Classification of Date Fruit Types Using CNN Algorithm Based on Type
DOI:
https://doi.org/10.57152/malcom.v3i1.724Keywords:
Classification, Date Fruit, Convolutional Neural Network, Mobile NetV2Abstract
Date fruits are an important commodity in the agriculture and food industry. However, in the process of sales and distribution to ordinary people, there are often errors in identifying different types of date fruits. Therefore, this research aims to develop an automatic classification system to distinguish the types of date fruits based on their types using the Convolutional Neural Network (CNN) algorithm. The case study was conducted at Hamima Dates date shop. The data used are fruit images with 9 categories and a total of 1658 samples, which are divided into 1496 samples for training data and 162 samples for testing data. The test results show that the CNN algorithm has a high level of accuracy in classifying the type of date fruit, with an accuracy of 96%. In this study, feature analysis was also conducted to determine the contribution of each feature to the classification of date fruit types. The results of this study can be the basis for the development of a more sophisticated date fruit automatic classification system and can be applied to other types of fruits
References
Tridge, “overview of global date market (2020),” https://www.tridge.com/ intelligences/dates, 2022.
R. A. Rahmadani, S. Bulkis, and M. A. Budiman, “POTENSI BUDIDAYA KURMA DI INDONESIA DITINJAU DARI PERSPEKTIF EKONOMIS DAN EKOLOGIS,” 2017.
M. H. Romario, E. Ihsanto, and T. M. Kadarina, “Sistem Hitung dan Klasifikasi Objek dengan Metode Convolutional Neural Network,” Jurnal Teknologi Elektro, vol. 11, no. 2, p. 108, Jun. 2020, doi: 10.22441/jte.2020.v11i2.007.
S. Z. M. Zaki, M. Asyraf Zulkifley, M. Mohd Stofa, N. A. M. Kamari, and N. Ayuni Mohamed, “Classification of tomato leaf diseases using MobileNet v2,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 9, no. 2, p. 290, Jun. 2020, doi: 10.11591/ijai.v9.i2.pp290-296.
N. Kalchbrenner, E. Grefenstette, and P. Blunsom, “A Convolutional Neural Network for Modelling Sentences,” Apr. 2014.
R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Insights Imaging, vol. 9, no. 4, pp. 611–629, Aug. 2018, doi: 10.1007/s13244-018-0639-9.
R. B. Arif, Md. A. B. Siddique, M. M. R. Khan, and M. R. Oishe, “Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Convolutional Neural Network,” Sep. 2018, doi: 10.1109/CEEICT.2018.8628078.
D. Raval and J. Undavia, “A Comprehensive assessment of Convolutional Neural Networks for skin and oral cancer detection using medical images,” Healthcare Analytics, p. 100199, May 2023, doi: 10.1016/j.health.2023.100199.
L. F. de J. Silva, O. A. C. Cortes, and J. O. B. Diniz, “A novel ensemble CNN model for COVID-19 classification in computerized tomography scans,” Results in Control and Optimization, vol. 11, p. 100215, Jun. 2023, doi: 10.1016/j.rico.2023.100215.
R. K. V, J. M. K, and K. R. V, “Modelling a deep network using CNN and RNN for accident classification,” Measurement: Sensors, p. 100794, May 2023, doi: 10.1016/j.measen.2023.100794.
M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” May 2016.
M. Ramchandani et al., “Survey: Tensorflow in Machine Learning,” J Phys Conf Ser, vol. 2273, no. 1, p. 012008, May 2022, doi: 10.1088/1742-6596/2273/1/012008.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Jan. 2018.
T. Kluyver et al., “Jupyter Notebooks—a publishing format for reproducible computational workflows,” in Positioning and Power in Academic Publishing: Players, Agents and Agendas - Proceedings of the 20th International Conference on Electronic Publishing, ELPUB 2016, IOS Press BV, 2016, pp. 87–90. doi: 10.3233/978-1-61499-649-1-87.
J. Hao and T. K. Ho, “Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language,” Journal of Educational and Behavioral Statistics, vol. 44, no. 3, pp. 348–361, Jun. 2019, doi: 10.3102/1076998619832248.
