SISTEM DETEKSI TINGKAT KEMATANGAN TANDAN BUAH SEGAR KELAPA SAWIT MENGGUNAKAN METODE CONVNET BERBASIS ANDROID
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Date
2022-06
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Elfitra
Abstract
One of the oil palms harvesting processes is to determine the maturity level of Fresh Fruit
Bunches (FFB). FFB maturity is one of the determinants quality productions of palm oil
processing materials. In general, FFB maturity can be checked manually by farmers by
direct observation. Manual selection of FFB, of course, requires time and experienced
farmers to be able to determine maturity correctly. ConvNet is a machine learning method
that can be used to quickly determine the maturity level of oil palm FFB. ConvNet allows
the model to recognize the shape, color, and edge of each TBS training data. Using 900
FFB image data, the model can detect the maturity level of oil palm from three classes,
namely raw, ripe, and empty bunches. The results of the training model have an accuracy
of 86% with a precision and recall of more than 90%.
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Keywords
Android, Convolutional Neural Network, Palm Oil, Detections system
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