Skripsi

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

---

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.

---

Availability

Detail Information

Series Title

-

Call Number

T1629362024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xv, 75 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

004.07

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

-

Subject(s)

Pemrosesan data
Prodi Teknik Informatika

Specific Detail Info

-

Statement of Responsibility

MURZ

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment