| dc.description.abstract | The objective of this research was to determine the optimum input parameter during papaya IPB 1 storage using artificial neu.al netwotk (ANN) and genetics algorithms (GA). ANN was used to prediction conelation input parameter and papaya quality parameter. Based data variant was built five scenario. Optimation propagation algorithm structure with trialerror iteration, hidden layer unit, momentum and input unit variation. lnput parameter signiticantly to quality parameter is maturity Ievel, storage temperalure and storage time. Coefficienl of determination (d) tolal sotuble solid (ISS) and hardness prediction and measurement were o.BgB4, 0.7668 for training and 0.3189, 0.431 for validation. Root Mean Square Error (RMSE) were 0.0A241. lf want to minimization in papaya quality change during maximum storage (182.503 hours) papaya was storage in Oyo of maturily level and fC of storage time. | en |
