Evaluasi Perbandingan Kinerja Convolutional Neural Networks untuk Klasifikasi Kualitas Biji Kakao

Indra Riyana Rahadjeng, Muhammad Noor Hasan Siregar, Agus Perdana Windarto, M.Kom

Abstract


The assessment of cocoa bean quality plays a crucial role in the chocolate industry, and automated approaches utilizing image processing techniques and classifiers have become increasingly appealing. In this study, we implemented and compared the performance of image classifiers using Convolutional Neural Network (CNN) architectures for cocoa bean quality classification. By employing this approach, we developed a system capable of accurately and efficiently classifying cocoa bean images, reducing dependence on human evaluation. We compared several CNN architectures, including VGGNet, to evaluate their performance in cocoa bean image classification. Experimental results demonstrated that CNN-based classifiers can provide accurate assessments of cocoa bean quality, with significant success rates. This research contributes to the development of efficient and accurate image classification systems for cocoa beans, which can enhance efficiency in the chocolate industry and ensure product quality. Additionally, our testing results indicate that the model with a batch size of 64 achieved the highest accuracy of 98.44%, outperforming the other three tested batch sizes in cocoa bean classification performance.

Keywords


Cocoa Bean Quality Assessment; Image Processing; Classifier; Convolutional Neural Network (CNN); CNN architecture; VGGNet

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References


E. G. Winarto, Rahmayati, and A. Lawi, “Implementasi Arsitektur Inception Resnet-V2 untuk Klasifikasi Kualitas Biji Kakao,†Konf. Nas. Ilmu Komput. 2021 , pp. 132–137, 2021.

S. V. M. Dan and K. N. Knn, “Perbandingan Kinerja Pengklasifikasi Citra Buah Kakao Sakit Dan Sehat Menggunakan Support Vector Machine (Svm) Dan K-Nearest Neighbors (Knn),†vol. 14, no. 1, pp. 1–8, 2023.

R. Febrian, B. M. Halim, M. Christina, D. Ramdhan, and A. Chowanda, “Facial expression recognition using bidirectional LSTM - CNN,†Procedia Comput. Sci., vol. 216, no. 2022, pp. 39–47, 2023, doi: 10.1016/j.procs.2022.12.109.

V. Choudhary, P. Guha, G. Pau, R. K. Dhanaraj, and S. Mishra, “Automatic Classification of Cowpea Leaves Using Deep Convolutional Neural Network,†Smart Agric. Technol., p. 100209, 2023, doi: 10.1016/j.atech.2023.100209.

S. Sowmya and D. Jose, “Contemplate on ECG signals and classification of arrhythmia signals using CNN-LSTM deep learning model,†Meas. Sensors, vol. 24, no. October, p. 100558, 2022, doi: 10.1016/j.measen.2022.100558.

S. Anwar and Ã. Rocha, “Special issue on towards advancements in machine learning for exploiting large-scale and heterogeneous repositories,†Neural Comput. Appl., vol. 5, pp. 7909–7911, 2023, doi: 10.1007/s00521-022-08182-5.

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 Control Optim., vol. 11, no. September 2022, p. 100215, 2023, doi: 10.1016/j.rico.2023.100215.

B. Eidel, “Deep CNNs as universal predictors of elasticity tensors in homogenization,†Comput. Methods Appl. Mech. Eng., vol. 403, p. 115741, 2023, doi: 10.1016/j.cma.2022.115741.

D. Ruan, J. Wang, J. Yan, and C. Gühmann, “CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis,†Adv. Eng. Informatics, vol. 55, no. June 2022, p. 101877, 2023, doi: 10.1016/j.aei.2023.101877.

W. N. Ismail, H. A. Alsalamah, M. M. Hassan, and E. Mohamed, “AUTO-HAR: An adaptive human activity recognition framework using an automated CNN architecture design,†Heliyon, vol. 9, no. 2, p. e13636, 2023, doi: 10.1016/j.heliyon.2023.e13636.

A. Abbas, J. P. Vantassel, B. R. Cox, K. Kumar, and J. Crocker, “A frequency-velocity CNN for developing near-surface 2D vs images from linear-array, active-source wavefield measurements,†Comput. Geotech., vol. 156, no. February, p. 105305, 2023, doi: 10.1016/j.compgeo.2023.105305.

R. Li, R. Gao, and P. N. Suganthan, “A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition,†Inf. Sci. (Ny)., vol. 624, pp. 833–848, 2023, doi: 10.1016/j.ins.2022.12.088.

N. N. Prakash, V. Rajesh, D. L. Namakhwa, S. Dwarkanath Pande, and S. H. Ahammad, “A DenseNet CNN-based liver lesion prediction and classification for future medical diagnosis,†Sci. African, vol. 20, p. e01629, 2023, doi: 10.1016/j.sciaf.2023.e01629.

Z. A. Sejuti and M. S. Islam, “A hybrid CNN–KNN approach for identification of COVID-19 with 5-fold cross validation,†Sensors Int., vol. 4, no. November 2022, p. 100229, 2023, doi: 10.1016/j.sintl.2023.100229.

Q. Hou, R. Xia, J. Zhang, Y. Feng, Z. Zhan, and X. Wang, “Learning visual overlapping image pairs for SfM via CNN fine-tuning with photogrammetric geometry information,†Int. J. Appl. Earth Obs. Geoinf., vol. 116, no. October 2022, p. 103162, 2023, doi: 10.1016/j.jag.2022.103162.

M. Chu, P. Wu, G. Li, W. Yang, J. L. Gutiérrez-Chico, and S. Tu, “Advances in Diagnosis, Therapy, and Prognosis of Coronary Artery Disease Powered by Deep Learning Algorithms,†JACC Asia, vol. 3, no. 1, pp. 1–14, 2023, doi: 10.1016/j.jacasi.2022.12.005.

K. Cheng, “Hierarchical attributes learning for pedestrian re-identification via parallel stochastic gradient descent combined with momentum correction and adaptive learning rate,†Neural Comput. Appl., vol. 2, 2019, doi: 10.1007/s00521-019-04485-2.

N. Youssouf, “Traffic sign classification using CNN and detection using faster-RCNN and YOLOV4,†Heliyon, vol. 8, no. 12, 2022, doi: 10.1016/j.heliyon.2022.e11792.

G. Rajeshkumar et al., “Smart office automation via faster R-CNN based face recognition and internet of things,†Meas. Sensors, vol. 27, no. November 2022, p. 100719, 2023, doi: 10.1016/j.measen.2023.100719.

J. D. Rosita P and W. S. Jacob, “Multi-Objective Genetic Algorithm and CNN-Based Deep Learning Architectural Scheme for effective spam detection,†Int. J. Intell. Networks, vol. 3, no. December 2021, pp. 9–15, 2022, doi: 10.1016/j.ijin.2022.01.001.




DOI: https://doi.org/10.30865/mib.v7i3.6533

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