Palm Fruit Ripeness Detection System Using  Convolutional Neural Network (CNN) Algorithm

Josua Nainggolan; Debi Yandra Niska; Faridawaty Marpaung; Insan Taufik; Kana Saputra S

doi:10.59934/jaiea.v4i3.989

Authors

Josua Nainggolan Universitas Negeri Medan
Debi Yandra Niska Universitas Negeri Medan
Faridawaty Marpaung Universitas Negeri Medan
Insan Taufik Universitas Negeri Medan
Kana Saputra S Universitas Negeri Medan

DOI:

https://doi.org/10.59934/jaiea.v4i3.989

Keywords:

Palm, Accuracy, Classification, CNN, MobileNetV2

Abstract

Oil Palm Fruit is a valuable natural resource crop in the plantation sector in Indonesia, with promising future growth prospects. To produce the best oil palm fruit, good sorting is needed. With good oil palm fruit, adequate technology is needed to assist in sorting oil palm fruit. Therefore, this study aims to help companies sort oil palm fruit bunches. In this study, CNN was used with the MobileNetV2 algorithm and the training accuracy results reached a peak of 98.20%, while the validation accuracy remained high at 95.00%. This proves that this model is very good and very feasible for further research. This method has proven to be the best choice for achieving high accuracy and low loss, but also minimizing errors in prediction.

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