Classification of Melinjo Fruit Ripeness Using a Convolutional Neural Network (CNN) Based on Digital Images
DOI:
https://doi.org/10.30871/jaic.v10i1.11744Keywords:
Melinjo, Convolutional Neural Network, Classification, Deep-CNNAbstract
The subjective and ineffective manual sorting of melinjo fruit, a key ingredient in Indonesian cuisine, results in inconsistent quality. This study aims to create and evaluate an automated classification system for judging the ripeness of Gnetum gnemon fruit in order to solve these issues and offer a reliable and objective quality control method. The approach was to create a customized Deep Convolutional Neural Network (Deep-CNN). The model was trained and evaluated using a simple dataset of 5,718 images that were separated into three maturity levels: raw, semi-ripe, and fully ripe. Twenty percent of the dataset was used for testing, and the remaining 80 percent was used for training. Image preparation techniques like contrast enhancement and scaling to 250x250 pixels were applied in order to optimize the model's input data. The evaluation was conducted using a test dataset consisting of 1,144 photos. After eight epochs of training with the Adam optimizer, the generated Deep-CNN model demonstrated remarkable efficacy with a final classification accuracy of 99.91%. The high level of performance that remained throughout the testing phase confirmed the model's strong ability to accurately identify the ripeness levels of melinjo fruit. The previously unresolved issue of automated melinjo classification is addressed in this work with a tailored and remarkably accurate (99.91%) solution. Its primary advantage is that it provides a trustworthy and unbiased technical alternative to subjective hand sorting. This directly meets industry needs by offering a scalable method to improve operational effectiveness, standardize product quality, and increase the commercial value of melinjo fruit of agricultural products.
Downloads
References
[1] M. Iqbal, Darmein, and I. Mawardi, “Rancang Bangun Mesin Pengupas Kulit Melinjo dengan Daya 1 HP,” Jurnal Mesin Sains Terapan, vol. 6, no. 1, pp. 13–17, 2022. doi: 10.30811/jmst.v6i1.2852.
[2] S. Koswara et al., Emping Melinjo: Modul Produksi Pangan untuk Industri Rumah Tangga. Jakarta: Direktorat Surveilan dan Penyuluhan Keamanan Pangan, Badan Pengawas Obat dan Makanan Republik Indonesia, 2020.
[3] M. Ali, A. Wulandari, and Siwidyah Desi Lastianti, “Pengendalian Mutu Pada Emping Melinjo Di Ud. Intisari Jaya Di Yogyakarta Jomblang Palbapang Bantul,” RePEc: Research Papers in Economics, Jan. 2018, doi: https://doi.org/10.31219/osf.io/cq2w4.
[4] A. Papenmeier, D. Kern, D. Hienert, Y. Kammerer, and C. Seifert, “How Accurate Does It Feel? – Human Perception of Different Types of Classification Mistakes,” CHI Conference on Human Factors in Computing Systems, Apr. 2022, doi: 10.1145/3491102.3501915.
[5] A. Sharma, R. K. Patel, P. Pranjal, B. Panchal, and S. S. Chouhan, “Computer Vision-Based Smart Monitoring and Control System for Crop,” 2024, pp. 65–82. doi: 10.1007/978-981-99-8684-2_5.
[6] Zahrotul Ilmi Wijayanti, “Penerapan Teknologi CNN Dalam Proses Pendeteksi Kematangan Buah Stroberi”, Uranus, vol. 2, no. 3, pp. 01–12, Jul. 2024
[7] A. I. Hanifah and A. Hermawan, “Klasifikasi Kematangan Pisang Menggunakan Metode Convolutional Neural Network,” Komputika : Jurnal Sistem Komputer, vol. 12, no. 2, pp. 49–56, Sep. 2023, doi: https://doi.org/10.34010/komputika.v12i2.9999
[8] M. Kinanti and Anief Fauzan Rozi, “Implementasi Convutional Neural Network Dalam Menentukan Tingkat Kematangan Mentimun Dan Tomat Berdasarkan Warna Kulit,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 5, pp. 10388–10394, Sep. 2024, doi: https://doi.org/10.36040/jati.v8i5.11076.
[9] B. E. Lumban Batu, W. A. Saputra, and A. Sa’adah, “Banana and Orange Classification Detection Using Convolutional Neural Network,” Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), vol. 13, no. 3, Dec. 2024, doi: 10.23887/janapati.v13i3.80032.
[10] Alvaro, Timur Gadzhievich Aygumov, and Movsar Musaevich Matygov, “Training of a Convolutional Neural Network for the Classification of Coffee Fruit State,” E3S Web of Conferences, vol. 537, pp. 10023–10023, Jan. 2024, doi: https://doi.org/10.1051/e3sconf/202453710023.
[11] S. Qasim, K. N. Khan, M. Yu, and M. S. Khan, “Performance Evaluation of Background Subtraction Techniques for Video Frames,” International Conference on Artificial Intelligence, pp. 102–107, Apr. 2021, doi: 10.1109/ICAI52203.2021.9445253
[12] K. Gunaranjan, K. Vijay, and P. Naveen, “Transfer Learning and Fine-tuned CNN Architecture for Dog Breed Classification,” May 2024, doi: 10.1109/aiiot58432.2024.10574709
[13] G. Y. Ong, N. Abdullah, F. Ridzuan, W. A. Mustafa, and L. H. Alzubaidi, “Enhancing Data Quality in Image Pre-processing: A Case Study on Plant Disease Classification,” Nov. 2023, doi: 10.1109/icteasd57136.2023.10584873
[14] S. J, “Fruit freshess prediction using cnn,” Indian Scientific Journal Of Research In Engineering And Management, May 2024, doi: 10.55041/ijsrem33633
[15] R. Shanmukh, “Application of Texture Analysis Techniques and Image Statistics to Fund us Images for Effective Comparison and Analysis,” Sep. 2023, doi: 10.54882/7420237411079
[16] P. Bigioi, M. C. Munteanu, A. Caliman, C. Zaharia, and D. Dinu, “A convolutional neural network,” Dec. 19, 2016 [Online].Available: https://patents.google.com/patent/WO2017129325A1/en
[17] N. K. Manaswi, “Convolutional Neural Networks,” Apress, Berkeley, CA, 2018, pp. 91–96. doi: 10.1007/978-1-4842-3516-4_6
[18] S. Geerthik, G. A. Senthil, K. J. Oliviya, and R. Keerthana, “A System and Method for Fruit Ripeness Prediction Using Transfer Learning and CNN,” Apr. 2024, doi: 10.1109/ic3iot60841.2024.10550209
[19] M. Yahya, D. Udhayanithi, and A. Venkataraman, “Automatic Fruit Quality Inspection System Using Image Processing,” International journal of scientific research in science, engineering and technology, pp. 01–08, Jun. 2023, doi: 10.32628/ijsrset23103177
[20] Y. Liu, H. Pu, H. Pu, and D.-W. Sun, “Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices,” Trends in Food Science and Technology, vol. 113, pp. 193–204, Jul. 2021, doi: 10.1016/J.TIFS.2021.04.042.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Anggi Ade Kurniawan, Setyonigsih Wibowo, Nur Latifah Dwi Mutiara Sari
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
