Implementation of K-Medoids Clustering Method for Indihome Service Package Market Segmentation

Juniar Hutagalung; Muhammad  Syahril; Sobirin Sobirin

doi:10.47709/cnahpc.v4i2.1458

Authors

Juniar Hutagalung STMIK Triguna Dharma, Medan, Indonesia
Muhammad Syahril STMIK Triguna Dharma, Medan, Indonesia
Sobirin Sobirin STMIK Triguna Dharma, Medan, Indonesia

DOI:

10.47709/cnahpc.v4i2.1458

Keywords:

Clustering; IndiHome; K-Medoids; Market Segmentation; Service Package

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Abstract

IndiHome (Indonesia Digital Home) is a leading digital fibre optic service product consisting of fibre optic internet services, landline telephones, and interactive TV services. Although the coverage of Indihome products is extensive in the city of Medan, in marketing, Indihome products have not reached the planned target. Based on data from Indihome service package users that have been received, Indihome product users only numbered 6419 customers in all STOs in Medan City. At the same time, the target was planned by PT. Telkom Access Medan, namely Marketing Indihome products, must reach 5,000 customers per month in all STOs in Medan City. Indihome product marketing is an obstacle for PT. Telkom Access Medan, because the Indihome product is a new product, the people of Medan City do not fully know what Indihome is and what facilities they get from using the Indihome service package. Therefore PT. Telkom Access Medan needs to make a plan to make a marketing strategy. The first step that needs to be done is to segment the market for the Indihome service package. This study aimed to determine the application of Data Mining using the K-Medoids Clustering method in the Indihome service package market segmentation at PT. Telkom Access Medan. With this research, it is hoped that it can provide a reference for the results of the decision so that it can help related parties to make it easier to classify the market segmentation of the Indihome service package at PT. Telkom Access Medan. Because the value of S > 0, then the calculation is stopped and ends in the 3rd iteration. Indihome service package data processing uses the k-medoids clustering method in the form of potential, potential, and not potential STO (Sentral Telephone Automated) cluster members.

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References

Aditya, A., Sari, B. N., Padilah, T. N., Sari, B. N., & Padilah, T. N. (2021). Perbandingan pengukuran jarak Euclidean dan Gower pada klaster k-medoids Comparison analysis of Euclidean and Gower distance measures on k-medoids. Jurnal Teknologi Dan Sistem Komputer, 9(October 2020), 1–7. https://doi.org/10.14710/jtsiskom.2021.13747.

Alfiah, F., Farizi, D. Al, & Widodo, E. (2022). Analisis Clustering K-Medoids Berdasarkan Indikator Kemiskinan di Jawa Timur Tahun 2020 K-Medoids Clustering Analysis Based on Poverty Indicators in East Java in 2020. Jurnal Ilmiah Sains, 22(April), 1–7. https://doi.org/10.35799/jis.v22i1.35911.

Andini, A. D., & Arifin, T. (2020). Implementasi Algoritma K-Medoids Untuk Klasterisasi Data Penyakit Pasien Di Rsud Kota Bandung. Jurnal Responsif?: Riset Sains Dan Informatika, 2(2), 128–138. https://doi.org/10.51977/jti.v2i2.247.

Bimantoro, T., & Wardhani, A. K. (2020). Implementasi Algoritma Partitioning Around Medoids Dalam Pengelompokan Restoran. Indonesian Journal of Technology, Informatics and Science (IJTIS), 2(1), 33–36. https://doi.org/10.24176/ijtis.v2i1.5651.

Daffa Rafif Agustian, B. A. D. (2022). Analisis Clustering Demam Berdarah Dengue Dengan Algoritma K-Medoids (Studi Kasus Kabupaten Karawang). JIKO (Jurnal Informatika Dan Komputer), 6(1), 18–26. http://dx.doi.org/10.26798/jiko.2022.v6i1.504.

Egi, S. Syam, Y. Syahra, A. A. (2021). Data Mining in Grouping Indihome Customer Data Using the K–Means Clustering Method at PT.Telkom Akses. Jurnal Mantik, 4(4), 2604–2612. https://doi.org/10.35335/mantik.Vol4.2021.1220.pp2604-2612.

Fira, A., Rozikin, C., & Garno. (2021). Komparasi Algoritma K-Means dan K-Medoids Untuk Pengelompokkan Penyebaran Covid-19 di Indonesia. Journal of Applied Informatics and Computing (JAIC), 5(2), 133–138. https://doi.org/10.30871/jaic.v5i2.3286.

Fitralisma, G., & Mandasari. (2020). Analisis strategi segmentasi pasar guna menghadapi pesaing dan meningkatkan penjualan di masa pandemi. Manajemen Dan Akuntansi, 16(1), 287–293. https://doi.org/10.32534/jv.v16i1.1886.

Hutagalung, J., Ginantra, N. L. W. S. R., Bhawika, G. W., Parwita, W. G. S., Wanto, A., & Panjaitan, P. D. (2021). COVID-19 Cases and Deaths in Southeast Asia Clustering using K-Means Algorithm. Journal of Physics: Conference Series, 1783(1). https://doi.org/10.1088/1742-6596/1783/1/012027.

Hutagalung, J., & Sonata, F. (2021). Penerapan Metode K-Means Untuk Menganalisis Minat Nasabah Asuransi. Jurnal Media Informatika Budidarma, 5(3), 1187–1194. https://doi.org/10.30865/mib.v5i3.3113.

Irfan, M., Ammar, T., & Jesslyn, J. (2021). K-Medoids Clustering dengan Jarak Dynamic Time Warping dalam Mengelompokkan Provinsi di Indonesia Berdasarkan Kasus Aktif Covid-19.PRISMA 4, 685–692.

Kurniawan, W., Rifai, A., Gata, W., & Gunawan, D. (2020). Analisis Algoritma K-Medoids Clustering Dalam Menentukan Pemesanan Hotel. 8(2), 182–187.

Marlina, D., Lina, N., Fernando, A., & Ramadhan, A. (2018). Implementasi Algoritma K-Medoids dan K-Means untuk Pengelompokkan Wilayah Sebaran Cacat pada Anak. Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer Dan Teknologi Informasi, 4(2), 64. https://doi.org/10.24014/coreit.v4i2.4498.

Nasyuha, A. H., Jama, J., Abdullah, R., Syahra, Y., Azhar, Z., Hutagalung, J., & Hasugian, B. S. (2021). Frequent pattern growth algorithm for maximizing display items. Telkomnika (Telecommunication Computing Electronics and Control), 19(2), 390–396. https://doi.org/10.12928/TELKOMNIKA.v19i2.16192.

Orisa, M., & Faisol, A. (2021). Analisis Algoritma Partitioning Around Medoid untuk Penentuan Klasterisasi. Jurnal Teknologi Informasi Dan Terapan (J-TIT), 8(2), 86–90. https://doi.org/10.25047/jtit.v8i2.258.

Prakasawati, P. E., Chrisnanto, Y. H., & Hadiana, A. I. (2019). Segmentasi Pelanggan Berdasarkan Produk Menggunakan Metode K- Medoids. KOMIK (Konferensi Nasional Teknologi Informasi Dan Komputer), 3(1), 335–339. https://doi.org/10.30865/komik.v3i1.1610.

Salna Sasi Ediyana, Jaenudin, Doni Wihartika, R. A. G. A. (2021). Analisis Peramalan Penjualan Indihome Dalam Penentuan Safety Stock Ont Di Pt. Telkom Indonesia Wilayah Sukabumi. Fakultas Ekonomi Dan Bisnis Universitas Pakuan, 1–13. https://jom.unpak.ac.id/index.php/ilmumanajemen/article/view/1830.

Samudi, Slamet Widodo, H. B. (2022). Algoritma K-Medoids Untuk Menentukan Clustering Data Covid 19 Di Dki Jakarta. JURSIMA (Jurnal Sistem Informasi Dan Manajemen), 10(1), 122–127. https://ejournal.stmikgici.ac.id/

Sindi, S., Ningse, W. R. O., Sihombing, I. A., R.H.Zer, F. I., & Hartama, D. (2020). Analisis Algoritma K-Medoids Clustering Dalam Pengelompokan Penyebaran Covid-19 Di Indonesia. Jurnal Teknologi Informasi, 4(1), 166–173. https://doi.org/10.36294/jurti.v4i1.1296.

Utomo, W. (2021). The comparison of k-means and k-medoids algorithms for clustering the spread of the covid-19 outbreak in Indonesia. ILKOM Jurnal Ilmiah, 13(1), 31–35. https://doi.org/10.33096/ilkom.v13i1.763.31-35.