Penerapan Algoritma K-Means Dalam Mengelompokkan Jumlah Penerimaan Sinyal Telepon Seluler Di Sumatera Utara
-
Wati Rizky Pebrianti Sitompul
*
STIKOM Tunas Bangsa, Pematang Siantar,
Indonesia
- Solikhun Solikhun STIKOM Tunas Bangsa, Pematang Siantar, Indonesia
- Widodo Saputra STIKOM Tunas Bangsa, Pematang Siantar, Indonesia
- Selli Oktaviani STIKOM Tunas Bangsa, Pematang Siantar, Indonesia
- agus perdana windarto STIKOM Tunas Bangsa, Pematang Siantar, Indonesia (*) Corresponding Author
Abstract
The purpose of this study was to cluster the number of cell phone signal reception in North Sumatra. The source of the data used is obtained from BPS. The variable used is the number of cell phone reception signals in North Sumatra. This research uses Data Mining Technique with K-means algorithm. It is hoped that the results of this study can provide input to the North Sumatra Province in order to determine the reception of cellular telephone signals, so as to increase the growth and development of telephone signal reception in North Sumatra. And 4G/LTE data obtained that there are 4 high clusters, namely (Mandailing Natal, Simalungun, Deli Serdang, Padang Lawas), 17 medium clusters, namely (Nias, South Tapanuli, Labuan Batu, Humbang Hasundutan, West Pakpak, Samosir, Labuhan Batu, South , Labuhan Batu Utara, North Nias, West Nias, Sibolga, Tanjung Balai, Pematangsiantar, Tebing Tinggi, Binjai, Padang Sidempuan, Gunung Sitoli), and there are 2 low clusters (Central Tapanuli, North Tapanuli, Toba, Asahan, Dairi, Karo , Langkat, South Nias, Serdang Bedagai, Batu Bara, North Padang Lawas, Medan).
References
[2] Budiman, Edy. 2015. “Buku Algoritma Dan Pemrograman.”
[3] Chandra, Muhammad Dwi, Eka Irawan, Ilham Syahputra Saragih, Agus Perdana Windarto, and Dedi Suhendro. 2021. “Penerapan Algoritma K-Means Dalam Mengelompokkan Balita Yang Mengalami Gizi Buruk Menurut Provinsi.” BIOS : Jurnal Teknologi Informasi Dan Rekayasa Komputer 2 (1): 30–38. https://doi.org/10.37148/bios.v2i1.19.
[4] Efori Buulolo,2020. Data Mining Untuk Perguruan Tinggi. Edited by Dwi Novidiantoko. Pertama. yogyakarta: Deeppublish. https://doi.org/All Righat Reserved.
[5] Erlangga, Nanda. 2019. “PENERAPAN DATA MINING DALAM MENGELOMPOKAN PRODUKSI JAGUNG MENURUT PROVINSI MENGGUNAKAN ALGORITMA K-MEANS” 3: 702–9.
[6] https://doi.org/10.30865/komik.v3i1.1681.
[7] Et, Al. M. G. Sadewo. 2018. “Algoritma K-Means Dalam Mengelompokkan Desa
[8] / Kelurahan Menurut Keberadaan Keluarga Pengguna Listrik Dan Sumber Penerangan Jalan Utama Berdasarkan Provinsi.” Seminar Nasional Teknologi Komputer & Sains (SAINTEKS) SAINTEKS 2019, 754–61.
[9] Faesal, Andris, Aziz Muslim, Aditya Hastami Ruger, and Kusrini Kusrini. 2020. “Sentimen Analisis Terhadap Komentar Konsumen Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means.” MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer 19 (2): 207–13. https://doi.org/10.30812/matrik.v19i2.640.
[10] Ginting, M. D. (2019). Penggunaan Telepon Genggam Pada Masyarakat Perbatasan (Survei Pada Kecamatan Tanjung Beringin, Kabupaten Serdang Bedagai, Provinsi Sumatera Utara). Jurnal PIKOM (Penelitian Komunikasi Dan Pembangunan), 20(1), 58. https://doi.org/10.31346/jpikom.v20i1.1906
[11] Hartanti, Dian, Data Mining, Algoritma K-means, and Data Mining. n.d. “MODEL CLUSTERING MENGGUNAKAN ALGORITMA K-MEANS PADA DATA KELUHAN PELANGGAN PT . PLN ( STUDI KASUS : PT . PLN ( PERSERO ) DISTRIBUSI JAKARTA DAN TANGERANG ).”
[12] https://sumut.bps.go.id/statictable/2021/04/23/2463/banyaknya-desa-1-kelurahan- menurutkabupaten-kota-dan-penerimaan-sinyal-internet-telepon-seluler- 2019-2020dan-2021.html
[13] https://www.kominfo.go.id/content/detail/6095/indonesiaraksasa-teknologi- digital-asia/0/sorotan_media
[14] Ian H, Witten & Eibe Frank. 2017. “Data Mining-1.Pdf.”
[15] Jollyta, Denny; Ramadhan, William; Zarlis, Muhammad; 2020. Konsep Data Mining Dan Penerapan. Pertama. Yogyakarta: deepublish. https://doi.org/All Right Reserved.
[16] Pramudita, Disi Amalia, and Bagus Sumargo. 2019. “Pengelompokan Pengguna Internet Dengan Metode K-Means Clustering.” Jurnal Statistika Dan Aplikasinya 3 (1): 1–12. https://doi.org/10.21009/jsa.03101.
[17] Putri, Lailil Muflikhah; Dian Eka Ratnawati; Rekyan Regasari Mardi. 2018. Data Mining.Pdf. Edited by Tim UB Press. Pertama. Malang: UB Press. https://doi.org/All Rights Reserved.
[18] Retno, Dwi, and Sekar Mayangsari. 2019. “PENGELOMPOKKAN JUMLAH DESA / KELURAHAN YANG MEMILIKI DENGAN MENGGUNAKAN METODE K-MEANS CLUSTER” 3: 370–77.
[19] https://doi.org/10.30865/komik.v3i1.1615.
[20] Wardani, Ni Wayan. 2020. Penerapan Data Mining Dalam Analytic CRM. Edited by Janner Simarmata. Denpasar: Yayasan Kita Menulis. kitamenulis.id
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