Implementasi Algoritma Convolutional Neural Network (CNN) untuk Pengenalan dan Klasifikasi Buah Berdasarkan Citra Digital
DOI:
https://doi.org/10.55606/jupikom.v4i2.4116Keywords:
Classification, CNN, Fruit, RecognitionAbstract
Object recognition, particularly fruit classification, plays a crucial role in various fields, ranging from agricultural automation to digital marketplaces. This study proposes a fruit classification system based on RGB images, developed using a Convolutional Neural Network (CNN) architecture consisting of convolutional layers, pooling layers, fully connected layers, and dropout for model stability. The model was trained using the Adam optimization algorithm on an augmented dataset to enhance data variation and reduce overfitting. The resulting model achieved an average accuracy of 98%, demonstrating the reliability of CNNs in pattern recognition tasks. To enhance usability, the model was integrated into a graphical user interface (GUI) built with MATLAB R2023b App Designer, allowing users to add datasets, train the model, and predict new images without writing any code. The findings highlight that while the model performs well, its accuracy remains dependent on consistent image backgrounds; therefore, expanding the variety of fruit types and background conditions in the dataset is essential to improve the system's robustness in real-world applications.
References
Alwanda, M. R., Ramadhan, R. P. K., and Alamsyah, D., "Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle," Jurnal Algoritme, vol. 1, no. 1, pp. 45–56, 2020. [Online]. Available: https://doi.org/10.35957/algoritme.v1i1.434
Anggara, D., Suarna, N., and Arie Wijaya, Y., "Performance Comparison Analysis Of Optimizer Adam, SGD, and RMSPROP on The H5 Model," Jurnal Ilmiah NERO, vol. 8, no. 1, 2023. [Online]. Available: https://www.kaggle.com/datasets/jonathanoheix/face-expression-recognition-dataset
Fachrezzy, I. R., and Budisusila, E. N., "IMPLEMENTASI NEURAL NETWORK UNTUK DETEKSI OTOMATIS JENIS BUAH BERDASARKAN CITRA WARNA DAN BENTUK MENGGUNAKAN MATLAB," Jurnal Teknologi Pembelajaran Interaktif, vol. 5, no. 1, pp. 1–7, 2025.
Fadillah, R. Z., Irawan, A., Susanty, M., and Artikel, I., "Data Augmentasi Untuk Mengatasi Keterbatasan Data Pada Model Penerjemah Bahasa Isyarat Indonesia (BISINDO)," Jurnal Informatika, vol. 8, no. 2, pp. 208–214, 2021. [Online]. Available: https://ejournal.bsi.ac.id/ejurnal/index.php/ji/article/view/10768
Gibran, M. K., and Saleh, A., "A Hybrid RBF Neural Network and FCM Clustering for Diabetes Prediction Dataset," Journal of Computer Science, Information Technology and Telecommunication Engineering, vol. 4, no. 2, pp. 395–401, 2023. [Online]. Available: https://doi.org/10.30596/jcositte.v4i2.15573
Husen, D., "Evaluasi teknik augmentasi data untuk klasifikasi tumor otak menggunakan cnn pada citra mri," TEKNIMEDIA, vol. 5, no. 2, pp. 219–228, 2024.
Magdalena, R., Saidah, S., Pratiwi, N. K. C., and Putra, A. T., "Klasifikasi Tutupan Lahan Melalui Citra Satelit SPOT-6 dengan Metode Convolutional Neural Network (CNN)," Jurnal Edukasi Dan Penelitian Informatika (JEPIN), vol. 7, no. 3, p. 335, 2021. [Online]. Available: https://doi.org/10.26418/jp.v7i3.48195
Naranjo-Torres, J., Mora, M., Hernández-García, R., Barrientos, R. J., Fredes, C., and Valenzuela, A., "A review of convolutional neural network applied to fruit image processing," Applied Sciences (Switzerland), vol. 10, no. 10, p. 2, 2020. [Online]. Available: https://doi.org/10.3390/app10103443
Pratama, G. A., Puspaningrum, E. Y., and Maulana, H., "Convolutional Neural Network Dan Faster Region Convolutional Neural Network Untuk Klasifikasi Kualitas Biji Kopi Arabika," Jurnal Informatika Dan Teknik Elektro Terapan, vol. 12, no. 3, pp. 2776–2785, 2024. [Online]. Available: https://doi.org/10.23960/jitet.v12i3.4887
Purwono, Ma’arif, A., Rahmaniar, W., Fathurrahman, H. I. K., Frisky, A. Z. K., and Haq, Q. M. U., "Understanding of Convolutional Neural Network (CNN): A Review," International Journal of Robotics and Control Systems, vol. 2, no. 4, pp. 739–748, 2022. [Online]. Available: https://doi.org/10.31763/ijrcs.v2i4.888
Risdin, F., Mondal, P. K., and Hassan, K. M., "Convolutional Neural Networks (CNN) for Detecting Fruit Information Using Machine Learning Techniques," IOSR Journal of Computer Engineering, vol. 22, no. 2, pp. 1–13, 2020. [Online]. Available: https://doi.org/10.9790/0661-2202010113
Saleh, A., Ridwan, A., and Gibran, M. K., "Machine Learning and Fuzzy C-Means Clustering for the Identification of Tomato Diseases," Indonesian Journal of Computer Science, vol. 12, no. 5, pp. 2401–2413, 2023. [Online]. Available: https://doi.org/10.33022/ijcs.v12i5.3379
Sriani, S., and Nabila, A., "Implementasi Deep Learning Untuk Mengidentifikasi Umur Manusia Menggunakan Convolutional Neural Network (Cnn)," Jurnal Informatika Dan Teknik Elektro Terapan, vol. 12, no. 3, pp. 1836–1843, 2024. [Online]. Available: https://doi.org/10.23960/jitet.v12i3.4457
Winardi, P., and Setyati, E., "Identifikasi Jenis Daging dengan Menggunakan Algoritma Convolution Neural Network," Journal of Information System, Graphics, Hospitality and Technology, vol. 3, no. 2, pp. 82–88, 2021. [Online]. Available: https://doi.org/10.37823/insight.v3i02.178
Yanto, B., Rouza, E., Fimawahib, L., Hayadi, B. H., and Pratama, R. R., "Penerapan Algoritma Deep Learning Convolutional Neural Network Dalam Menentukan Kematangan Buah Jeruk Manis Berdasarkan Citra Red Green Blue (RGB)," Jurnal Teknologi Informasi Dan Ilmu Komputer, vol. 10, no. 1, pp. 59–66, 2023. [Online]. Available: https://doi.org/10.25126/jtiik.20231015695
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