The Comparison of Data Classification Efficiency to Predict Decision Making in the Future of Election for Thai Businessman by Data Mining Techniques
Keywords:
Decision tree, naïve bayes, logistic regression, random forestAbstract
This research aimed to compare the model efficiency used for prediction of the decision making in the future of election for Thai businessman. The data was collected from the decision making in the future of election for 7 region throughout Thailand businessman. The dataset had 6 attributes and 2,917 records. The RapidMiner Studio program 10.1 was used to find the number of relations attributes and model, and the 10-Fold Cross Validation to evaluate the model: Decision Tree, Naïve Bayes, Logistic Regression, Deep Learning and Random Forest.
The results showed that the accuracy of Random Forest, Decision Tree, Naïve Bayes and Logistic Regression were at 88.85%, 88.51%, 88.46% and 88.11 respectively. The research could be used to predict, to analyze election campaign of the parties and to develop an information system to support the policy making for developing country.
References
De Clercq, D., Haq, I. U., Azeem, M. U., & Ahmad, H. N. (2019). The relationship between workplace incivility and helping behavior: roles of job dissatisfaction and political skill. The Journal of psychology, 153(5), 507-527.
Good, M.C. & Schwepker, C. H. (2022). Business-to-business salespeople and political skill: Relationship building, deviance, and performance. Journal of Business Research, 139(2022), 32-43.
Han, J., Pei, J. & Tong, (2022). Data mining concepts and techniques (4th ed.). Morgan Kaufman Publishers. Retrieved from https://gsmis.snru.ac.th/e-thesis/file_att1/ 2023031063426423118_fulltext.pdf (in Thai)
Jaroenpuntaruk, W. (2015). Date Warehouse, Data Mining and Business Intelligence. Nonthaburi: Sukhothai Thammathirat Open University. (in Thai).
limmane, A. (2015). State society and change: A consideration on power, policy, and relationship network (2nd ed). Bangkok: Siamparitas. (in Thai)
Plangsree, S. (2022). Factors affecting people’s decision to vote for the council member and administrators of Nakhon Phanom local government organzations (master’s thesis). Sakon Nakhon Rajabhat University, Sakon Nakhon, Thailand.
Riera, P., & Cantú, F. (2022). Electoral systems and ideological voting. European Political Science Review, 14(4), 463-481.
Sinsomboonthong, S. (2015). Data Mining (1st ed). Bangkok: Chamchuri Product. (in Thai)
Teeravakin, L. (2009, May 13). Role of business persons of political. Retrieved from https://mgronline.com/daily/detail/9520000053345
Witten, I. H., Frank, E. & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques (3rd ed.). Burlington, Massachusetts: Morgan Kaufmann Publishers.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Local Administration Journal
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The copyright of all articles published in the Local Administration Journalis owned by the College of Local Administration, Khon Kaen University.
