Data Mining Development to Enhance Production and Marketing for Ton Phueng Home Forest Community Enterprise, Ton Phueng Sub-District, Phang Khon District, Sakon Nakhon Province
Article Sidebar
Main Article Content
Abstract
This study aimed to (1) develop a prototype dashboard innovation for planning production and marketing processes using data mining, and (2) evaluate the efficiency of the prototype dashboard for a local community enterprise group in Ton Phueng Sub-district, Phang Khon District, Sakon Nakhon Province. The objective was to enhance production planning based on consumer demand across different periods, support marketing and promotional activities, increase income, and guide the development of new products tailored to consumer needs. Sales data from the enterprise group were collected using Google Forms and analyzed through the CRISP-DM data mining process using RapidMiner Studio. The decision tree algorithm (ID3) was employed to classify sales volumes into three categories: low, medium, and high. The experimental results indicated that the ID3 decision tree model achieved an accuracy of 73.08%. The rules generated by the decision tree were then applied as filtering conditions on the dashboard to summarize sales data, supporting strategic production and marketing planning. The user satisfaction evaluation revealed that the overall satisfaction level with the dashboard was at the highest level ( = 4.70, SD = 0.47).
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Strategic and Provincial Development Group, "Sakon Nakhon Provincial Development Plan 5 years (2018–2022), revised edition for fiscal year 2021," Sakon Nakhon: Sakon Nakhon Provincial Office, 2021, pp. 118. (in Thai)
C. Petchphan, Fundamental data, problems, and requirements of Ton Phueng Family Forest Community
Enterprise [Interview]. Chairman of Ton Phueng Family Forest Community Enterprise; 12 November 2022.
(in Thai)
D. Nattakan, "The Development of Business Intelligence System for Promotion Analysis on Consumer Behavior by using Data Mining," M.S. special problem, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand, 2017. (in Thai)
B. Abdulloh, P. Sulaiman, L. Ismaae, A. Attapol, P. Pannee, and D. Fudailah, "Data Mining Techniques by using Association Rules for Purchasing Behavior Analysis of Retail Customers: A case study of D2Shop, Pattani Province," Journal of Technology Management, Maha Sarakham Rajabhat University., vol. 9, no. 2, pp. 18–29, 2022. (in Thai)
J. Nivea, "Fraud detection model in imbalanced data using dimension reduction and machine lerning algorithms," M.S. thesis, Sripatum University, Bangkok, Thailand, 2017. (in Thai)
P. Chapman et al., "CRISP-DM 1.0: Step-by-step data mining guide," SPSS Inc., 2000.
R. Wirth and J. Hipp, "CRISP-DM: Towards a standard process model for data mining," in Proc. 4th Int.
Conf. Practical Appl. Knowl. Discov. Data Mining, 2000, pp. 29–39.
M. Shearer, "The CRISP-DM model: The new blueprint for data mining," J. Data Warehousing, vol. 5, no. 4,
pp. 13–22, 2000.
J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Morgan Kaufmann, 2011.Atisin
T. Fawcett, "An introduction to ROC analysis," Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, 2006.
A. K. A. Tufféry, Data Mining and Statistics for Decision Making, Wiley, 2011.
S. Thidarat, C. Atisin, E. Prakong, T. Weerawut, and B. Wipawan, " The Study of the Students Behavior from the Faculty of Science and Technology Phuket Rajabhat University in Consuming the Food and Beverage using Data Mining," Proceedings of the 12th National and International Research Conference: Global Goals, Local Actions: Looking Back and Moving Forward 2022, pp. 488-499, Online Conference, Suan Sunandha Rajabhat University, 2022. (in Thai)
P. Nuthapong, K. Ponthep, B. Wipawan, and J. Somjai, " Food Recommended System Using Association Rule Technique," PKRU SciTech Journal, vol. 4, no. 1, pp. 1–12, 2020. (in Thai)
J. R. Quinlan, Induction of decision trees, Mach. Learn., vol.1, no.1, pp.81-106, 1986.
