KLASIFIKASI TINGKAT KEMATANGAN BUAH PEPAYA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN)

SUSANTI, DELLA and Rini, Dian Palupi and Marieska, Mastura Diana (2024) KLASIFIKASI TINGKAT KEMATANGAN BUAH PEPAYA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN). Undergraduate thesis, Sriwijaya University.

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Abstract

Image classification is a major challenge in the digital world, especially in the field of deep learning. so this research develops a classification system using Convolutional Neural Network (CNN) with five architectures namely GoogLeNet (InceptionV3), MobileNet, ResNet50, SqueezeNet, and Visual Geometry Group (VGG16) to classify papaya fruit. With the number of data for ripe papaya 267, unripe papaya 290, and rotten papaya 100, a total of 657 papaya fruit images were used. MobileNet test results showed the best performance with 100% training accuracy, 98.48% testing, 98% precision, 96% recall, and 97% F1-score. GoogLeNet achieved 99.44% training accuracy and 92% F1-score, while VGG16 obtained 88% accuracy and 86% F1-score. ResNet50 and SqueezeNet were less optimal with F1-score of 34% and 20% respectively. Based on this evaluation, MobileNet was declared as the best architecture for papaya fruit ripeness classification in this study because it was able to optimize the model with small data. Keyword: Classification, Papaya, Convolutional Neural Network (CNN), GoogLeNet (InceptionV3), ResNet50, SqueezeNet, MobileNet, and VGG16.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi Tingkat Kematangan Buah Pepaya, Convolutional Neural Network
Subjects:	T Technology > T Technology (General) > T58.6-58.62 Management information systems > T58.6 General works Industrial engineering Information technology. Information systems (General) Management information systems -- Continued
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Della Susanti
Date Deposited:	08 Jan 2025 01:39
Last Modified:	08 Jan 2025 01:39
URI:	http://repository.unsri.ac.id/id/eprint/162936

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