Ambon Banana Maturity Classification Based On Convolutional Neural Network (CNN)

Authors

  • Yuha Aulia Nisa University of Dian Nuswantoro
  • Christy Atika Sari University of Dian Nuswantoro
  • Eko Hari Rachmawanto University of Dian Nuswantoro
  • Noorayisahbe Mohd Yaacob Malaysia-Japan International Institute of Technology (MJIIT), University of Technology Malaysia (UTM)

DOI:

10.33395/sinkron.v8i4.12961

Keywords:

Banana, Classification, Convolutional Neural Network, Maturity, Image enhancement

Abstract

The banana (Musa paradical), is an excellent fruit produced nationally and high in vitamins. In Indonesia, banana production is at a higher level than other fruit products. However, one of them is the issue with bananas' post-harvest, which arises when they are produced in huge quantities on a large scale or by an industry that sorts bananas. So far, the determination of the maturity level of bananas is done by relying on visual analysis limited to the color of the skin by the human eye. However, this identification approach has several drawbacks. First, this method requires significant effort in the banana sorting process. In addition, the perception of the fruit's maturity level can vary, because humans can experience fatigue and lack of consistency in judgment. In addition, human judgment is also influenced by subjective factors that can affect the final result. Considering this problem, developed a system to classify the ripeness level of Ambon bananas. This system utilizes image enhancement features to increase contrast, which is implemented using a Convolutional Neural Network (CNN). The classification process is carried out through image processing using MATLAB R2022a software, which forms the basis of a classification system with 4 classes which include 486 images of unripe Ambon bananas, 235 images of half-ripe Ambon bananas, 309 images of perfectly ripe Ambon bananas, 184 images of rotten Ambon bananas. The dataset analyzed in this study totaled 1214 data divided into 1093 training data and 121 test data. The CNN method is used in this data classification, and the results show an accuracy rate of 95.87%.

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Author Biographies

Christy Atika Sari, University of Dian Nuswantoro

Christy Atika Sari received the master in Informatic Engineering from Dian Nuswantoro University and University Teknikal Malaysia Melaka (UTeM) in 2012. She is currently active as author in international journal and confrence scopus indexed. She also awarded as best author and best paper in national and intenational confrence in 2019 and 2020 respectively and awarded from Ristekbrin DIKTI as the indonesian top 50 best researchers in 2020. She currently as lecturer in intelligent systems and and continue to develop the research field image processing and data hiding. She can be contacted at email: christy.atika.sari@dsn.dinus.ac.id.

Eko Hari Rachmawanto, University of Dian Nuswantoro

Eko Hari Rachmawanto, M.Kom, M.CS
Head of Study Program of Informatics Engineering (S1)
University of Dian Nuswantoro (PSDKU Kediri)
Penanggungan 41A, Bandar Lor, Mojoroto, Kediri, 64114, Indonesia

Noorayisahbe Mohd Yaacob, Malaysia-Japan International Institute of Technology (MJIIT), University of Technology Malaysia (UTM)

Ts. Dr. Noorayisahbe Binti Mohd Yaacob, currently a lecturer in the software engineering department at the Malaysia-Japan International Institute of Technology Faculty, University of Technology Malaysia (UTM). She holds a doctoral degree from University Technical Malaysia Malacca (UTeM). She earned her M. Sc. IT degree by research related to Software Engineering and Intelligence System in the Biomedical Computing Engineering area and her B.Sc. CS degree in Software Development (Hons) from UTeM. She received her diploma in ICT (Programming) from Seberang Perai Polytechnic. She has working experience in the multinational and education sectors for more than 5 years, including 9 years of experience in research.

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How to Cite

Nisa, Y. A., Sari, C. A., Rachmawanto, E. H., & Mohd Yaacob, N. (2023). Ambon Banana Maturity Classification Based On Convolutional Neural Network (CNN). Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(4), 2568-2578. https://doi.org/10.33395/sinkron.v8i4.12961

Issue

Vol. 8 No. 4 (2023): Article Research Volume 8 Issue 4, October 2023

Section

Articles

License

Copyright (c) 2023 Yuha Aulia Nisa, Christy Atika Sari, Eko Hari Rachmawanto, Noorayisahbe Mohd Yaacob

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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