A Hybrid MOO, MCGDM, and Sentiment Analysis Methodologies for Enhancing Regional Expansion Planning: A Case Study Luwu-Indonesia

Faisal, Muhammad and Irmawati and T.K.A, Rahman and Jufri and Sahabudin and Herlinah and Mulyadi, Ida (2025) A Hybrid MOO, MCGDM, and Sentiment Analysis Methodologies for Enhancing Regional Expansion Planning: A Case Study Luwu-Indonesia. International Journal of Mathematical, Engineering and Management Sciences, 10 (1). pp. 163-188.

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Abstract

The study introduces a new framework integrating Multi Objective Optimization (MOO), Multi Criteria Group Decision Making (MCGDM), and sentiment analysis to improve regional expansion planning, particularly in developing countries. The framework addresses challenges such as governance, resource allocation, and public inclusion, aiming to make decision-making more transparent and adaptive. The study involves government agencies, experts, and communities using relevant variables. The model integrated 12 factors and 27 criteria within the MOO, encompassing 5 regions and 25 subdistricts in the MCGDM. Additionally, it analyzed 900 citizen comments through sentiment analysis and employed 9 indicators from 15 regions. The results demonstrated high accuracy, achieving a score of 0.98. The study demonstrates that the hybrid approach optimizes planning by balancing technical criteria and public sentiment, leading to more inclusive outcomes. The study lays a solid foundation for future research and practical application, making valuable contributions to the field of decision science, regional planning, and beyond.

Item Type:	Journal
Uncontrolled Keywords:	MOO, MCGDM, Machine learning, Sentiment analysis, Region expansion
Divisions:	School of Science and Technology
Depositing User:	Muhamad Aizat Nazmi Mohd Nor Hamin
Date Deposited:	24 Aug 2026 09:14
Last Modified:	24 Aug 2026 09:14
URI:	http://ur.aeu.edu.my/id/eprint/1512

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