02353ntm a2200229 a 4500001001400000003000700014005001700021008004100038020002200079040000800101100003200109245010200141260003400243300005700277500005700334502013300391504002800524520144300552610007201995650004502067650001102112vtls000099229KUKTEM20251117113326.0170420t2016    my a   f am   000 0 eng d  aTHE0001252(Local)  aUMP0 aSalidy Chindamanee Chamlong10aTomato ripeness classification using artificial neural network /cSalidy Chindamanee A/P Chamlong  aKuantan, Pahang :bUMP,c2016  axiv, 85 p. :bill. (some col.) ;c30 cm. +e1 CD-ROM  aFaculty of Computer Systems and Software Engineering  aProject paper (Bachelor of Computer Science (Computer Systems & Networking) With Honours) -- Universiti Malaysia Pahang – 2016  aBibliography : p. 55-563 aThe 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.20aFaculty of Computer Systems and Software EngineeringxDissertations 0aUniversities and CollegesxDissertations 0aTheses