ROTTEN AND FRESH FRUITS CLASSIFICATION USING CNN ALGORITHM

PINTO, JOAO BOSCO SOARES (2022) ROTTEN AND FRESH FRUITS CLASSIFICATION USING CNN ALGORITHM. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The classification of rotten and fresh fruits is an essential task in the food industry, with significant economic implications for fruit producers. This study proposes a methodology for the classification of rotten and fresh fruits using a convolutional neural network algorithm. The methodology involves dataset collection, data preprocessing, data augmentation, model training, and model evaluation. A dataset of 1400 fruit images labeled as rotten or fresh was collected, and a convolutional neural network model was designed with three convolutional layers, two maxpooling layers, and two fully connected layers. The model achieved an accuracy of 98.04% on the validation set. The results demonstrate the effectiveness of the proposed methodology and highlight the potential of convolutional neural network algorithms in the food industry. The developed system provides a practical and reliable solution to the classification of rotten and fresh fruits, which can lead to significant economic benefits for fruit producers. Keywords: Fruit classification, Freshness detection, Rotten fruit identification, Convolutional Neural Networks (CNN), Deep Learning, Image Processing, Food Industry.

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 23 174
Call Number:	SIK/18/23/042
NIM/NIDN Creators:	41519010205
Uncontrolled Keywords:	Fruit classification, Freshness detection, Rotten fruit identification, Convolutional Neural Networks (CNN), Deep Learning, Image Processing, Food Industry.
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 020 Library and Information Sciences/Perpustakaan dan Ilmu Informasi > 025 Operations, Archives, Information Centers/Operasional Perpustakaan, Arsip dan Pusat Informasi, Pelayanan dan Pengelolaan Perpustakaan > 025.4 Subject Analysis and Control/Subjek Analisis dan Kontrol Perpustakaan > 025.46 Classification of Specific Subject/Klasifikasi Khusus 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 070 Documentary Media, Educational Media, News Media, Journalism, Publishing/Media Dokumenter, Media Pendidikan, Media Berita, Jurnalisme, Penerbitan > 070.1-070.9 Standard Subdivisions of Documentary Media, Educational Media, News Media, Journalism, Publishing/Subdivisi Standar Dari Media Dokumenter, Media Pendidikan, Media Berita, Jurnalisme, Penerbitan > 070.4 Journalism/Jurnalisme, Jurnalistik, Pers > 070.44 Features and Special Topics/Fitur dan Topik Khusus > 070.444 Miscellaneous Information, Advice, Amusement/Berbagai Macam Informasi, Saran, Hiburan 600 Technology/Teknologi > 660 Chemical Engineering and Related Technologies/Teknologi Kimia dan Ilmu yang Berkaitan > 664 Food Technology/Teknologi Pembuatan Makanan Komersial > 664.8 Fruits and Vegetables/Teknologi Pembuatan Makanan dari Buah-buahan, Sayur-Mayur dan Sayuran
Divisions:	Fakultas Ilmu Komputer > Sistem Informasi
Depositing User:	khalimah
Date Deposited:	02 Nov 2023 03:28
Last Modified:	02 Nov 2023 03:28
URI:	http://repository.mercubuana.ac.id/id/eprint/82524

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