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All Journal Jurnal Nasional Teknologi Informasi dan Aplikasinya
Junior, Vodka Joe
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Computer Science & IT

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Pengklasifikasian Kualitas Pisang dengan Deep Learning CNN Arsitektur VGG16 Junior, Vodka Joe; Santi Astawa, I Gede
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p10

Abstract

Bananas are one of the most popular fruits consumed worldwide, valued for their nutritional benefits and versatility in various dishes. However, ensuring banana quality, including ripeness and integrity, remains crucial in meeting consumer expectations and maintaining supply chain standards. Manual classification of banana quality can be tedious, prompting the need for efficient methods. In this study, we explore the classification of banana quality using Convolutional Neural Network (CNN) with VGG16 architecture and image augmentation. Leveraging previous research and considering the superior performance of VGG16, we gathered data from Kaggle and evaluated our model's accuracy. The implementation yielded promising results, achieving a peak accuracy of 97.50% with 15 epochs and an 80%-20% training-validation data split. This surpasses previous methods, indicating the effectiveness of CNN with VGG16 in banana quality classification. Keywords: Banana quality, Convolutional Neural Network, VGG16 architecture, Image augmentation, Classification accuracy
Co-Authors I Gede Santi Astawa