Pemodelan Hybrid AI untuk Prediksi Nutrisi Pakan Berbasis Simulasi NIR
DOI:
https://doi.org/10.29407/2cmb3g20Abstract
Analisis kandungan nutrisi pakan ternak merupakan tahapan penting dalam menentukan kualitas dan efisiensi produksi peternakan. Penggunaan alat Near Infrared Reflectance (NIR) mampu memberikan hasil analisis yang cepat, namun biaya perangkat dan pengujiannya relatif tinggi. Penelitian ini bertujuan mengembangkan model hybrid Artificial Intelligence berbasis simulasi spektrum NIR untuk memprediksi kandungan nutrisi pakan secara digital. Dataset yang digunakan berupa data spektral dan data nutrisi beberapa jenis pakan ruminansia. Tahapan penelitian meliputi preprocessing data, normalisasi, pelatihan model Autoencoder sebagai pembangkit sinyal spektral tiruan, serta Artificial Neural Network sebagai model prediksi nutrisi. Evaluasi model dilakukan menggunakan MSE, RMSE, MAE, dan R². Hasil penelitian menunjukkan model hybrid mampu menghasilkan prediksi dengan tingkat kesalahan rendah dan nilai koefisien determinasi tinggi. Sistem yang dikembangkan berpotensi menjadi alternatif simulasi analisis nutrisi pakan berbasis kecerdasan buatan dengan biaya lebih efisien.
Keywords:
Artificial Intelligence, Hybrid Model, NIR, Nutrisi Pakan, Prediksi##plugins.themes.default.displayStats.downloads##
References
[1] N. Q. Aini and A. C. Septaningsih, “Artificial Intelligence untuk
PeningkatanProduktivitas Ternak: Pendekatan Inovatif dalam Peternakan,” Jurnal Ilmiah
Peternakan Halu Oleo, vol. 7, no. 1, pp. 9–17, Jan. 2025, doi: 10.56625/jipho.v7i1.234.
[2] R. A. Anggraeni Kamid, L. Khotijah, and N. R. Kumalasari, “Analisis Keragaman
Kualitas Nutrien Berbagai Pakan Ruminansia di Wilayah Indonesia,” Jurnal Ilmu Nutrisi
dan Teknologi Pakan, vol. 22, no. 1, pp. 14–22, Apr. 2024, doi: 10.29244/jintp.22.1.14-
22.
[3] C. Maduro Dias, H. Nunes, and A. Borba, “Near-Infrared Spectroscopy in Animal
Nutrition: Historical Insights, Technical Principles, and Practical Applications,” 2024,
Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/analytica5040033.
[4] R. Zahera, L. A. Sari, I. G. Permana, and Despal, “The use of near-infrared reflectance
spectroscopy (NIRS) to predict dairy fibre feeds in vitro digestibility,” in IOP Conference
Series: Earth and Environmental Science, IOP Publishing Ltd, Jan. 2022. doi:
10.1088/1755-1315/951/1/012100.
[5] W. Zhang, L. C. Kasun, Q. J. Wang, Y. Zheng, and Z. Lin, “A Review of Machine
Learning for Near-Infrared Spectroscopy,” Dec. 01, 2022, MDPI. doi:
10.3390/s22249764.
[6] C. Jiang, J. Zhao, Y. Ding, and G. Li, “Vis–NIR Spectroscopy Combined with GAN Data
Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the
Qinghai–Tibet Plateau,” Sensors, vol. 23, no. 7, Apr. 2023, doi: 10.3390/s23073686.
[7] X. Xin et al., “Combination of near-infrared spectroscopy with Wasserstein generative
adversarial networks for rapidly detecting raw material quality for formula products,” Opt.
Express, vol. 32, no. 4, p. 5529, Feb. 2024, doi: 10.1364/oe.516341.
[8] N. J. Palange et al., “Artificial intelligence-driven near-infrared spectrophotometry model
for rapid quantification of anti-nutritional factors in soybean (Glycine max.),” Discover
Applied Sciences, vol. 7, no. 6, Jun. 2025, doi: 10.1007/s42452-025-07235-3.
[9] S. Wulandari, M. Adhyatma, D. Pantaya, A. Jayanegara, and R. A. Nurfitriani, “Near
infrared spectroscopy for the quality control of rice bran,” Livestock and Animal
Research, vol. 20, no. 2, p. 210, Jul. 2022, doi: 10.20961/lar.v20i2.54353.
[10] F. Yuwita, I. Ifmalinda, and M. Makky, “Prediction of Caffeine and Protein of Arabica
Coffee Beans Using Near Infrared Spectroscopy (NIRS),” Jurnal Teknik Pertanian
Lampung (Journal of Agricultural Engineering), vol. 12, no. 4, p. 852, Dec. 2023, doi:
10.23960/jtep-l.v12i4.852-862.
[11] M. E. Hossain, M. A. Kabir, L. Zheng, D. L. Swain, S. McGrath, and J. Medway, “Nearinfrared spectroscopy for analysing livestock diet quality: A systematic review,” Heliyon,
vol. 10, no. 22, Nov. 2024, doi: 10.1016/j.heliyon.2024.e40016.
[12] S. Tkatek, S. Amassmir, A. Belmzoukia, and J. Abouchabaka, “Predictive fertilization
models for potato crops using machine learning techniques in Moroccan Gharb region,”
International Journal of Electrical and Computer Engineering, vol. 13, no. 5, pp. 5942–
5950, Oct. 2023, doi: 10.11591/ijece.v13i5.pp5942-5950.
[13] Y. L. R. Tulung, A. F. Pendong, J. J. M. Londok, C. A. Rahasia, and S. A. E. Moningkey,
“Ilmu Nutrisi Ternak dan Pengetahuan Bahan Pakan,” pp. 1–69, 2022.
[14] J. Ha, M. Kambe, and J. Pe, Data Mining: Concepts and Techniques. 2011. doi:
10.1016/C2009-0-61819-5.
[15] A. Ashfahani, M. Pratama, E. Lughofer, and Y. S. Ong, “DEVDAN: Deep evolving
denoising autoencoder,” Neurocomputing, vol. 390, pp. 297–314, 2020, doi:
10.1016/j.neucom.2019.07.106.
[16] A. Manconi, G. Armano, M. Gnocchi, and L. Milanesi, “A Soft-Voting Ensemble
Classifier for Detecting Patients Affected by COVID-19,” Applied Sciences
(Switzerland), vol. 12, no. 15, 2022, doi: 10.3390/app12157554.
[17] S. Mehmood and U. M. Mataram, “Parameter Tuning in Backpropagation Neural
Networks :,” JTIK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), vol. 11, no. 1,
pp. 110–124, 2025, doi: 10.33480/jitk.v11i1.6484.INTRODUCTION.
[18] D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared
is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis
evaluation,” PeerJ Comput. Sci., vol. 7, pp. 1–24, 2021, doi: 10.7717/PEERJ-CS.623.
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