Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Journal of Information Systems and Technology Research Journal of Engineering, Technology and Computing (JETCom)
Ria Pasaribu, Tio
Unknown Affiliation
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Information Systems and Technology Research

 1 
Data Mining Grouping Of Drug Users By Age Using Clustering Method (Case Study: BNN Binjai City) Ananda, Rizki; Serasi Ginting, Budi; Ria Pasaribu, Tio
Journal of Information Systems and Technology Research Vol. 1 No. 2 (2022): May 2022
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (847.842 KB) | DOI: 10.55537/jistr.v1i2.139

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.
Co-Authors Imran Lubis Rizki Ananda Serasi Ginting, Budi Zul'Aini, Nurul Hasanah