02588ntm a2200277 a 4500 vtls000099229 KUKTEM 20251117113326.0 170420t2016 my a f am 000 0 eng d THE0001252(Local) 201905271345 atie 201704201041 saini UMP FSKKP .S25 2016 r Bc. Salidy Chindamanee Chamlong Tomato ripeness classification using artificial neural network / Salidy Chindamanee A/P Chamlong Kuantan, Pahang : UMP, 2016 xiv, 85 p. : ill. (some col.) ; 30 cm. + 1 CD-ROM Faculty of Computer Systems and Software Engineering Project paper (Bachelor of Computer Science (Computer Systems & Networking) With Honours) -- Universiti Malaysia Pahang – 2016 Bibliography : p. 55-56 The classification of tomato into its maturity level can be determined by several parameters such as size, shape and color. It is important to classify it before send to markets, because to import to distant market, the tomato should be pack at colour break stage and for local market, it should be pack when it is fully red. The purpose of this research is to proposed neural network algorithm for classification and identifying the maturity level, by which this algorithm can be run in the software called “Matlab” by using the Matlab code. In this research, the parameter that will be used to identify the maturity of the tomato is external color. At start, some of the tomato image will be prepare, which include image for ripe tomato, halp ripe tomato and unripe tomato. Then, each of the image will be extract its rgb value using Matlab. After obtain the value, this value will be manually rescale. Next ANN algorithm will be train and test by perform some calculation to classify the tomato into stage. Some sample data sets is prepared used to train the network and backpropagation algorithm will be use. The trained data then will produce MSE and RMSE value to show the difference of the actual result with the expected one. From this study, the neural network model for tomato classification has achieved the best MSE value of 0.009972142. The colour of tomato is believed to be the major method in determining its maturity. Faculty of Computer Systems and Software Engineering Dissertations Universities and Colleges Dissertations Theses 0 0 0 1 20000 20000 2019-09-04 0 FSKKP .S25 2016 r Bc. 0000117576 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 20000 20000 2019-09-04 0 CD 10691 | FSKKP .S25 2016 r Bc. 0000117577 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 6771 6777 VTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5000*5020*5040*5200*6100*6500*6501*9992