Teknik Machine Learning dengan Metode SVM untuk Deteksi Anomali pada Kendala Jaringan FTTH
Abstract
FTTH is a technology that is important in providing high-speed internet services to customers. However, interruptions or anomalies in the FTTH network may cause service interruptions that impact user experience.
Anomaly data from FTTH telecommunications networks are collected and processed using preprocessing techniques to prepare data before being used in SVM model training. The training process is carried out by using training data to classify data as normal or anomaly. After the training, an evaluation of the performance of the SVM model was carried out using test data that had never been seen before.
The results of the analysis show the ability of the SVM model to detect anomalies in FTTH telecommunication networks with high accuracy and good performance. The conclusion of this study is that machine learning techniques with the SVM method have great potential in anomaly detection in FTTH telecommunication networks.
Downloads
Download data is not yet available.
References
[1] Chowdhury, A. A., Islam, M. R., & Mahmud, S. M. (2017). Design and implementation of GPON FTTH access network. 2017 4th International Conference on Electrical Engineering and Information Communication Technology (ICEEICT). IEEE.
[2] Kaur, N., & Singh, A. (2019). Performance evaluation of GPON-FTTH network using OPNET. 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE.
[3] Pinninti, V., & Gandham, S. (2015). GPON: Next generation FTTH technology for broadband access. 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE.
[4] Sharma, N., & Kumar, A. (2016). A comparative study of GPON, EPON and FTTH architectures for fiber access networks. 2016 International Conference on Next Generation Intelligent Systems (ICNGIS). IEEE.
[5] Rezaei, A., Rezaei, S., & Yazdi, M. S. (2018). Performance evaluation of GPON, EPON, and WDM-PON for FTTH applications. Optical and Quantum Electronics, 50(5), 1-14.
[6] Rezaei, A., Rezaei, S., & Yazdi, M. S. (2019). Performance analysis of GPON, EPON and WDM-PON for FTTH applications. Optical Switching and Networking, 31, 41-49.
[7] Rahman, M. M., Rahman, M. S., & Moniruzzaman, M. (2014). A comparative analysis of GPON, EPON, and GEPON. International Journal of Scientific & Engineering Research, 5(6), 1306-1313.
[8] Zhang, L., & Ma, J. (2018). Design and analysis of FTTH system based on GPON technology. Journal of Physics: Conference Series, 1096(1), 012012.
[9] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
[10] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
[11] Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14(3), 199-222.
[12] Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines. Microsoft Research.
[13] Steinwart, I., & Christmann, A. (2008). Support Vector Machines. Springer.
[14] Moukafih, B., Outtagarts, A., & Bouhorma, M. (2021). Machine learning for anomaly detection in optical networks: Techniques, applications and research challenges. Optical Switching and Networking, 41, 100639
[15] Kaur, H., Arora, A., & Saini, H. (2020). A systematic review on machine learning techniques for diagnosis and prognosis of COVID-19. Journal of Healthcare Engineering, 2020, 8881026.
[16] Li, T., Li, Y., & Wang, M. (2022). A comprehensive survey on deep learning-based methods for image segmentation. Pattern Recognition, 122, 108376
[17] Elsayed, S., & Sarker, I. H. (2021). Machine learning for cybersecurity: A comprehensive survey. IEEE Access, 9, 179620–179648.
[18] Shawe-Taylor, J., & Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press.
[19] Schölkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press.
[20] Bakhshi, T. A., & Ghita, B. (2020). Anomaly detection in network traffic: A machine learning perspective. Journal of Network and Computer Applications, 170, 102807.
[21] Akbar, M., Kavitha, V., & Goyal, N. (2022). A survey on ML-based anomaly detection in communication networks: Challenges and opportunities. Computer Networks, 203, 108640.
[22] Silva, L. M., Rios, R. E., Silva, W. S., & Nogueira, J. M. (2021). Using deep learning for real-time anomaly detection in optical access networks. IEEE Access, 9, 12649–12660.
[23] Tufekci, P., & Demir, Ö. (2021). Anomaly detection in telecommunications network traffic using unsupervised machine learning algorithms. Expert Systems with Applications, 183, 115352.
[24] Alawe, I., Carela-Español, V., Barlet-Ros, P., & Sole-Pareta, J. (2020). Anomaly detection using machine learning in 5G networks. IEEE Transactions on Network and Service Management, 17(4), 2145–2159.
[25] Wang, X., Gao, J., Chen, H., & Chen, Y. (2021). Autoencoder-based anomaly detection for optical fiber network faults. Optik, 226, 165963.
[26] Patcha, A., & Park, J. M. (2007). An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 51(12), 3448-3470.
[27] Aggarwal, C. C. (2016). Outlier analysis. Springer.
[28] Markou, M., & Singh, S. (2003). Novelty detection: A review—Part 1: Statistical approaches. Signal Processing, 83(12), 2481-2497.
[29] Chandola, V., & Kumar, V. (2012). Big data analytics: Anomaly detection techniques for large-scale and time-series data. In Data mining and knowledge discovery handbook (pp. 1069-1089). Springer.
[30] Pedoman Desain dan Perencanaan Integrated Optical Distribution Network (I-ODN) PP Direksi Telkom Indonesia No. PR.402.08/r.00/TK.000/COO-D00000/2019
[31] Pedoman Operasi dan Pemeliharaan Jaringan Integrated Optical Distribution Network (I-ODN) PP Direksi Telkom Indonesia No. PR.402.09/r.00/TK.000/COO-D00000/2021Zeb, K., Shami, A., & Malik, A. W. (2020). A review of green communication in 5G and beyond: Recent trends and open research challenges. Computer Networks, 178, 107200.
[32] Tata Kelola Instalasi Kabel Premises Broadband Jaringan Integrated Optical Distribution Network (I-ODN) PP Direksi Telkom Indonesia No. PR.402.03/r.00/TK.000/COO-A00000/2014
[33] Pedoman Instalasi dan Pedoman Pemasangan Jaringan Fiber to the Home (PPJ FTTH). Nomor Dokumen: PED F-014-2013 R&D Center Telkom Indonesia. 30 Desember 2013
[2] Kaur, N., & Singh, A. (2019). Performance evaluation of GPON-FTTH network using OPNET. 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE.
[3] Pinninti, V., & Gandham, S. (2015). GPON: Next generation FTTH technology for broadband access. 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE.
[4] Sharma, N., & Kumar, A. (2016). A comparative study of GPON, EPON and FTTH architectures for fiber access networks. 2016 International Conference on Next Generation Intelligent Systems (ICNGIS). IEEE.
[5] Rezaei, A., Rezaei, S., & Yazdi, M. S. (2018). Performance evaluation of GPON, EPON, and WDM-PON for FTTH applications. Optical and Quantum Electronics, 50(5), 1-14.
[6] Rezaei, A., Rezaei, S., & Yazdi, M. S. (2019). Performance analysis of GPON, EPON and WDM-PON for FTTH applications. Optical Switching and Networking, 31, 41-49.
[7] Rahman, M. M., Rahman, M. S., & Moniruzzaman, M. (2014). A comparative analysis of GPON, EPON, and GEPON. International Journal of Scientific & Engineering Research, 5(6), 1306-1313.
[8] Zhang, L., & Ma, J. (2018). Design and analysis of FTTH system based on GPON technology. Journal of Physics: Conference Series, 1096(1), 012012.
[9] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
[10] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
[11] Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14(3), 199-222.
[12] Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines. Microsoft Research.
[13] Steinwart, I., & Christmann, A. (2008). Support Vector Machines. Springer.
[14] Moukafih, B., Outtagarts, A., & Bouhorma, M. (2021). Machine learning for anomaly detection in optical networks: Techniques, applications and research challenges. Optical Switching and Networking, 41, 100639
[15] Kaur, H., Arora, A., & Saini, H. (2020). A systematic review on machine learning techniques for diagnosis and prognosis of COVID-19. Journal of Healthcare Engineering, 2020, 8881026.
[16] Li, T., Li, Y., & Wang, M. (2022). A comprehensive survey on deep learning-based methods for image segmentation. Pattern Recognition, 122, 108376
[17] Elsayed, S., & Sarker, I. H. (2021). Machine learning for cybersecurity: A comprehensive survey. IEEE Access, 9, 179620–179648.
[18] Shawe-Taylor, J., & Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press.
[19] Schölkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press.
[20] Bakhshi, T. A., & Ghita, B. (2020). Anomaly detection in network traffic: A machine learning perspective. Journal of Network and Computer Applications, 170, 102807.
[21] Akbar, M., Kavitha, V., & Goyal, N. (2022). A survey on ML-based anomaly detection in communication networks: Challenges and opportunities. Computer Networks, 203, 108640.
[22] Silva, L. M., Rios, R. E., Silva, W. S., & Nogueira, J. M. (2021). Using deep learning for real-time anomaly detection in optical access networks. IEEE Access, 9, 12649–12660.
[23] Tufekci, P., & Demir, Ö. (2021). Anomaly detection in telecommunications network traffic using unsupervised machine learning algorithms. Expert Systems with Applications, 183, 115352.
[24] Alawe, I., Carela-Español, V., Barlet-Ros, P., & Sole-Pareta, J. (2020). Anomaly detection using machine learning in 5G networks. IEEE Transactions on Network and Service Management, 17(4), 2145–2159.
[25] Wang, X., Gao, J., Chen, H., & Chen, Y. (2021). Autoencoder-based anomaly detection for optical fiber network faults. Optik, 226, 165963.
[26] Patcha, A., & Park, J. M. (2007). An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 51(12), 3448-3470.
[27] Aggarwal, C. C. (2016). Outlier analysis. Springer.
[28] Markou, M., & Singh, S. (2003). Novelty detection: A review—Part 1: Statistical approaches. Signal Processing, 83(12), 2481-2497.
[29] Chandola, V., & Kumar, V. (2012). Big data analytics: Anomaly detection techniques for large-scale and time-series data. In Data mining and knowledge discovery handbook (pp. 1069-1089). Springer.
[30] Pedoman Desain dan Perencanaan Integrated Optical Distribution Network (I-ODN) PP Direksi Telkom Indonesia No. PR.402.08/r.00/TK.000/COO-D00000/2019
[31] Pedoman Operasi dan Pemeliharaan Jaringan Integrated Optical Distribution Network (I-ODN) PP Direksi Telkom Indonesia No. PR.402.09/r.00/TK.000/COO-D00000/2021Zeb, K., Shami, A., & Malik, A. W. (2020). A review of green communication in 5G and beyond: Recent trends and open research challenges. Computer Networks, 178, 107200.
[32] Tata Kelola Instalasi Kabel Premises Broadband Jaringan Integrated Optical Distribution Network (I-ODN) PP Direksi Telkom Indonesia No. PR.402.03/r.00/TK.000/COO-A00000/2014
[33] Pedoman Instalasi dan Pedoman Pemasangan Jaringan Fiber to the Home (PPJ FTTH). Nomor Dokumen: PED F-014-2013 R&D Center Telkom Indonesia. 30 Desember 2013
Published
2025-07-29
How to Cite
KARDIYASA, Komang Agus Putra; SANTIKA, Putu Agus.
Teknik Machine Learning dengan Metode SVM untuk Deteksi Anomali pada Kendala Jaringan FTTH.
Majalah Ilmiah Teknologi Elektro, [S.l.], v. 24, n. 1, p. 71-78, july 2025.
ISSN 2503-2372.
Available at: <http://ojs.unud.ac.id/index.php/mite/article/view/125338>. Date accessed: 29 sep. 2025.
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License
