Pengembangan Multimodal Convolutional Neural Network untuk Grading Buah Jambu Kristal dengan Dua Perspektif Citra
Abstrak
Indonesia merupakan negara dengan sektor pertanian yang memiliki potensi besar, salah satu produk unggulannya adalah buah-buahan. Terdapat beberapa komoditas buah-buahan di Indonesia yang belum mendapatkan perhatian yang seharusnya, contohnya adalah buah jambu kristal. Upaya peningkatan pemanfaatan jambu kristal dapat dicapai dengan mengoptimalkan proses produksinya. Pengoptimalan ini dapat dicapai dengan menerapkan otomatisasi pada berbagai tahap, dan tahapan grading menjadi salah satu aspek yang sangat menguntungkan. Proses grading dapat diotomatisasi dengan pendekatan computer vision, lebih spesifik Multimodal Convolutional Neural Network (CNN). Pendekatan ini melakukan grading buah jambu kristal dengan masukan citra atas dan citra samping buah. Pendekatan CNN biasa tidak dapat menerima lebih dari satu modalitas sehingga penciri kualitas buah yang diperoleh lebih terbatas dan sangat mungkin untuk tidak mencukupi untuk grading dengan benar. Penelitian dilakukan dengan membangun model Multimodal CNN yang dapat menerima dua macam citra tadi dan menghasilkan prediksi kualitas buah jambu kristal. Model dilatih dengan data pasangan citra atas dan citra samping buah jambu kristal yang sudah melalui pemrosesan awal. Model dengan kinerja terbaik didapatkan dengan penerapan optimizer Adam tanpa scheduler dan learning rate awal sebesar 0.001 pada proses pelatihannya terhadap data yang mendapatkan pemrosesan awal secara lengkap. Model ini mendapatkan nilai akurasi 0.95 dan F1 score 0.95.
Referensi
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Yanto, B., Rouza, E., Fimawahib, L., Hayadi, B. H., & Pratama, R. R. (2023). 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, 10(1), 59-66.
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Badan Pusat Statistik, 2023. Ekspor Buah-Buahan Tahunan menurut Negara Tujuan Utama. [online] Badan Pusat Statistik. Tersedia di: <https://www.bps.go.id/statictable/2019/02/18/2020/ekspor-buah-buahan-tahunan-menurut-negara-tujuan-utama-2012-2022.html> [Diakses 12 Oktober 2023]
Chakraborty, S.K., Subeesh A., Dubey, K., Jat, D., Chandel, N.S., Potdar, R., Rao, N.R.N.V.G., Kumar, D., 2023. Development of an optimally designed real-time automatic citrus fruit grading–sorting machine leveraging computer vision-based adaptive deep learning model. Engineering Applications of Artificial Intelligence.
Ghosh, A., Sufian, A., Sultana, F., Chakrabarti, A., De, D., 2020. Fundamental Concepts of Convolutional Neural Network. In: Balas, V., Kumar, R., Srivastava, R. (eds) Recent Trends and Advances in Artificial Intelligence and Internet of Things. Intelligent Systems Reference Library, vol 172. Springer, Cham. https://doi.org/10.1007/978-3-030-32644-9_36
Gurubelli, Y., Ramanathan, M., & Ponnusamy, P., 2019. Fractional fuzzy 2DLDA approach for pomegranate fruit grade classification. Computers and Electronics in Agriculture, 162, 95-105.
Gyanendra K. Verma, 2023. Multimodal Affective Information Fusion, Multimodal Affective Computing: Affective Information Representation, Modelling, and Analysis 1: 49. https://doi.org/10.2174/9789815124453123010008
Haouhat, A., Bellaouar, S., Nehar, A., & Cherroun, H., 2023. Modality Influence in Multimodal Machine Learning. arXiv preprint arXiv:2306.06476.
Huang, S. C., Pareek, A., Seyyedi, S., Banerjee, I., & Lungren, M. P., 2020. Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ digital medicine, 3(1), 136.
Imran, H., Aamir, K., & Ilyas, M. (2022). Convolutional Neural Network (CNN) with Randomized Pooling.
Kalamkar S, Mary A.G, 2023. Multimodal image fusion: A systematic review. Decision Analytics Journal Volume 9, 100327, ISSN 2772-6622. https://doi.org/10.1016/j.dajour.2023.100327.
Kementerian Perdagangan, 2022. Produk Unggulan Indonesia. [online] Pusat Pelatihan Sumber Daya Manusia Ekspor dan Jasa Perdagangan Kementerian Perdagangan. Tersedia di: <http://ppejp.kemendag.go.id/produk-unggulan-indonesia/> [Diakses 12 Oktober 2023]
Knott, M., Perez-Cruz, F., Defraeye, T., 2023. Facilitated machine learning for image-based fruit quality assessment. Journal of Food Engineering.
Matsuzaka, Y., & Yashiro, R., 2023. AI-Based Computer Vision Techniques and Expert Systems. AI, 4(1), 289-302.
Melessea, T.Y., Bollob, M., Pasqualea, V.D., Centrob, F., & Riemma S., 2022. Machine Learning-Based Digital Twin for Monitoring Fruit Quality Evolution. 3rd International Conference on Industry 4.0 and Smart Manufacturing.
Mundargi, Z., Bhatti, S., Chandra, A., Kamble, A., Jiby, B., & Arole, R. (2023). PrePy - a customize library for data preprocessing in Python. 2023 International Conference for Advancement in Technology (ICONAT). https://doi.org/10.1109/iconat57137.2023.10080134
Novianto, D., & Sugihartono, T., 2020. Sistem Deteksi Kualitas Buah Jambu Air Berdasarkan Warna Kulit Menggunakan Algoritma Principal Component Analysis (Pca) dan K-Nearest Neigbor (K-NN). Jurnal Ilmiah Informatika Global, 11(2).
Papers With Code, tanpa tahun. Max Pooling [online]. Tersedia di: <https://paperswithcode.com/method/max-pooling>
Phung, V. H., & Rhee, E. J., 2019. A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Applied Sciences, 9(21), 4500.
Podareanu, D., Codreanu, V., van Leeuwen, G. C., & Weinberg, V., 2019. Best practice guide-deep learning. Partnership for Advanced Computing in Europe (PRACE), Tech. Rep, 2.
Prasetyo, N. A., Surtono, A., Junaidi, J., & Pauzi, G. A., 2021. Sistem Identifikasi Tingkat Kematangan Buah Nanas Secara Non-Destruktif Berbasis Computer Vision. Journal of Energy, Material, and Instrumentation Technology, 2(1), 1-10.
Rajasree, R., Columbus, C.C., & Shilaja, C., 2020. Multiscale-based multimodal image classification of brain tumor using deep learning method. Neural Computing and Applications.
Sasmi, W.T., Sayuti, M., Yulianti, H.T., & Sulastri, F., 2022. Manfaat Jambu Kristal sebagai Daya Tahan Tubuh di Masa Pandemi COVID-19. Universitas Buana Perjuangan Karawang.
Sulistiyanti, S.R., Setyawan, F.A., & Komarudin, M., 2016. PENGOLAHAN CITRA; Dasar dan Contoh Penerapannya. TEKNOSAIN: Yogyakarta.
Wilson, K., 2023. Digital Images, Resolution, and Color Models. In: Introduction to Photoshop. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-8963-1_9
Yanto, B., Rouza, E., Fimawahib, L., Hayadi, B. H., & Pratama, R. R. (2023). 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, 10(1), 59-66.
Zou, A., 2023. A survey of convolution. Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022) (Vol. 12597, pp. 966-972). SPIE.
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