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.
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References
Chyzhmar, K., Dniprov, O., Korotiuk, O., Shapoval, R., Sydorenko, O.: State information security as a challenge of information and computer technology development. J. Secur. Sustain. Issues 9(3) (2020)
Votinova, E., Votinov, M.: Information society: analyzing problems and prospects of using information technologies, computers and communication networks. Webology 16(1) (2019)
Tomasov, A., Holik, M., Oujezsky, V., Horvath, T., Munster, P.: GPON PLOAMd message analysis using supervised neural networks. Appl. Sci. (Switz.) 10(22), 8139 (2020)
E-commerce-businnes-technology-society. Int. J. Eng. Technol. Manag. Res. 4(10) (2020)
Gupta, A., Srivastava, A., Bohara, V.: Resource allocation in solar-powered FiWi networks. IEEE Access 8, 198691–198705 (2020)
Furda, A., Fidge, C., Barros, A.: A practical approach for detecting multi-tenancy data interference. Sci. Comput. Program. 163, 160–173 (2018)
Ain, K., Hidayati, H., Aulia Nastiti, O.: Expert system for stroke classification using naive bayes classifier and certainty factor as diagnosis supporting device. J. Phys.: Conf. Ser. 1–7 (2020)
Putri, N., Apriono, C., Natali, Y.: Increasing residential capacity in gigabit-capable passive optical network using high splitting ratio. In: 2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020 (2020)
Amaya, L., et al.: Implementation of the pineaplle tetra module for an audit process through impersonation of aps in the 2.4 and 5 GHz band in the telecommunications laboratory of the electronics and telecommunications career. RISTI – Rev. Iberica Sist. Tecnol. Inform. 2020(E27) (2020)
Rusek, J.: The point nuisance method as a decision-support system based on Bayesian inference approach. Arch. Min. Sci. 65(1) (2020)
Oztemel, E., Gursev, S.: Literature review of Industry 4.0 and related technologies. J. Intell. Manuf. 31(1), 127–182 (2020)
Dehkordi, J., Tralli, V.: Interference analysis for optical wireless communications in network-on-chip (NoC) Scenarios. IEEE Trans. Commun. 68(3), 1662–1674 (2020)
Randrianantenaina, I., Elsawy, H., Dahrouj, H., Kaneko, M., Alouini, M.: Uplink power control and ergodic rate characterization in FD cellular networks: A stochastic geometry approach. IEEE Trans. Wirel. Commun. 18(4), 2093–2110 (2019)
Safari, M., Haas, H., Kazemi, H.: A wireless optical backhaul solution for optical attocell networks. IEEE Trans. Wirel. Commun. 18(2), 807–823 (2019)
Mane, N., Verma, A., Arya, A.: A pragmatic optimal approach for detection of cyber attacks using genetic programming. In: 20th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2020 – Proceedings (2020)
Zschech, P., Horn, R., Höschele, D., Janiesch, C., Heinrich, K.: Intelligent user assistance for automated data mining method selection. Bus. Inf. Syst. Eng. 62(3), 227–247 (2020)
Svec, P., Benko, L., Kadlecik, M., Kratochvil, J., Munk, M.: Web usage mining: data pre-processing impact on found knowledge in predictive modelling. Proc. Comput. Sci. 171, 168–178 (2020)
Guan, X., Fan, Y., Qin, Q., Deng, K., Yang, G.: Construction of science and technology achievement transfer and transformation platform based on deep learning and data mining technology. J. Intell. Fuzzy Syst. 39(2), 1843–1854 (2020)
Wang, A., Gao, X.: Multi-tasks discovery method based on the concept network for data mining. IEEE Access 7, 139537–139547 (2019)
Maus, V., et al.: A global-scale data set of mining areas. Sci. Data 7(1), 1–13 (2020)
Holik, M., Horvath, T., Oujezsky, V.: Application for GPON frame analysis. Electron. (Switz.) 8(6), 700 (2019)
Hassan, W., Idrus, S., King, H., Ahmed, S., Faulkner, M.: Idle sense with transmission priority in fibre-wireless networks. IET Commun. 14(9), 1428–1437 (2020)
Thangappan, T., Therese, B., Suvarnamma, A., Swapna, G.: Review on dynamic bandwidth allocation of GPON and EPON. J. Electron. Sci. Technol. 18(4), 100044 (2020)
Huang, D., Zhang, Y., Lin, H., Zou, L., Liu, Z.: Rule inference network model for classification. Ruan Jian Xue Bao/J. Softw. 31(4) (2020)
Ehrhardt, A., et al.: Maintenance and deployment of fibre infrastructure in the access domain for FTTH networks. Photonische Netze - 13. ITG-Fachtagung (2020)
Castellano, J., Moral-García, S., Mantas, C., Abellán, J.: On the use of m-probability-estimation and imprecise probabilities in the naïve bayes classifier. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 28(4), 661-682 (2020)
Rezaeian, N., Novikova, G.: Persian text classification using naive bayes algorithms and support vector machine algorithm. Indon. J. Electr. Eng. Inform. 8(1), 178–188 (2020)
Tuomo, A., Suutala, J., Röning, J., Koskimäki, H.: Better classifier calibration for small datasets. ACM Trans. Knowl. Discov. Data 14(3), 1–19 (2020)
Mahanama, B., Mendis, W., Jayasooriya, A., Malaka, V., Thayasivam, U., Umashanger, T.: Educational data mining: a review on data collection process. In: 18th International Conference on Advances in ICT for Emerging Regions, ICTer 2018 – Proceedings (2019)
El Mohadab, M., Bouikhalene, B., Safi, S.: Automatic CV processing for scientific research using data mining algorithm. J. King Saud Univ. – Comput. Inf. Sci. 32(5), 561–567 (2020)
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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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