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The purposes of this research are to 1) design and develop the local economy data analytical system for support decision making using data mining techniques and 2) evaluate the system performance. The performance evaluation is assessed by experts 3 persons and the officer of the subdistrict administration organization 15 persons. The statistics used in the research are mean and standard deviation.
The research results showed that the system developed with the Random Tree algorithm is the most efficient with accuracy 98.13%, recall 98.20%, precision 98.10%, and f-measure 98.10%. This can support staff decision making for development planning and help in the community. In the system evaluation, the results show that the system achieves a good ranking ( = 4.37, SD = 0.60).
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