04040nas|a2200457 a 4500 20260817163102.0 170110s2012 th uu|m rtt 0| a1eng d .b12177635 AIT RSPR no.IM-12-07 Tanawat Sermvongtrakul Applying decision trees, artificial neural networks and support vector machine to classify the potential of gas stations Pathum Thani, Thailand : Asian Institute of Technology, 2012 54 p. : ill. Research studies project report ; no. IM-12-07 A research study submitted in partial fulfillment of the requirements forthedegree of Master of Science inInformation Management, School of Engineering of Technology Research Studies Project Report (M.Sc.) - Asian Institute of Technology, 2012 In oil retail industries, gas stations are builtto serve households and industries. An operation of a gas station has a very high risk of loss. To reduce thatrisk, managements need to consider several factors influencing revenues of the station before building a new gas station.With the rapid development in information technology, many different data mining approaches are applied to support management{u2019}s decisions. This study focuses on using several data mining techniques to classify the potential of gas stations. The potential means the capability of growth or being without loss; therefore, this study uses sales volume as an indicator of the potential of gas stations. Using classification techniques in the data mining candiscover some hidden knowledge on existing gas station data and other related information and the knowledgecan also be used for helping the management making a decision for building the new station,which is very beneficial.This study conducted 3 experiments, which use artificial neural networks, support vector machine and decision trees. The results show that using artificial neural networks hasthe highest accuracy in classifying the potential of gas stations. It is more than 85% accuracy for all models. On the contrary, using support vector machine and decision trees, both of them getloweraccuracy rate on testing data. Based on these results,the artificial neural networks technique can serve as a decision support tool for classify a potential of a new gas station, which can reduce the human error in the decision-making process or even help to the management to make a decision with very high accuracy. Neural networks (Computer science) Decision trees Decision support systems Data mining Guha, Sumanta, Chairperson Vatcharaporn Esichaikul, Examination Committee Duboz, Raphael, Examination Committee Royal Thai Government Fellowship, Scholarship donor Asian Institute of Technology. Research studies project report ; no. IM-12-07 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B01437 .b12177635 mnait m 240409 0 170110 m a m 0 000:001:PDF:b1217763:002906:0:0:0:0:0:0 Abstract n mnait mnait mnarc 20 40 Barcode : 30050120409759 CREATED : 2017-10-01 RECORD # : i13064782 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050120409742 CREATED : 2017-10-01 RECORD # : i13064794 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 1 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050120403687 CREATED : 2017-04-07 RECORD # : i13086959 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 7337 7337 0 0 0 0 MAIN MAIN mnait 2026-08-17 50.00 0 AIT RSPR no.IM-12-07 30050120409759 2026-08-17 16:31:02 1 2026-08-17 20 0 0 0 0 MAIN MAIN mnait 2026-08-17 50.00 0 AIT RSPR no.IM-12-07 30050120409742 2026-08-17 16:31:02 2 2026-08-17 20 0 0 0 0 MAIN MAIN mnarc 2026-08-17 0 AIT RSPR no.IM-12-07 30050120403687 2026-08-17 16:31:02 1 2026-08-17 40