IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK ALGORITHMS TO DETECT THE RIPENESS OF PALM FRUITS BASED ON IMAGE COLORS

Aditya Guna Dharma, NPM 2008100002 (2024) IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK ALGORITHMS TO DETECT THE RIPENESS OF PALM FRUITS BASED ON IMAGE COLORS. Tugas_Akhir (Artikel) International Journal of Science, Technology & Management, 5 (4). ISSN 2772-4015 (e-ISSN)

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Abstract

The ripeness of oil palm fruit greatly affects the quality of the oil produced. Usually, humans manually detect the maturity of oil palm fruit, a process that can be inconsistent and time-consuming. Therefore, there is a need for automated methods that can improve accuracy and efficiency in determining the maturity of oil palm fruit. The study proposes using the Convolutional Neural Network algorithm to detect oil palm fruit maturity based on digital color images. We expect a convolutional neural network, known for its effectiveness in image analysis, to deliver accurate results in classifying fruit maturity. The aim of this study is to develop and test a model of a convolutional neural network that can classify oil palm fruit into three maturity categories: raw, ripe, and rotten. This study employs a maturity-categorized data set of oil palm images. We built and trained a convolutional neural network model using this data set. We evaluate the model performance using four main metrics: accuracy, precision, recall, and f1-score. We perform an in-depth analysis to assess model performance in each maturity category. The confusion matrix yielded an accuracy of 74.6%. The convolutional neural network model developed showed the highest precision results for the ripe fruit (86.00%), followed by rotten fruit (72.30%), and raw fruit (67.30%). We obtained the highest recalls (85.40%) for the raw fruit category, followed by ripe fruits (75.50%) and rotten fruits (72.30%. Keywords : CNN, Confusion Matrix, Deep Learning, Palm Fruit, Ripeness

Item Type:	Article
Uncontrolled Keywords:	CNN, Confusion Matrix, Deep Learning, Palm Fruit, Ripeness
Subjects:	T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > ZA Information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions:	Fakultas Sains Dan Teknologi > Teknologi Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	22 Aug 2024 08:12
Last Modified:	22 Aug 2024 08:12
URI:	http://repository.ulb.ac.id/id/eprint/943

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