Implementation Of Data Mining On Telkom Customer Data Using The K-Nearest Neigbor Method To Predict Service Status
Implementasi Data Mining Pada Data Pelanggan Telkom Menggunakan Metode K-Nearest Neigbor Untuk Memprediksi Status Pelayanan
DOI:
https://doi.org/10.21070/ups.1534Keywords:
data mining, knn, sidoarjoAbstract
The use of the internet makes it easier for people to get information. The large number of wifi users resulted in an increase in Telkom Indonesia. This impact resulted in frequent delays in the service process. The purpose of this study is to classify service status as normal or abnormal. This study implements the k-nearest neighbor classification method. The data used is Telkom customer data of 15,113 (May to June 2018), dataset of 100 data. The input consists of 60% training data and 40% testing data. Weka processes calculations and displays classification results in the form of normal or abnormal. In Weka, the correctness prediction is 92.5%, the error prediction is 7.5%. In manual calculations, the correctness prediction is 98%, the error prediction is 3%. From the results of calculations using KNN, the highest results were obtained using manual with truth level of 98%, error rate of 3%.
Downloads
References
A. Penyelenggara and J. Internet, “RESPONDEN Survei Nasional PENETRASI Pengguna Internet 2018,” 2018.
D. A. Kesawasidhi, “Pengaruh Kinerja Kualitas Pelayanan Terhadap Kepuasan Dan Loyalitas Pelanggan Pt. Telekomunikasi Indonesia Tbk. Cabang Yogyakarta,” Fak. Ekon. Manaj. Univ. Islam Indones., pp. 1–21, 2017.
A. Wibisono and , S., “Pengaruh Kualitas Jasa Pelayanan Terhadap Kepuasan Pelanggan,” Perform. “ J. Bisnis Akuntansi,” vol. 6, no. 2, p. 32, 2016, doi: 10.24929/feb.v6i2.268.
I. Oktavianis, “Upaya PT. Telkom Dalam Meningkatkan Kualitas Pelayanan Internet Speedy Dalam Memuaskan Pelanggan (Studi Pada PT. Telkom Malang),” J. Adm. Publik Mhs. Univ. Brawijaya, vol. 1, no. 1, p. 72569, 2013.
H. Leidiyana, “Penerapan Algoritma K-Nearest Neighbor Untuk Penentuan Resiko Kredit Kepemilikan Kendaraan Bermotor,” J. Penelit. Ilmu Komputer, Syst. Embed. Log., vol. 1, no. 1, pp. 65–76, 2013.
S. Sumarlin, “Implementasi Algoritma K-Nearest Neighbor Sebagai Pendukung Keputusan Klasifikasi Penerima Beasiswa PPA dan BBM,” J. Sist. Inf. Bisnis, vol. 5, no. 1, pp. 52–62, 2015, doi: 10.21456/vol5iss1pp52-62.
W. Yustanti, “Algoritma K-Nearest Neighbour untuk Memprediksi Harga Jual Tanah,” J. Mat. Stat. dan komputasi, vol. 9, no. 1, pp. 57–68, 2012.
S. A. Hasan, S. Suhada, and L. Hadjaratie, “Klasifikasi Status Gizi Menggunakan K-Nearest Neighbor,” vol. 3, no. 1, pp. 6–11, 2015.
gunadharma, “Definisi dan Simbol Flowchart,” Defin. Dan Simbol Flowchart, 2016.
D. Purnamasari, J. Henharta, Y. P. Sasmita, F. Ihsani, and I. W. S. Wicaksana, “Machine Learning ‘Get Easy Using WEKA,’” Dapur Buku, pp. 1–40, 2013.
Downloads
Additional Files
Posted
License
Copyright (c) 2023 UMSIDA Preprints Server
This work is licensed under a Creative Commons Attribution 4.0 International License.
