Klasterisasi Angka Usia Muda Melek TIK Berdasarkan Algoritma K-Means Menurut jumlah Provinsi Indonesia

Olivia Immanuela Massie(1*), Tesa Nur Padilah(2),

(1) Universitas Singaperbangsaa Karawang
(2) Universitas Singaperbangsaa Karawang
(*) Corresponding Author

Abstract


Advances in technology that can’t be separated from human life, making information easier to obtain. The results of a survey conducted by APJII on the penetration of internet users in 2018 based on age, stated that the use of the internet was dominated by young people. For this reason, it is hoped that in the future there will be improvements in numbers in the form of equitable use of ICT in all provinces in Indonesia. This research was conducted based on the clustering of young people who are ICT literate. The amount of data used is in accordance with the current number of Indonesian provinces, which are thirty-four provinces using the K-Means algorithm. The dataset in this study was obtained from the official government website https://.www.bps.go.id/ from 2017 to 2019. Clustering was carried out only to group provinces into two types of groups, which can later be used as evaluation material for the government in the framework of the equitable distribution of ICT in each province. The final result of this study is that there are twenty-five provinces that are included in cluster 1 and nine provinces are included in cluster group 2. Thus it is necessary to increase the ICT literacy rate for provincial clusters whose values are still lagging behind other provinces.

Full Text:

PDF

References


Aditya, A., Jovian, I., & Sari, B. N., “Implementasi K-Means Clustering Ujian Nasional Sekolah Menengah Pertama diI Indonesia Tahun 2018/2019”. Jurnal Media Informatika Budidarma, pp 51-58, 2020.

APJII., “Laporan Survei : Penetrasi & Profil Perilaku Pengguna Internet Indonesia 2018”. Jakarta: Polling Indonesia, 2019.

Isyarah, F., Hasan, M., & Wiza, F., “Clustering Daerah Miskin di Provinsi Riau Menggunakan Metode K-Means”. Prosiding-Seminar Nasional Teknologi Informasi & Ilmu Komputer (SEMASTER), pp 1-12, 2020.

Oktavia, R., Hardinata, J. T., & Irawan., “Penerapan Metode Algoritma K-means dalam Pengelompokan Angka Harapan Hidup Saat Lahir Menurut Provinsi”. KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen), pp 164-161, 2020.

Priyatman, H., Sajid, F., & Haldivany, D., “Klasterisasi Menggunakan Algoritma K-Means Clustering untuk Memprediksi Waktu Kelulusan Mahasiswa”. JEPIN (Jurnal Edukasi dan Penelitian Informatika), Vol. 5 No. 1, pp 62-66, 2019.

Saifullah, & Hidayati, N., “Pengelompokan Persentase Buta Huruf Umur 15-44 Menurut Provinsi Menggunakan Algoritma K-Means”. Kumpulan JurnaL Ilmu Komputer (KLIK), pp 230-240, 2020.

Saragih, A. T., Sembiring, A. S., & Sayuthi, M., “Penerapan Metode Clustering K-Means uuntuk Proses Seleksi Calon Peserta Lomba MTQ”. Jurnal Pelita Informatika, Volume 17, Nomor 2, pp 117-122, 2018.

Sujanah, A., Sukroni, A., Satika, A., & Krismawati., “Statistik Ketenagakerjaan Usia Muda di Indonesia”. Jakarta: Sub Direktorat Statistik Ketenagakerjaan, 2016.

Triyansyah, D., & Fitrianah, D., “Analisis Data Mining Menggunakan Algoritma K-Means Clustering Untuk Menentukan Strategii Marketing (Studi Kasus: Hoyweapstore)”. IncomTech, Jurnal Telekomunikasi dan Komputer, Vol.8, No.3, pp 164-182, 2018.

Vhallah, I., Sumijan, & Santony, J., “Pengelompokan Mahasiswa Potensial Drop Out menggunakan Metodee Clustering K-Means”. JURNAL RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol. 2 No. 2, pp 572-577, 2018.

Wahyudi, H. S., & Sukmasari, M. P., “Teknologii dan Kehidupan Masyarakat”. Jurnal Analisa Sosiologi, pp 13-24, 2014.

Yunita, Efendi, R., & Rini, D. P., “Metode K-Means & SAW dalam Seleksi Penerima Dana Zakat pada Badan Amili Zakat”. TEKNOMATIKA, Vol.09, No.02, pp 163-174, 2019.




DOI: http://dx.doi.org/10.30645/j-sakti.v5i2.374

Refbacks

  • There are currently no refbacks.


J-SAKTI (Jurnal Sains Komputer & Informatika)
Published Papers Indexed/Abstracted By:

Jumlah Kunjungan :

View My Stats