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Journal : Journal of Applied Data Sciences

Congestion Predictive Modelling on Network Dataset Using Ensemble Deep Learning Purnawansyah, Purnawansyah; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna; Haviluddin, Haviluddin; Raja, Roesman Ridwan; Darwis, Herdianti; Nafalski, Andrew
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.333

Abstract

Network congestion arises from factors like bandwidth misallocation and increased node density leading to issues such as reduced packet delivery ratios and energy efficiency, increased packet loss and delay, and diminished Quality of Service and Quality of Experience. This study highlights the potential of deep learning and ensemble learning for network congestion analysis, which has been less explored compared to packet-loss based, delay-based, hybrid-based, and machine learning approaches, offering opportunities for advancement through parameter tuning, data labeling, architecture simulation, and activation function experiments, despite challenges posed by the scarcity of labeled data due to the high costs, time, computational resources, and human effort required for labeling. In this paper, we investigate network congestion prediction using deep learning and observe the results individually, as well as analyze ensemble learning outcomes using majority voting, from data that we recorded and clustered using K-Means. We leverage deep learning models including BPNN, CNN, LSTM, and hybrid LSTM-CNN architectures on 12 scenarios formed out of the combination of level datasets, normalization techniques, and number of recommended clusters and the results reveal that ensemble methods, particularly those integrating LSTM and CNN models (LSTM-CNN), consistently outperform individual deep learning models, demonstrating higher accuracy and stability across diverse datasets. Besides that, it is preferably recommended to use the QoS level dataset and the combinations of 3 clusters due to the most consistent evaluation results across different configurations and normalization strategies. The ensemble learning evaluation results show consistently high performance across various metrics, with accuracy, Matthews Correlation Coefficient, and Cohen's Kappa values nearing 100%, indicates excellent predictive capability and agreement. Hamming Loss remains minimal highlighting the low misclassification rates. Notably, this study advances predictive modeling in network management, offering strategies to enhance network efficiency and reliability amidst escalating traffic demands for more sustainable network operations.
Co-Authors - Ardiansyah, - Abdul Hadi, Afif Adam Ramadhani P Adiba Qonita Ahmad Farobi Ahmad Fuadi Aji P Wibawa Aji Prasetya Wibawa Ali, Waleed Annas Gading Pertiwi Arif Mudi Priyatno Aya Shofia Mufti Bambang Nurdewanto Bintang Romadhon Binti Afifah Brilliant, Muhammad Zidan Budi Wibowotomo Darwis, Herdianti Dasuki, Moh. Didik Dwi Prasetya Ega Gefrie Febriawan Elta Sonalitha Fadhlullah, Aufar Faiq Fadli Hidayat, M. Noer Falah, Moh Zainul Fitriyah Fitriyah Fitriyah Fitriyah Gading Pertiwi, Annas Gamma Fitrian Permadi Hairani Hairani Hakkun Elmunsyah Haviluddin Haviluddin Hazizah, Chalista Yulia Heru Wahyu Herwanto Hirashima, Tsukasa I Made Wirawan Imansyah, Pranadya Bagus Indriana, Poppy Kornelius Kamargo/Irawan Dwi Wahyono Kornelius Kamargo Kurniawan, Rizky Rizaldi M. Ardhika Mulya Pratama M. Habibullah Arief M. Zainal Arifin Martin Indra Wisnu Prabowo Maryani, Sri Moh Zainul Falah Moh. Robieth Alfan Alhamid Mohamad Yusuf Kurniawan Muhammad Afnan Habibi Muhammad Firman Aji Saputra Muhammad Iqbal Akbar Muhammad Jauharul Fuady Muhammad Rizki Irwanto Mulki Indana Zulfa, Mulki Indana Mulya Pratama, M. Ardhika Nafalski, Andrew Natasya Titania Ramadhanti Nazhiroh Tahta Arsyillah Nurhidayati Pindo Tutuko Poppy Indriana Purnawansyah Purnawansyah Qonita, Adiba Raja, Roesman Ridwan Ramdhani, Muhammad Ilham Rendy Yani Susanto Rhomdani, Rohmad Wahid Rismayanti, Nurul Rizal, Muhammad Fatkhur Rosydah, Lucyta Qutsyaning Saifudin, Ilham Salsabila, Nabila Sari, Heni Vidia Satria Putra Pratama Setiadi Cahyono Putro Shandy Krisnawan Sihombing, Wesly M Siti Sendari Soenar Soekopitojo Soraya Norma Mustika Sri Farida Utami Suastika Yulia Riska Sucipto Sucipto Sucipto Sucipto Sujito Sujito Syaad Patmanthara Syah, Abdullah Iskandar Syamsul Arifin Utomo Pujianto Wahyu Caesarendra Wahyu Sakti Gunawan Wahyu Sakti Gunawan Irianto Wibawa, Aji P Wisnu Prabowo, Martin Indra Yogi Dwi Mahandi Yuniardini, Fatma