Implementasi Multimodal Convolutional Neural Network dan Extreme Gradient Boosting untuk Grading Buah Jambu Kristal Skala Industri
Kata Kunci:
grading, multimodal, convolutional neural network, xgboost, jambu kristalAbstrak
Buah jambu kristal merupakan salah satu komoditas buah-buahan yang populer dibudidayakan oleh sektor pertanian di Indonesia. Jambu kristal mem-iliki kandungan gizi atau nutrisi yang berlimpah sehingga buah ini dinilai sangat baik untuk dimanfaatkan dan perlu ditingkatkan kualitas produksinya oleh para petani di Indonesia. Upaya peningkatan pemanfaatan jambu kristal dapat di-capai dengan mengoptimalkan proses produksinya. Pengoptimalan ini dapat dicapai dengan menerapkan otomatisasi pada salah satu proses budidaya yaitu tahapan grading kualitas buah. Tahapan grading yang biasa dilakukan oleh petani dapat diotomatisasi dengan pendekatan computer vision menggunakan algoritma pemrosesan gambar yang multimodal, lebih spesifiknya multimodal Convolutional Neural Network dan Extreme Gradient Boosting. Metode ini akan membuat proses grading otomatis dengan mempelajari masukan data berupa citra buah jambu dari perspektif samping. Percobaan penggabungan dua mo-dalitas algoritma pembelajaran mesin menjadi multimodal CNN dan XGBoost berbasis XGBoost ini merupakan usaha untuk menangani konteks grading buah agar menjadi lebih luas dan lengkap dalam menghasilkan prediksi kualitas buah jambu kristal. Model CNN dilatih dengan data citra samping buah, Model XGBoost dilatih dengan data luas permukaan buah, dan model Multimodal di-latih menggunakan hasil dari prediksi kedua model sebelumnya untuk menemukan pola pada data yang ada. Model dengan kinerja terbaik adalah model multimodal berbasis XGBoost dengan penerapan jenis booster yaitu Gra-dient Boost Tree, learning rate sebesar 0.01, kedalaman tree maksimal sebesar 5, dan minimal bobot tree leaf sebesar 3. Model ini memiliki nilai akurasi sebe-sar 0.92, ROC-AUC sebesar 0.99, dan skor F1 sebesar 0.93.
Referensi
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Herdiat, I., Dwiratna, S. and Kendarto, D.R., 2018. Evaluasi kesesuaian lahan tanaman jambu kristal sebagai upaya perluasan lahan di kabupaten sumedang menggunakan teknik analisis geospasial. In Seminar Nasional Inovasi Produk Pangan Lokal Untuk Mendukung Ketahanan Pangan Universitas Mercu Buana Yogyakarta (pp. 80-86).
Pratidina, R., Syamsun, M. and Wijaya, N.H., 2015. Analisis Pengendalian Mutu Jambu Kristal dengan Metode Six Sigma di ADC IPB-ICDF Taiwan, Bogor. Jurnal Manajemen Dan Organisasi, 6(1), pp.1-18.
Dina, O. M. A., Abdelhalim, R.A., and Elrakha, B. B. 2014. Physicochemical and Nutritional Value of Red and White Guava Cultivars Grown in Sudan. JAAS 2(2):27-30.
Widodo, S.E., Putri, R.A., Waluyo, S. and Zulferiyenni, N., 2021. Deteksi Tingkat Kematangan Buah Jambu Biji (Psidium Guajava L.) Kristal Secara Tak Merusak Dengan Metode Thermal Image.
González-Aguilar, G.A., Tiznado-Hernandez, M.E., Zavaleta-Gatica, R. and Martınez-Téllez, M.A., 2004. Methyl jasmonate treatments reduce chilling injury and activate the defense response of guava fruits. Biochemical and biophysical research communications, 313(3), pp.694-701.
Bobde, Y., Jothi, B., & Sharma, A. Iweews: An Intelligent Waste Extractor For Efficient Waste Segregation By Using Deep Learning.
Zhang, S., Yao, L., Sun, A. and Tay, Y., 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR), 52(1), pp.1-38.
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.
Melesse, 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.
Knott, M., Perez-Cruz, F. and Defraeye, T., 2023. Facilitated machine learning for image-based fruit quality assessment. Journal of Food Engineering, 345, p.111401.
Novianto, D. and 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).
Rajasree, R., Columbus, C.C. and Shilaja, C., 2021. Multiscale-based multimodal image classification of brain tumor using deep learning method. Neural Computing and Applications, 33(11), pp.5543-5553.
Matsuzaka, Y., & Yashiro, R., 2023. AI-Based Computer Vision Techniques and Expert Systems. AI, 4(1), 289-302.
Haouhat, A., Bellaouar, S., Nehar, A., & Cherroun, H., 2023. Modality Influence in Multimodal Machine Learning. arXiv preprint arXiv:2306.06476.
Verma, M., Singh, J. and Kumari, S., 2023, December. Enhancing Heart Disease Prediction with Ensemble Deep Learning and Feature Fusion in a Smart Healthcare Monitoring System. In International Conference on Advanced Computing and Intelligent Technologies (pp. 523-533). Singapore: Springer Nature Singapore.
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.
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
Zou, A., 2023, March. A survey of convolution. In Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022) (Vol. 12597, pp. 966-972). SPIE.
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.
Papers With Code, tanpa tahun. Max Pooling [online]. Tersedia di: https://paperswithcode.com/method/max-pooling
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.
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