal. Applied Materials and Mechanics, 185, 517 – 521.. 3. Cambria, E., Poria, S., Hazarika, D., & Kwok, K. (2022). SenticNet 7: A commonsense-based neurosymbolic AI framework for sentiment analysis . Information Fusion , 87, 49 – 70. 4. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology . MIS Quarterly , 13(3), 319 – 340. 5. Hassan, M., & Haron, R. (2023). Artificial Intelligence applications in Islamic finance: A systematic literature review . Journal of Islamic Accounting and Business Research , 14(1), 77 – 96. 6. Ibrahim, A. (2023). Ethical dimensions of AI in Islamic institutions: A Maqasid al-Shariah approach . International Journal of Ethics and Technology in Islam , 2(1), 45 – 60. 7. Liu, B. (2020). Sentiment analysis: Mining opinions, sentiments, and emotions . Cambridge University Press. 8. Nasir, M. N., Zainuddin, N., & Yusof, H. (2024). A systematic literature review of sentiment analysis in the Malay language and its approach . Journal of Computing and AI Research , 6(1), 22 – 39. 9. Rahman, A., Fauzi, F., & Mohd, S. (2020). A conceptual model of depositors’ trust and loyalty on Hajj institution: Case of Lembaga Tabung Haji Malaysia . Journal of Islamic Marketing , 11(3), 785 – 799. 10. Khan, A., Atique, M., & Thakare, V. M. (2015). Combining lexicon-based and learning- 109 based methods for Twitter sentiment analysis. Special Issue of International Journal of Electronics, Communication & Soft Computing Science and Engineering (IJECSCSE), 89 – 96. 11. Kouloumpis, E., Wilson, T., & Moore, J. (2011). Twitter sentiment analysis: The good the bad and the omg! Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, USA., 538 – 541. 12. Neri, F., Aliprandi, C., Capeci, F., Cuadros, M., & By, T. (2012). Sentiment analysis on social media. Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, 919 – 926. 13. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and 112 Trends in Information Retrieval, Vol. 2, No, 1 – 135. 14. Troussas, C., Virvou, M., Espinosa, K. J., Llaguno, K., & Caro, J. (2013). Sentiment analysis of Facebook statuses using Naïve Bayes Classifier for language learning. 4th International Conference on Information, Intelligence, Systems and Applications (IISA), 198 – 205. 15. Brody, J.G, Dunagan, SC, Morello-Frosch, R., Brown, P, Patton, S, Rudel, R.A Reporting individual result for biomonitoring and environment exposures: Lessons learned from environment communication case studies. Environ. Health, 2014, 13, 40 (Cross) (PubMed)