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Telecommunication Network Interference Analysis Using Naive Bayes Classifier Algorithm

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Part of the Lecture Notes in Networks and Systems book series (volume 457)

Abstract

XYZ company is a telecommunications company that offers a variety of services to Indonesians, including high-speed internet connection via fiber optic lines. The customer data for 1.000 subscribers will be utilized to conduct an analysis of the disruption generated by the communication network or the main device and other supporting devices connected to the internet network in this study. The classification of data in certain classes will be known using the Naive Bayes Classifier Algorithm, and the results of the classification will be used as a solution to calculate the interference that frequently occurs, namely code 1035 interference caused by customer data not having internet service, voice service, or IPTV service. The code 1054 interference was caused by a mismatch of the customer’s active device in the system during the transition or migration from Copper Cable to Fiber Optic (GPON00-GPON05). The probability of interference code 1035 is TP Rate = 0.988, FP Rate = 0.033, Precision Recall = 0.988, F-Measure = 0.988, MCC = 0.971, ROC Area = 1.000 and PRC Area = 1.000. And accuracy by class code 1054 is TP Rate = 0.984, FP Rate = 0.007, Precision Recall = 0.985, F-Measure = 0.982, MCC = 0.979, ROC Area = 1.000 and PRC Area = 1.000.

Keywords

  • Telecommunications network
  • Data mining
  • Naive bayes classifier

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Correspondence to Suhadi Suhadi .

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Marisa, M., Ramli, A.A., Suhadi, S., Sulistyowati, S., Robbani, I.H. (2022). Telecommunication Network Interference Analysis Using Naive Bayes Classifier Algorithm. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_17

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