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Improving Classification Accuracy of Local Coconut Fruits with Image Augmentation and Deep Learning Algorithm Convolutional Neural Networks (CNN)
Abstract
Local coconut varieties must be classified to maintain the quality and genetic diversity of coconuts as the main commodity in Indonesia's largest coconut-producing region. This study introduces a deep learning module for improved classification of coconuts, using color jitter as part of a data augmentation strategy to supplement the existing dataset and utilizing well-known CNN-based models like VGG16 for image analysis, with a focus on the needs of future research. The goal is to improve the classification accuracy of local coconut varieties through deep learning. We investigate both data augmentations and EDA, and we use VGG-16-based CNN models to enhance the classification performance. We used a confusion matrix for the model evaluation, containing metrics like accuracy, precision, recall, and f1-score. Results reveal that a color jitter augmentation model attained a training accuracy of 99.12%, testing accuracy of 97.33%, and validation accuracy of 97.33%. Model exploration using VGG16, on the other hand, improved all three: training accuracy—99.87%, testing accuracy—98.77%, and validation accuracy—98.97% average F1-score: 99%. Our research contributes massively to providing the best automatic classification method that will benefit and help farmers shorten their jobs while promoting economic growth in trading effectively across Indonesian regions. Its novelty is in combining image augmentation and CNNs, concerning the VGG16 model, showing better.
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K. Dwivedi, M. K. Dutta, and J. P. Pandey, “EMViT-Net: A novel transformer-based network utilizing CNN and multilayer perceptron for the classification of environmental microorganisms using microscopic images,” Ecol. Inform., vol. 79, no. December 2023, p. 102451, 2024, doi: 10.1016/j.ecoinf.2023.102451.
A. Abdullah, Z. Zikrullah, and U. Usman, “Rancang Bangun Sistem Prediksi Pola Sidik Jari Menggunakan Metode Backpropagation,” J. Inform. dan Tek. Elektro Terap., vol. 11, no. 3, 2023, doi: 10.23960/jitet.v11i3.3166.
E. P. C. Edi Ismanto, “Jaringan Syaraf Tiruan Algoritma Backpropagation Dalam Memprediksi Ketersediaan Komoditi Pangan Provinsi Riau,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 2, no. 2, pp. 196–209, 2017, doi: 10.36341/rabit.v2i2.152.
R. Sanjaya, Abdullah, and Usman, “Penerapan Metode Multilayer Perceptron Untuk Memprediksi Tanda Tangan Mahasiswa,” J. Algoritm. Log. dan Komputasi, vol. 5, no. 2, pp. 462–470, 2022, doi: http://dx.doi.org/10.30813/j-alu.v5i02.2760.
Mitra Saputra Ardi, Abdullah, Usman, “Rancang Bangun Pendeteksi Kualitas Beras Menggunakan Metode K-Nearest Neighbor Berbasis Android,” J. Inform. Upgris, vol. 7, no. 2, 2021, [Online]. Available: http://journal.upgris.ac.id/index.php/JIU/article/view/8467%0Ahttps://journal.upgris.ac.id/index.php/JIU/article/download/8467/4990
Y. Shima, “Image Augmentation for Object Image Classification Based on Combination of Pre-Trained CNN and SVM,” J. Phys. Conf. Ser., vol. 1004, no. 1, pp. 85–97, 2018, doi: 10.1088/1742-6596/1004/1/012001.
H. BPS, Kabupaten Indragiri, “Kabupaten Indragiri Hilir Dalam Angka,” Badan Pus. Stat. Kabupaten Indragiri Hilir, vol. 21, pp. 1–574, 2024, [Online]. Available: https://inhilkab.bps.go.id/publication/2024/02/28/8e8d5b89bd09a76881a7efc2/kabupaten-indragiri-hilir-dalam-angka-2024.html
R. Sari, “Strategi Pemerintah Kabupaten Indragiri Hilir Dalam Menjaga Stabiltas Harga Guna Meningkatkan Kesejahteraan Petani Kelapa Di Kecamatan Batang Tuaka,” JISIP UNJA (Jurnal Ilmu Sos. Ilmu Polit. Univ. Jambi), vol. 5, no. 1, pp. 52–63, 2021, doi: 10.22437/jisipunja.v5i1.17698.
H. Mardesci, S. Santosa, N. Nazir, and R. A. Hadiguna, “Penentuan Produk Prospektif Dari Tiga Produk Unggulan Olahan Kelapa Di Kabupaten Indragiri Hilir, Riau,” J. Teknol. Pertan., vol. 6, no. 2, pp. 11–18, 2017, doi: 10.32520/jtp.v6i2.103.
T. Liu, “BHN: A Brain-like Heterogeneous Network,” 2020, [Online]. Available: http://arxiv.org/abs/2005.12826
Abdullah and Usman, “Sistem Cerdas untuk Klasifikasi Buah Kelapa Menggunakan Metode Backpropagation,” Selondang Mayang, pp. 87–94, 2016.
M. R. Ridha and F. Yunita, “Pemilihan Bibit Kelapa Menggunakan Metode Nearest Mean Classifier Untuk Masyarakat Petani Kelapa Di Kabupaten Indragiri Hilir,” J. Perangkat Lunak, vol. 2, no. 3, pp. 101–114, 2020, doi: 10.32520/jupel.v2i3.1306.
A. Muni and M. Jibril, “Penerapan Metode Naive Bayes Classifier Dalam Pemilihan Kualitas Bibit Kelapa Untuk Masyarakat Petani Kelapa Di Indragiri Hilir,” J. Perangkat Lunak, vol. 5, no. 3, pp. 313–322, 2023, doi: 10.32520/jupel.v5i3.2780.
M. Masparudin, A. Abdullah, and U. Usman, “SISTEM PREDIKSI KUALITAS SANTAN KELAPA MENGGUNAKAN NEAREST MEAN CLASSIFIER (NMC),” SISTEMASI, vol. 9, no. 3, p. 646, Sep. 2020, doi: 10.32520/stmsi.v9i3.1015.
A. Abdullah, U. Usman, and M. Efendi, “Sistem Klasifikasi Kualitas Kopra Berdasarkan Warna dan Tekstur Menggunakan Metode Nearest Mean Classifier (NMC),” J. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 4, p. 297, Dec. 2017, doi: 10.25126/jtiik.201744479.
R. Triyogi, R. Magdalena, and B. Hidayat, “Mendeteksi Kematangan Buah Kelapa Sawit Menggunakan Convolutional Neural Network Deep Learning,” J. Nas. SAINS dan Tek., vol. 1, no. 1, pp. 22–27, 2023.
C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0197-0.
M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification,” Neurocomputing, vol. 321, pp. 321–331, 2018, doi: 10.1016/j.neucom.2018.09.013.
M. Chen and W. “Grace” Guo, “DCGAN-CNN with physical constraints for porosity prediction in laser metal deposition with unbalanced data,” Manuf. Lett., vol. 35, pp. 1146–1154, 2023, doi: 10.1016/j.mfglet.2023.08.116.
W. Bönke, B. Alaei, A. Torabi, and D. Oikonomou, “Data augmentation for 3D seismic fault interpretation using deep learning,” Mar. Pet. Geol., vol. 162, no. January, 2024, doi: 10.1016/j.marpetgeo.2024.106706.
I. S. Chandra, R. K. Shastri, D. Kavitha, K. R. Kumar, S. Manochitra, and P. B. Babu, “CNN based color balancing and denoising technique for underwater images: CNN-CBDT,” Meas. Sensors, vol. 28, no. February, p. 100835, 2023, doi: 10.1016/j.measen.2023.100835.
F. Wagner, A. Eltner, and H. G. Maas, “River water segmentation in surveillance camera images: A comparative study of offline and online augmentation using 32 CNNs,” Int. J. Appl. Earth Obs. Geoinf., vol. 119, no. February, p. 103305, 2023, doi: 10.1016/j.jag.2023.103305.
W. Baccouch, S. Oueslati, B. Solaiman, and S. Labidi, “A comparative study of CNN and U-Net performance for automatic segmentation of medical images: Application to cardiac MRI,” Procedia Comput. Sci., vol. 219, no. 2022, pp. 1089–1096, 2023, doi: 10.1016/j.procs.2023.01.388.
E. Dzotsenidze, E. Valla, S. Nõmm, K. Medijainen, P. Taba, and A. Toomela, “Generative Adversarial Networks as a Data Augmentation Tool for CNN-Based Parkinson’s Disease Diagnostics,” IFAC-PapersOnLine, vol. 55, no. 29, pp. 108–113, 2022, doi: 10.1016/j.ifacol.2022.10.240.
B. Marty, R. Gaudin, T. Piperno, D. Rouquette, C. Schwob, and L. Mezeix, “Methodology to classify high voltage transmission poles using CNN approach from satellite images for safety public regulation application : Study case of rural area in Thailand,” Syst. Soft Comput., vol. 6, no. November 2023, p. 200080, 2024, doi: 10.1016/j.sasc.2024.200080.
H. Mo and G. Zhao, “RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network,” Pattern Recognit., vol. 146, no. September 2023, p. 109994, 2024, doi: 10.1016/j.patcog.2023.109994.
B. J. Filia et al., “Improving Batik Pattern Classification using CNN with Advanced Augmentation and Oversampling on Imbalanced Dataset,” Procedia Comput. Sci., vol. 227, pp. 508–517, 2023, doi: 10.1016/j.procs.2023.10.552.
I. Slamet, D. C. Susilotomo, E. Zukhronah, S. Subanti, and I. Susanto, “Retinopathy Classification using Convolutional Neural Network Method with Adam Optimization and Applied Batch Normalization,” vol. 5, no. 3, pp. 1123–1133, 2024.
R. O. Alnashwan, S. A. Chelloug, N. S. Almalki, I. Issaoui, A. Motwakel, and A. Sayed, “Lighting Search Algorithm With Convolutional Neural Network-Based Image Captioning System for Natural Language Processing,” IEEE Access, vol. 11, pp. 142643–142651, 2023, doi: 10.1109/ACCESS.2023.3342703.
L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8.
V. V, C. R. K, and R. A. C., “Real Time Object Detection System with YOLO and CNN Models: A Review,” vol. XIV, no. 7, pp. 144–151, 2022, [Online]. Available: http://arxiv.org/abs/2208.00773
Y. Man, X. Weng, P. K. Sivakumar, M. O’Toole, and K. Kitani, “Multi-Echo LiDAR for 3D Object Detection,” Proc. IEEE Int. Conf. Comput. Vis., pp. 3743–3752, 2021, doi: 10.1109/ICCV48922.2021.00374.
A. Thapa et al., “Deep learning for breast cancer classification: Enhanced tangent function,” Comput. Intell., vol. 38, no. 2, pp. 506–529, 2022, doi: 10.1111/coin.12476.
R. Ranjan et al., “Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition,” vol. 14, no. 8, pp. 1–15, 2018, [Online]. Available: http://arxiv.org/abs/1804.01159
R. DPPI, “Gambaran Umum Kabupaten Indragiri Hilir,” Badan Penanaman Modal dan Promosi Daerah Provinsi Ria. [Online]. Available: https://dppi.riau.go.id/index.php?act=konten&task=read&id=3
Y. Song and Y. Wang, “Towards High-Performance Exploratory Data Analysis (EDA) via Stable Equilibrium Point,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 14450 LNCS, pp. 483–492, 2024, doi: 10.1007/978-981-99-8070-3_37.
R. Indrakumari, T. Poongodi, and S. R. Jena, “Heart Disease Prediction using Exploratory Data Analysis,” Procedia Comput. Sci., vol. 173, no. 2019, pp. 130–139, 2020, doi: 10.1016/j.procs.2020.06.017.
A. S. Paramita and A. Ramadhan, “An Unsupervised Learning and EDA Approach for Specialized High School Admissions,” J. Appl. Data Sci., vol. 5, no. 2, pp. 316–325, 2024, doi: 10.47738/jads.v5i2.178.
H. Liu, S. He, and J. Peng, “K-nearest neighbor based on exploratory data analysis of curriculum models of Chinese early childhood education,” Heliyon, vol. 10, no. 14, p. e33781, 2024, doi: 10.1016/j.heliyon.2024.e33781.
T. Islam, M. S. Hafiz, J. R. Jim, M. M. Kabir, and M. F. Mridha, “A systematic review of deep learning data augmentation in medical imaging: Recent advances and future research directions,” Healthc. Anal., vol. 5, no. December 2023, p. 100340, 2024, doi: 10.1016/j.health.2024.100340.
A. Tatar, M. Haghighi, and A. Zeinijahromi, “Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks,” J. Rock Mech. Geotech. Eng., no. xxxx, 2024, doi: 10.1016/j.jrmge.2024.02.015.
S. V, K. Prasad, C. Mukhopadhyay, and B. Banerjee, “Data augmentation for Gram-stain images based on Vector Quantized Variational AutoEncoder,” Neurocomputing, vol. 600, no. June, p. 128123, 2024, doi: 10.1016/j.neucom.2024.128123.
C. Harvey and A. Noheria, “Deep Learning Encoded Ecg - Avoiding Overfitting in Ecg Machine Learning,” J. Am. Coll. Cardiol., vol. 83, no. 13, p. 172, 2024, doi: 10.1016/s0735-1097(24)02162-4.
G. Wang, W. Li, S. Ourselin, and T. Vercauteren, “Automatic brain tumor segmentation using convolutional neural networks with test-time augmentation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11384 LNCS, pp. 61–72, 2019, doi: 10.1007/978-3-030-11726-9_6.
S. Zini, A. Gomez-villa, M. Buzzelli, and M. Bicocca, “Planckian Jitter : countering the color-crippling effects of color jitter on self-supervised training,” vol. 1, pp. 1–15.
K. Hulliyah, N. S. A. Abu Bakar, S. Aripiyanto, and D. Khairani, “Revolutionizing Digit Image Recognition: Pushing the Limits with Simple CNN and Challenging Image Augmentation Techniques on MNIST,” J. Appl. Data Sci., vol. 4, no. 3, pp. 119–129, 2023, doi: 10.47738/jads.v4i3.104.
F. Gapsari et al., “A convolutional neural network -VGG16 method for corrosion inhibition of 304SS in sulfuric acid solution by timoho leaf extract,” J. Mater. Res. Technol., vol. 30, no. March, pp. 1116–1127, 2024, doi: 10.1016/j.jmrt.2024.03.156.
Y. Chen, Y. Chen, S. Fu, W. Yin, K. Liu, and S. Qian, “VGG16-based intelligent image analysis in the pathological diagnosis of IgA nephropathy,” J. Radiat. Res. Appl. Sci., vol. 16, no. 3, p. 100626, 2023, doi: 10.1016/j.jrras.2023.100626.
T. G. W. Boers et al., “Jou rna lP,” Med. Image Anal., p. 103298, 2024, doi: 10.1016/j.media.2024.103298.
A. O. Topal, R. Chitic, and F. Leprévost, “One evolutionary algorithm deceives humans and ten convolutional neural networks trained on ImageNet at image recognition,” Appl. Soft Comput., vol. 143, p. 110397, 2023, doi: 10.1016/j.asoc.2023.110397.
X. Chen, H. Chen, J. Wan, J. Li, and F. Wei, “An Enhanced AlexNet-Based Model for Femoral Bone Tumor Classification and Diagnosis Using Magnetic,” J. Bone Oncol., p. 100626, 2024, doi: 10.1016/j.jbo.2024.100626.
B. Yadav, A. Indian, and G. Meena, “Recognizing Off-line Devanagari Handwritten Characters Using Modified Lenet-5 Deep Neural Network,” Procedia Comput. Sci., vol. 235, no. 2023, pp. 799–809, 2024, doi: 10.1016/j.procs.2024.04.076.
D. Valero-Carreras, J. Alcaraz, and M. Landete, “Comparing two SVM models through different metrics based on the confusion matrix,” Comput. Oper. Res., vol. 152, no. April 2022, p. 106131, 2023, doi: 10.1016/j.cor.2022.106131.
J. M. Jung et al., “Multi-algorithmic approach for detecting outliers in cattle intake data,” J. Agric. Food Res., vol. 15, no. December 2023, p. 101021, 2024, doi: 10.1016/j.jafr.2024.101021.
O. Peretz, M. Koren, and O. Koren, “Naive Bayes classifier – An ensemble procedure for recall and precision enrichment,” Eng. Appl. Artif. Intell., vol. 136, no. PB, p. 108972, 2024, doi: 10.1016/j.engappai.2024.108972.
K. Tanaka and G. G. Vega Yon, “Imaginary network motifs: Structural patterns of false positives and negatives in social networks,” Soc. Networks, vol. 78, no. December 2023, pp. 65–80, 2024, doi: 10.1016/j.socnet.2023.11.005.
E. Oktafanda, “Klasifikasi Citra Kualitas Bibit dalam Meningkatkan Produksi Kelapa Sawit Menggunakan Metode Convolutional Neural Network (CNN),” J. Inform. Ekon. Bisnis, vol. 4, no. 3, pp. 72–77, 2022, doi: 10.37034/infeb.v4i3.143.
DOI: https://doi.org/10.47738/jads.v6i1.389
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