Clustering the potential bandwidth upgrade of FTTH broadband subscribers


Sasa Ani Arnomo(1*); Yulia Yulia(2);

(1) Universitas Putera Batam
(2) STIE Nagoya Indonesia
(*) Corresponding Author

  

Abstract


A company needs to consider determining the customers potential before deciding to upgrade their bandwidth. It is important because, previously, determination was conducted randomly. Therefore, potential determination is necessary by grouping customers who have similar characteristics based on their data and attributes. This study employs data mining techniques using clustering method with K-means algorithm on broadband users group of 263 FTTH. The determination was determined based on end centroid point in the grouping. The results were divided into 5 clusters consisting of 34 highly potential users (12.92%), 29 potential users (11.02%), 56 fairly potential users (21.3%), 54 less potential users (20.53%), and the remaining 90 not potential users (34.22%). The comparison of the validity of the Davies-Bouldin Index for the 5 (five) clusters is 0.538 for K-Means and 0.819 for K-Medois. This indicates that K-Means results better score. This method is useful for efficient bandwidth sharing.

Keywords


Potential Upgrades; Bandwidth; Broadband; FTTH; Cluster

  
  

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doi  https://doi.org/10.33096/ilkom.v13i1.805.51-57 		  

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T. Hao, A. Sanchez-Postigo, P. Cheben, A. Ortega-Monux, and W. N. Ye, Dual-Band Polarization-Independent Subwavelength Grating Coupler for Wavelength Demultiplexing, IEEE Photonics Technol. Lett., vol. 32, no. 18, pp. 11631166, 2020.

D. Kuswoyo and N. Agani, Model Perhitungan Kebutuhan Bandwidth Jaringan Komputer menggunakan Sistem Pakar Fuzzy dengan Metode Adaptive Neuro Fuzzy Inference System(ANFIS) : Studi Kasus PT.GMF Aero Asia Cengkareng, J. TICOM, vol. 3, no. 3, pp. 115, 2015.

M. Yang, Y. Lian, J. Wang, and Y. Zhang, Dual-Mode Large-Mode-Area Multicore Fiber With Air-Hole Structure, IEEE Photonics J., vol. 11, no. 4, pp. 110, 2019.

N. D. Prasongko and R. Gernowo, Metode Quality Function Deployment Dan Fuzzy Topsis Untuk Sistem Pendukung Keputusan Pemilihan Perusahaan Penyedia Jasa Internet, J. Sist. Inf. BISNIS, vol. 5, no. 2, pp. 137144, 2015.

S. A. Arnomo and H. Hendra, Perbandingan Fitur Smartphone, Pemanfaatan Dan Tingkat Usability Pada Android Dan iOS Platforms, InfoTekJar (Jurnal Nas. Inform. dan Teknol. Jaringan), vol. 3, no. 2, pp. 184192, 2019.

A. Haque, Z. Wang, S. Chandra, B. Dong, L. Khan, and K. W. Hamlen, FUSION - An online method for multistream classification, Int. Conf. Inf. Knowl. Manag. Proc., vol. Part F131841, no. 2, pp. 919928, 2017.

A. Fitriyani, T. N. Damayanti, and M. S. Yudha, Perancangan Jaringan Fiber To The Home (FTTH) Perumahan Nataendah Kopo, e-Procceding Appl. Sci. Vol.1, No.2 Agustus 2915, vol. 4, no. 3, pp. 35653572, 2017.

F. Fitriastuti and D. P. Utomo, Implementasi Bandwith Management Dan Firewall System Menggunakan Mikrotik OS 2.9.27, J. Tek., vol. 4, no. 1, pp. 19, 2014.

F. N. Khasanah, Performa Kecepatan Akses Internet Dengan Squid Proxy Server Pada Ubuntu Server 10.10 Fata, Informatics Educ. Prof., vol. 2, no. 1, pp. 1118, 2017.

A. Naleng, H. Manossoh, and S. S. Tangkuman, Analisis Potensi Dan Efektivitas Pemungutan Retribusi Pasar Di Kabupaten Kepulauan Siau Tagulandang Biaro, J. EMBA J. Ris. Ekon. Manajemen, Bisnis dan Akunt., vol. 5, no. 2, pp. 22402249, 2017.

S. A. Arnomo, Pengaruh Sistem Informasi Pemasaran dan Loyalitas Konsumen Terhadap Kinerja Pemasaran, J. Ekon. Bisnis, vol. 1, no. 1, pp. 116, 2012.

R. Hadi, Soiful., Wibowo, Implementasi Manajemen Bandwidth menggunakan Metode Queue Tree (Studi Kasus pada Universitas Pancasila), J. Teknol. Inform. dan Komput., vol. 5 No. 1, no. 1, pp. 1923, 2019.

E. A. Darmadi, Manajemen Bandwidth Internet Menggunakan Mikrotik Router Di Politeknik Tri Mitra Karya Mandiri, IKRA-ITH Teknol. J. Sains Teknol., vol. 3, no. 3, pp. 713, 2019.

M. Fajri, R. Munadi, and T. Y. Arif, Manajemen Bandwidth Pada Jaringan Lokal Menggunakan Sistem Operasi Vyos, J. Karya Ilm. Tek. Elektro, vol. 5, no. 1, pp. 15, 2020.

P. Ferdiansyah, R. Indrayani, and S. Subektiningsih, Analisis Manajemen Bandwidth Menggunakan Hierarchical Token Bucket Pada Router dengan Standar Deviasi, J. Nas. Teknol. dan Sist. Inf., vol. 6, no. 1, pp. 3845, 2020.

D. P. Hidayatullah, R. I. Rokhmawati, and A. R. Perdanakusuma, Analisis Pemetaan Pelanggan Potensial Menggunakan Algoritma K-Means dan LRFM Model Untuk Mendukung Strategi Pengelolaan Pelanggan ( Studi Pada Maninjau Center Kota Malang ), J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 8, pp. 24062415, 2018.

A. Darmawan, N. Kustian, and W. Rahayu, Implementasi Data Mining Menggunakan Model SVM untuk Prediksi Kepuasan Pengunjung Taman Tabebuya, STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 2, no. 3, p. 299, 2018.

S. Beniwal and J. Arora, Classification and Feature Selection Techniques in Data Mining, Int. J. Eng. Res. Technol., vol. 1, no. 6, pp. 16, 2012.

L. Xu, C. Jiang, J. Wang, J. Yuan, and Y. Ren, Information security in big data: Privacy and data mining, IEEE Access, vol. 2, pp. 11491176, 2014.

R. Sowmya and K. R. Suneetha, Data Mining with Big Data, in Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017, 2017, vol. 26, no. 1, pp. 246250.

S. Li, D. C. Yen, W. Lu, and C. Wang, Identifying the signs of fraudulent accounts using data mining techniques, Comput. Human Behav., vol. 28, no. 3, pp. 10021013, 2012.

Z. Ge, Z. Song, S. X. Ding, and B. Huang, Data Mining and Analytics in the Process Industry: The Role of Machine Learning, IEEE Access, vol. 5, pp. 2059020616, 2017.

Y. Siyamto, Pemanfaatan Data Mining Dengan Metode Clustering Untuk Evaluasi Biaya Dokumen Ekspor Di Pt Winstar Batam, Media Inform. Budidarma, vol. 1, no. 2, pp. 2831, 2017.

P. Arora, Deepali, and S. Varshney, Analysis of K-Means and K-Medoids Algorithm for Big Data, in Physics Procedia, 2016, vol. 78, no. December 2015, pp. 507512.

S. Gopalani and R. Arora, Comparing Apache Spark and Map Reduce with Performance Analysis using K-Means, Int. J. Comput. Appl., vol. 113, no. 1, pp. 811, 2015.

K. Handoko and L. S. Lesmana, Data Mining Pada Jumlah Penumpang Menggunakan Metode Clustering, Snistek, no. 1, pp. 97102, 2018.

M. A. Rahman and M. Z. Islam, A hybrid clustering technique combining a novel genetic algorithm with K-Means, Knowledge-Based Syst., vol. 71, no. August, pp. 345365, 2014.

P. S. Hasugian, Penerapan Data Mining untuk Klasifikasi Produk Menggunakan Algoritma K-Means (Studi Kasus : Toko Usaha Maju Barabai), J. Mantik Penusa, vol. 2, no. 2, pp. 191198, 2018.


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