Mango has different colors and sizes that indicate the level of maturity. Mango maturity level often makes farmers confused when choosing a mango that has a good maturity. Sometimes, mango farmers still use manual methods to distinguish mango maturity, while the way that human labor is often inaccurate and different in its determination. The difference is due to the different perceptions of each person. From these problems then the need of machine sorting system on agriculture is felt important. Therefore, researchers will conduct research on mango sortation system. Mango has many types such as “Harum Manis”, “Apple”, “Gincu”, etc. In this study type of mango that will be studied is mango “Gincu” because has a good color distribution. The goal of the research is to create a system that can sort mango that ripe or unripe. The method that used to do this research is separated into few step: problem identification, algorithm development, implementation and evaluation. The system is made using C language, Computer Vision and ANN (Artificial Neural Network) so the system can detect the color of mango that has been ripe or unripe. The output of this research will be compared to related research. The final output of this research is the system can detect the ripe or unripe mango with 94% accuracy.
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