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In this study, an efficiency comparison in prediction of Khao Dok Mali 105 paddy rice classification with data mining techniques was compared. The seven classification methods were the followings: (1) k-nearest neighbor method using IBk algorithm; (2) decision tree method using J48 algorithm; (3) artificial neural network method using multilayer perceptron algorithm; (4) support vector machine method using polynomial kernel; (5) rule-based method using decision table algorithm; (6) binary logistic regression method; and (7) naïve Bayes method. The following efficiency comparisons of classification were employed: accuracy, recall, F-measure, and mean square error (MSE). The important results are as follows. The k-nearest neighbor method using random seed = 10, 20 and 30 showed the best accuracy, recall, F-measure, and MSE at 100 %, 1.000, 1.000 and 0.00002 respectively. The support vector machine method and rule-based using random seed = 10, and 20 exhibited the best recall at 1.000. Since the k-nearest neighbor method offered the best efficiencies for all the 4 values, it was considered the best prediction method.
 Seekuka, J, 2014, Features for Classifying Rice Grains by Image Analysis, Master Thesis, Kasetsart University, Bangkok, 52 p. (in Thai)
 Ruangphayak, S., 2016, Checking the Purity and Quality of Rice Quickly by KASP SNPline Detection, Rice Science Center, Kasetsart University, Bangkok, 54 p. (in Thai)
 Aluru S., 2011, Morphology based feature extraction and recognition for enhanced wheat quality evaluation, Int. Conf. Contemp. Comp. 4: 41-50.
 Zhao-yan, C.F.L., Ying, Y. and Rao, X., 2005, Identification of rice seed varieties using neural network, J. Zhejiang Univ. Sci. 6: 1095-1100.
 Kongseri, N., Bangvarg, J., Cheapun, K., Wongpiyachon, S., Sukvivat, V., Sawangjit, P. and Tangvisuttijit, S., 2004, Quality and Investigation of Thai Jasmine Rice, Academic Agriculture Department, Ministry of Agriculture and Cooperatives, 62 p. (in Thai)
 Vanavichit, A., Tragoonrung, S. and Toojinda, T., 2003, Biotechnology and Rice Varieties Improvement, Chap. in Science and Technology with Thai Rice, Thailand’s National Science and Technology Development Agency, 85 p. (in Thai)
 Panichkul, P., 2005, Development Data Mining System by Decision Tree, Work System Development Project, Master Thesis, King Montkut’s Institute of Technology Ladkrabang, Bangkok, 62 p. (in Thai)
 Wu, X. and Kumar, V., 2009, The Top Ten Algorithms in Data Mining, University of Minnesota Department of Computer Science and Engineering, CRC Press, Minneapolis, 215 p.
 Thammasombut, R., 2012, Decision Support System for Selection the Mobile Internet Package Using Decision Tree, Major of Business Computer, Faculty of Business Administration, Rajapruek College, Sakon Nakhon, 77 p. (in Thai)
 Berson, A. and Smith, S.J., 1997, Data Warehousing, Data Mining, and OLAP, McGraw-Hill, New York, 612 p.
 Nuipian, V., 2010, Comparison of efficiency and analysis of data classification using artificial neural network, support vector machine, Naïve Bayes and k-nearest neighbor, Natl. Conf. Comp. Inform. Technol. 5: 131-138. (in Thai)
 Murti, S. and Mahantappa, M., 2012, Using rule based classifiers for the predictive analysis of breast cancer recurrence, J. Inform. Eng. Appl. 2(2): 12-19.
 Vanichbuncha, K., 2009, Multivariate Analysis, Thammasan Co., Ltd., Bangkok, 589 p. (in Thai)
 Sinsomboonthong, S., 2017, Data Mining 1: Discovering Knowledge in Data, 2nd Ed., Chamchuree Products Co., Ltd., Bangkok, 512 p. (in Thai)
 Larose, D.T., 2005, Discovering Knowledge in Data: An Introduction to Data Mining, John Wiley and Sons, New Jersey, 222 p.