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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Intelligent Informatics
Khairun Nidzam Ramli
Universiti Tun Hussein Onn Malaysia
Computer Science & IT

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Journal : International Journal of Advances in Intelligent Informatics

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Cable fault classification in ADSL copper access network using machine learning Nurul Bashirah Ghazali; Dang Fillatina Hashim; Fauziahanim Che Seman; Khalid Isa; Khairun Nidzam Ramli; Zuhairiah Zainal Abidin; Saizalmursidi Md Mustam; Mohammed Al Haek
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.488

Abstract

Asymmetrical Digital Subscriber Line (ADSL) is the technology widely deployed worldwide, but its performance may be limited with respect to its intrinsic. The nature of the copper cable causes it to be more susceptible to signal degradation and faulty line. Common ADSL line faults are short-wired fault, open-wired fault, bridge taps, and uneven pair. However, ADSL technology is still one of the most established networks, and users in the suburban area still depend on the technology to access the internet service. This paper discussed and compared a machine learning algorithm based on Decision Trees (J48), K-Nearest Neighbor, Multi-level Perceptron, Naïve Bayes, Random Forest, and Sequential Minimal Optimization (SMO) for ADSL line impairment that affects the line operation performance concerning their percentage of accuracy. Resulting from classifications done using algorithms as mentioned above, the random forest algorithm gives the highest overall accuracy for the ADSL line impairment dataset. The best algorithm for classifying DSL line impairment is chosen based on the highest accuracy percentage. The accomplishment classification of fault type in the ADSL copper access network project may benefit the telecommunication network provider by remotely assessing the network condition rather than on-site.
Co-Authors Dang Fillatina Hashim Fauziahanim Che Seman Khalid Isa Mohamad Md Som Mohammed Al Haek Mohd Noh Dalimin Mohd Shamian Zainal Nor Azizi Yusoff Nurul Bashirah Ghazali Saizalmursidi Md Mustam Shipun Anuar Hamzah Wahyu Mulyo Utomo Zuhairiah Zainal Abidin