Data Mining Grouping Of Drug Users By Age Using Clustering Method (Case Study: BNN Binjai City)

Authors

  • Rizki Ananda Stmik Kaputama Binjai
  • Budi Serasi Ginting STMIK Kaputama
  • Tio Ria Pasaribu STMIK Kaputama

DOI:

https://doi.org/10.55537/jistr.v1i2.139

Keywords:

Data Mining, Clustering, Drag User, Age

Abstract

Drug trafficking and abuse is a very complex problem, which requires efforts to overcome it. Given that there are still many obstacles in the process of grouping drug users at the Binjai City BNN Office, for this reason the author tries to create a system to support a computerized grouping process that can help automatically classify drug users based on age, so there is an opportunity to design a grouping data mining system in it. Data mining is part of a computer-based information system that employs one or more computer learning techniques to analyze and extract knowledge automatically that is used to support grouping within an organization or a company. Clustering is a method that is applied to create a grouping data mining system to make it easier for staff to classify drug users based on age. Based on the analysis that has been done on grouping drug user data using the clustering, it is necessary to do the cluster several times to get the same results according to the first process. In this process, the process is carried out 10 times to obtain cluster. In cluster 1 which is 3 9 4,  cluster 2 is 3 1 4, cluster 3 is 3 5 4 with the number of members of cluster 1 as much as 322 data, cluster 2 as much as 81 data and cluster 3 as much as 97 data.

Downloads

Download data is not yet available.

References

D. M. Sari, D. Promosi, I. Perilaku, F. K. Masyarakat, and U. Airlangga, “PERAN KADER ANTI PENYALAHGUNAAN NARKOBA BERBASIS PELAJAR OLEH BADAN NARKOTIKA NASIONAL SURABAYA THE ROLE OF DRUGS ABUSED STUDENT-BASED CADRE BY BNN SURABAYA,” pp. 128–140, 2005.

B. N. Nasional and N. Sintetis, “ARTIKEL NARKOBA : BAHAYA PENYALAHGUNAAN DAN PENCEGAHANNYA Oleh : Joyo Nur Suryanto Gono,” pp. 81–84.

A. A. Hasibuan, “Narkoba dan Penanggulangannya,” vol. 11, no. 1, pp. 33–44, 2017.

L. Terhadap, P. Siswa, and S. Loa, “PENGARUH PENDIDIKAN KESEHATAN TENTANG NARKOBADENGAN MEDIA LEAFLETTERHADAP PENGETAHUAN SISWASMPN1 LOA JANAN,” pp. 132–140, 2014.

Kurniawan, “Definisi & Pengertian Narkoba Dan Golongan/Jenis Narkoba Sebagai Zat Terlarang.,” 2008.

G. Gustientiedina, M. H. Adiya, and Y. Desnelita, “Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan,” J. Nas. Teknol. dan Sist. Inf., vol. 5, no. 1, pp. 17–24, 2019, doi: 10.25077/teknosi.v5i1.2019.17-24.

A. Ikhwan and N. Aslami, “Implementasi Data Mining untuk Manajemen Bantuan Sosial Menggunakan Algoritma K-Means,” JurTI (Jurnal Teknol. Informasi), vol. 4, no. 2, 2020, [Online]. Available: http://jurnal.una.ac.id/index.php/jurti/article/view/2103.

S. Rahayu, D. T. Nugrahadi, and F. Indriani, “CLUSTERING PENENTUAN POTENSI KEJAHATAN DAERAH DI KOTA BANJARBARU DENGAN METODE K-MEANS,” vol. 01, no. 01, pp. 33–45, 2014.

R. Setiawan and N. Tes, “PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING UNTUK MENENTUKAN STRATEGI PROMOSI MAHASISWA BARU ( Studi Kasus : Politeknik LP3I Jakarta ),” vol. 3, no. 1, pp. 76–92, 2016.

A. Almira, S. Suendri, and A. Ikhwan, “Implementasi Data Mining Menggunakan Algoritma Fp-Growth pada Analisis Pola Pencurian Daya Listrik,” J. Inform. …, vol. 6, no. 2, pp. 442–448, 2021, [Online]. Available: http://www.openjournal.unpam.ac.id/index.php/informatika/article/view/12278.

A. Ikhwan, “Analisis Cluster Jarak Antar Kecamatan Dengan Menggunakan Metode Euclidean Di Untuk Penetapan Zona Pengoperasian Mobil Mplik Di Kota Medan,” no. August, 2016.

Additional Files

Published

2022-05-31

How to Cite

Ananda, R., Serasi Ginting, B. ., & Ria Pasaribu, T. (2022). Data Mining Grouping Of Drug Users By Age Using Clustering Method (Case Study: BNN Binjai City). Journal of Information Systems and Technology Research, 1(2), 103–111. https://doi.org/10.55537/jistr.v1i2.139

Issue

Section

Articles

Similar Articles

<< < 1 2 3 4 5 

You may also start an advanced similarity search for this article.