Deteksi Pisang dengan Kematangan Secara Kimia Menggunakan Convolutional Neural Network
Kata Kunci:
MobileNetV3-small, ResNet152Abstrak
Naskah ini akan di terbitkan di Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Referensi
Alnuaim, A., Zakariah, M., Hatamleh, W. A., Tarazi, H., Tripathi, V., & Amoatey, E. T. (2022). Human-Computer Interaction with Hand Gesture Recognition Using ResNet and MobileNet. Computational Intelligence and Neuroscience, 2022.
Alsenan, A., Ben Youssef, B., & Alhichri, H. (2022). MobileUNetV3—A Combined UNet and MobileNetV3 Architecture for Spinal Cord Gray Matter Segmentation. Electronics, 11(15), 2388.
Arifki, H. H., & Barliana, M. I. (2018). Karakteristik dan manfaat tumbuhan pisang di Indonesia: Review Artikel. Farmaka, 16(3).
Dewi, C., Arisoesilanigsih, E., Mahmudy, W.F., & Solimun., 2023. Optimization of Information Gain Interval on Determining Artificial Ripeness of Banana Using Image Data with Imbalanced Class. Agriculture and Natural Resources, 057(4), 615-624.
Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151.
Gunasekara, S. R. W., Hemamali, K., Dayananada, T. G., & Jayamanne, V. S. (2015). Post harvest quality analysis of embul banana following artificial ripening techniques. International Journal of Science, Environment, 4(6), 1625–1632.
Islam, M. N., Imtiaz, M. Y., Alam, S. S., Nowshad, F., Shadman, S. A., & Khan, M. S. (2018). Artificial ripening on banana (Musa Spp.) samples: Analyzing ripening agents and change in nutritional parameters. Cogent Food & Agriculture, 4(1), 1477232.
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695.
Maduwanthi, S. D. T., & Marapana, R. (2019). Induced ripening agents and their effect on fruit quality of banana. International Journal of Food Science, 2019.
Mazen, F. M. A., & Nashat, A. A. (2019). Ripeness classification of bananas using an artificial neural network. Arabian Journal for Science and Engineering, 44, 6901–6910.
Pustokhin, D. A., Pustokhina, I. V, Dinh, P. N., Phan, S. Van, Nguyen, G. N., Joshi, G. P., & K, S. (2023). An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19. Journal of Applied Statistics, 50(3), 477–494.
Rodriguez, M., Pastor, F., & Ugarte, W. (2021). Classification of fruit ripeness grades using a convolutional neural network and data augmentation. 2021 28th Conference of Open Innovations Association (FRUCT), 374–380.
Saranya, N., Srinivasan, K., & Kumar, S. K. P. (2022). Banana ripeness stage identification: a deep learning approach. Journal of Ambient Intelligence and Humanized Computing, 13(8), 4033–4039.
Vaviya, H., Yadav, A., Vishwakarma, V., & Shah, N. (2019). Identification of artificially ripened fruits using machine learning. 2nd International Conference on Advances in Science & Technology (ICAST).
Vetrekar, N., Ramachandra, R., & Gad, R. S. (2023). Multilevel Fusion of Multispectral Images to Detect the Artificially Ripened Banana. IEEE Sensors Letters, 7(1), 1–4.
Alnuaim, A., Zakariah, M., Hatamleh, W. A., Tarazi, H., Tripathi, V., & Amoatey, E. T. (2022). Human-Computer Interaction with Hand Gesture Recognition Using ResNet and MobileNet. Computational Intelligence and Neuroscience, 2022.
Alsenan, A., Ben Youssef, B., & Alhichri, H. (2022). MobileUNetV3—A Combined UNet and MobileNetV3 Architecture for Spinal Cord Gray Matter Segmentation. Electronics, 11(15), 2388.
Arifki, H. H., & Barliana, M. I. (2018). Karakteristik dan manfaat tumbuhan pisang di Indonesia: Review Artikel. Farmaka, 16(3).
Dewi, C., Arisoesilanigsih, E., Mahmudy, W.F., & Solimun., 2023. Optimization of Information Gain Interval on Determining Artificial Ripeness of Banana Using Image Data with Imbalanced Class. Agriculture and Natural Resources, 057(4), 615-624.
Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151.
Gunasekara, S. R. W., Hemamali, K., Dayananada, T. G., & Jayamanne, V. S. (2015). Post harvest quality analysis of embul banana following artificial ripening techniques. International Journal of Science, Environment, 4(6), 1625–1632.
Islam, M. N., Imtiaz, M. Y., Alam, S. S., Nowshad, F., Shadman, S. A., & Khan, M. S. (2018). Artificial ripening on banana (Musa Spp.) samples: Analyzing ripening agents and change in nutritional parameters. Cogent Food & Agriculture, 4(1), 1477232.
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695.
Maduwanthi, S. D. T., & Marapana, R. (2019). Induced ripening agents and their effect on fruit quality of banana. International Journal of Food Science, 2019.
Mazen, F. M. A., & Nashat, A. A. (2019). Ripeness classification of bananas using an artificial neural network. Arabian Journal for Science and Engineering, 44, 6901–6910.
Pustokhin, D. A., Pustokhina, I. V, Dinh, P. N., Phan, S. Van, Nguyen, G. N., Joshi, G. P., & K, S. (2023). An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19. Journal of Applied Statistics, 50(3), 477–494.
Rodriguez, M., Pastor, F., & Ugarte, W. (2021). Classification of fruit ripeness grades using a convolutional neural network and data augmentation. 2021 28th Conference of Open Innovations Association (FRUCT), 374–380.
Saranya, N., Srinivasan, K., & Kumar, S. K. P. (2022). Banana ripeness stage identification: a deep learning approach. Journal of Ambient Intelligence and Humanized Computing, 13(8), 4033–4039.
Vaviya, H., Yadav, A., Vishwakarma, V., & Shah, N. (2019). Identification of artificially ripened fruits using machine learning. 2nd International Conference on Advances in Science & Technology (ICAST).
Vetrekar, N., Ramachandra, R., & Gad, R. S. (2023). Multilevel Fusion of Multispectral Images to Detect the Artificially Ripened Banana. IEEE Sensors Letters, 7(1), 1–4.
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