KINERJA ALGORITMA BACKPROPAGATION DAN RNN DALAM PREDIKSI BEBAN JARINGAN
Abstract
Bandwidth allocation optimization is crucial to ensure optimal network performance and user satisfaction. This research aims to identify the best machine learning algorithm between backpropagation and recurrent neural network (RNN) in predicting network load, using two different datasets. The main issue addressed is how to choose the right algorithm for network load prediction to optimize bandwidth allocation. The CRISP-DM methodology was used as the research framework, with four evaluation metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results showed that the backpropagation algorithm provided the best performance on the data with the lowest evaluation matrix: MAE 0.0203, MSE 0.0007, RMSE 0.0281, and MAPE 20%. In conclusion, the backpropagation algorithm is more suitable for predicting bandwidth requirements compared to RNN based on the evaluation metrics used, making it reliable for bandwidth allocation optimization.
References
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[2] B. Badieah, R. Gernowo, and B. Surarso, “Metode Jaringan Syaraf Tiruan Untuk Prediksi Performa Mahasiswa Pada Pembelajaran Berbasis Problem Based Learning (PBL),” J. Sist. Inf. Bisnis, vol. 6, no. 1, p. 46, 2016, doi: 10.21456/vol6iss1pp46-58.
[3] M. E. Karim, M. M. S. Maswood, S. Das, and A. G. Alharbi, “BHyPreC: A Novel Bi-LSTM Based Hybrid Recurrent Neural Network Model to Predict the CPU Workload of Cloud Virtual Machine,” IEEE Access, vol. 9, pp. 131476–131495, 2021, doi: 10.1109/ACCESS.2021.3113714.
[4] L. A. J. Mesquita, K. D. R. Assis, L. V. B. C. Pinho, R. C. Almeida, E. F. Simas Filho, and M. S. Alencar, “Resource planning on elastic optical networks using traffic matrix prediction,” AEU - Int. J. Electron. Commun., vol. 134, p. 153615, 2021, doi: 10.1016/j.aeue.2021.153615.
[5] N. A. S. Al-Jamali and H. S. Al-Raweshidy, “Intelligent traffic management and load balance based on spike isdn-iot,” IEEE Syst. J., vol. 15, no. 2, pp. 1640–1651, 2021, doi: 10.1109/JSYST.2020.2996185.
[6] M. Habib, T. O. Timoudas, Y. Ding, N. Nord, S. Chen, and Q. Wang, “A hybrid machine learning approach for the load prediction in the sustainable transition of district heating networks,” Sustain. Cities Soc., vol. 99, no. February, p. 104892, 2023, doi: 10.1016/j.scs.2023.104892.
[7] L. Dostal et al., “Predictability of vibration loads from experimental data by means of reduced vehicle models and machine learning,” IEEE Access, vol. 8, pp. 177180–177194, 2020, doi: 10.1109/ACCESS.2020.3027499.
[8] E. G. Tajeuna, M. Bouguessa, and S. Wang, “Mining Customers’ Changeable Electricity Consumption for Effective Load Forecasting,” ACM Trans. Intell. Syst. Technol., vol. 12, no. 4, 2021, doi: 10.1145/3466684.
[9] Y. Kim and S. Kim, “Electricity load and internet traffic forecasting using vector autoregressive models,” Mathematics, vol. 9, no. 18, 2021, doi: 10.3390/math9182347.
[10] C. J. Huang, K. W. Hu, and H. W. Cheng, “An Adaptive Bandwidth Management Algorithm for Next-Generation Vehicular Networks,” Sensors, vol. 23, no. 18, 2023, doi: 10.3390/s23187767.
[11] S. Venkatapathy, T. Srinivasan, H. G. Jo, and I. H. Ra, “An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning,” Sensors, vol. 23, no. 23, pp. 1–20, 2023, doi: 10.3390/s23239608.
[12] A. R. Abdellah, O. A. Mahmood, R. Kirichek, A. Paramonov, and A. Koucheryavy, “Machine learning algorithm for delay prediction in IoT and tactile internet,” Futur. Internet, vol. 13, no. 12, 2021, doi: 10.3390/fi13120304.
[13] B. Liu et al., “Adaptive dynamic wavelength and bandwidth allocation algorithm based on error-back-propagation neural network prediction,” Opt. Commun., vol. 437, no. 10, pp. 276–284, 2019, doi: 10.1016/j.optcom.2018.12.064.
[14] L. Shan, O. Zhao, K. Temma, K. Hattori, F. Kojima, and F. Adachi, “Evaluation of Machine Learnable Bandwidth Allocation Strategy for User Cooperative Traffic Forwarding,” IEEE Access, vol. 7, pp. 85213–85225, 2019, doi: 10.1109/ACCESS.2019.2925431.
[15] J. Song, L. Zhang, G. Xue, Y. P. Ma, S. Gao, and Q. L. Jiang, “Predicting hourly heating load in a district heating system based on a hybrid CNN-LSTM model,” Energy Build., vol. 243, p. 110998, 2021, doi: 10.1016/j.enbuild.2021.110998.
[16] W. Li, C. Zang, D. Liu, and P. Zeng, “Short-term load forecasting of long-short term memory neural network based on genetic algorithm,” 2020 IEEE 4th Conf. Energy Internet Energy Syst. Integr. Connect. Grids Towar. a Low-Carbon High-Efficiency Energy Syst. EI2 2020, pp. 2518–2522, 2020, doi: 10.1109/EI250167.2020.9346907.
[17] D. Wen, M. Bennis, and K. Huang, “Joint parameter-and-bandwidth allocation for improving the efficiency of partitioned edge learning,” IEEE Trans. Wirel. Commun., vol. 19, no. 12, pp. 8272–8286, 2020, doi: 10.1109/TWC.2020.3021177.
[18] X. Ma, Y. Jin, and Q. Dong, “A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting,” Appl. Soft Comput. J., vol. 54, pp. 296–312, 2017, doi: 10.1016/j.asoc.2017.01.033.
[19] H. Eskandari, M. Imani, and M. P. Moghaddam, “Convolutional and recurrent neural network based model for short-term load forecasting,” Electr. Power Syst. Res., vol. 195, no. February, p. 107173, 2021, doi: 10.1016/j.epsr.2021.107173.
[20] A. T. Liem, I. Hwang, E. Ganesan, S. W. Taju, and G. A. Sandag, “Bandwidth Allocation Based on Long-Short-Term-Memory in NG-EPON,” no. July, pp. 82095–82107, 2023.
[21] A. I. Arvanitidis, D. Bargiotas, A. Daskalopulu, V. M. Laitsos, and L. H. Tsoukalas, “Enhanced short-term load forecasting using artificial neural networks,” Energies, vol. 14, no. 22, pp. 1–14, 2021, doi: 10.3390/en14227788.
[22] Q. Jiang, Y. Cheng, H. Le, C. Li, and P. X. Liu, “A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting,” 2022.
[23] A. A. Ghozi, A. Aprianti, A. D. P. Dimas, and R. Fauzi, “Analisis Prediksi Data Kasus Covid-19 di Provinsi Lampung Menggunakan Recurrent Neural Network (RNN),” Indones. J. Appl. Math., vol. 2, no. 1, p. 25, 2022, doi: 10.35472/indojam.v2i1.763.
[24] J. Patterson and A. Gibson, Deep Learning A PRACTITIONER’S APPROACH, vol. 29, no. 7553. 2016. [Online]. Available: http://deeplearning.net/
[25] M. I. Sunu Jatmika, Tria Aprilianto, “EKSTRAKSI FITUR UNTUK MENGIDENTIFIKASI MARGA TANAMAN MENGGUNAKAN ALGORITMA BACKPROPAGATION,” J. Sist. dan Teknol. Inf., vol. Vol 6 No 1, no. Vol. 6 No. 1 (2020): Positif : Jurnal Sistem dan Teknologi Informasi, 2020.
Published
2024-12-31
How to Cite
ANNISAH, Wulan Nur; HARANI, Nisa Hanum; HABIBI, Roni.
KINERJA ALGORITMA BACKPROPAGATION DAN RNN DALAM PREDIKSI BEBAN JARINGAN.
Jurnal Teknik Informasi dan Komputer (Tekinkom), [S.l.], v. 7, n. 2, p. 1044-1053, dec. 2024.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1586>. Date accessed: 23 mar. 2025.
doi: https://doi.org/10.37600/tekinkom.v7i2.1586.
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