Klasifikasi Citra Dalam Identifikasi Jeruk Nipis dan Jeruk Mandarin Menggunakan Convolutional Neural Network (CNN)
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Nov 22, 2024
Kata Kunci:
Identifikasi Jenis buah,pengolahan citra
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Abstrak
Dengan berkembang pesatnya teknologi di bidang citra gambar (image processing) sangat membantu petani dalam identifikasi jenis buah jeruk.Penelitian ini bertujuan untuk mengidentifikasi perbedaan antara jeruk nipis (Citrus aurantiifolia) dan jeruk mandarin (Citrus reticulata) menggunakan metode pengolahan citra dan analisis morfologi. Pengolahan citra dilakukan untuk menganalisis perbedaan visual berdasarkan warna, tekstur, dan ukuran buah. Selain itu, analisis kimia dilakukan untuk membedakan komposisi senyawa-senyawa yang terkandung pada kedua jenis jeruk. Hasil penelitian menunjukkan bahwa pendekatan ini mampu mengidentifikasi perbedaan antara jeruk nipis dan jeruk mandarin dengan tingkat akurasi yang tinggi, serta dapat diaplikasikan dalam berbagai industri, termasuk pertanian dan pengolahan makanan
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Saputra, R., Erlanda, H., & Ramadhanu, A. (2024). Klasifikasi Citra Dalam Identifikasi Jeruk Nipis dan Jeruk Mandarin Menggunakan Convolutional Neural Network (CNN). JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 5(4), 213 - 218. https://doi.org/10.62527/jitsi.5.4.282
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Referensi
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2. Budianto, M. et al. (2019). Karakterisasi Sifat Kimia Jeruk. Jurnal Agronomi dan Hortikultura, 7(2), 89-96.
3. Cahyono, R., & Sari, M. (2021). Analisis Kandungan Nutrisi pada Jeruk. Jurnal Gizi Tropis, 11(1), 102-108.
4. Dewi, R. et al. (2022). Vitamin C dan Antioksidan pada Jeruk. Jurnal Biologi Tropika, 15(4), 130-137.
5. Fadhilah, A. et al. (2020). Uji Organoleptik Jeruk Nipis dan Mandarin. Jurnal Teknologi Pangan, 8(1), 45-52.'
6. FF Maulana, N Rochmawati (2019). Klasifikasi citra buah menggunakan convolutional neural network.Jurnal of Informatics and Computer Science
7. Ahmad, Z., & Hidayat, T. (2020). Kandungan Kimia dan Manfaat Kesehatan dari Buah Jeruk. Jurnal Agrikultur Indonesia, 15(2), 123-130.
8. Susanto, R., & Wulandari, A. (2019). Analisis Morfologi dan Kimia pada Varietas Jeruk. Jurnal Biologi Tropika, 12(3), 98-107.
9. Suryani, S., et al. (2022). "Nutritional and Health Benefits of Citrus Aurantiifolia." Journal of Nutritional Science, 11(3), 215-228.
10. Hasan, M., et al. (2021). "Morphological and Chemical Characteristics of Citrus Reticulata." International Journal of Agriculture and Food Research, 12(2), 113-126.
11. Zhang, Y., et al. (2020). "Comparative Study on Vitamin C Content in Citrus Fruits." Food Chemistry, 315, 126-134.
12. Lee, J., et al. (2023). "The Impact of Citrus Aurantiifolia on Immune System Enhancement." Journal of Functional Foods, 54, 142-150.
13. Kumar, P., et al. (2019). "Health Benefits and Nutritional Values of Mandarin Oranges." Journal of Medicinal Food, 22(4), 354-361.
14. Ali, M., et al. (2022). "Antioxidant Properties of Citrus Fruits and Their Potential Health Benefits." Antioxidants, 11(1), 32-45.
15. Smith, R., et al. (2021). "Comparison of Flavonoid and Carotenoid Content in Citrus Fruits." Plant Foods for Human Nutrition, 76(1), 58-65.
16. Chen, L., et al. (2020). "Role of Citrus Fruits in Cardiovascular Health." Journal of Cardiovascular Research, 89(5), 223-230.
17. Thompson, A., et al. (2022). "Dietary Fiber Content in Citrus Fruits and Its Health Implications." Nutritional Research Reviews, 35(2), 88-97.
18. Patel, R., et al. (2021). "Nutritional Comparison of Citrus Aurantiifolia and Citrus Reticulata." Journal of Food Science and Technology, 58(7), 2923-2931.
19. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
20. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444.
21. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25, 1097-1105.
22. Russakovsky, O., & Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211-252.
23. Redmon, J., Divvala, S., & Girshick, R. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779-788.