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All Journal Journal of Information Systems and Technology Research Journal of Engineering, Technology and Computing (JETCom)
Ria Pasaribu, Tio
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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

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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.
Implementation of Data Mining Teacher Performance Assessment Using the K-means Clustering Method in Student Learning Styles in the 4.0 Era Zul'Aini, Nurul Hasanah; Lubis, Imran; Ria Pasaribu, Tio
Journal of Engineering, Technology and Computing (JETCom) Vol. 3 No. 1 (2024): JETCom (March 2024)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/jetcom.v3i1.137

Abstract

 In today's digital era, information technology has changed various aspects of life, including in the world of education. Era 4.0 provides new challenges for education, including in assessing teacher performance. Teacher performance evaluation is an important aspect in improving the quality of education. However, in measuring teacher performance, there are many factors to consider, including student learning styles. This research was conducted at the Paba Binjai school by conducting direct interviews with 700 students who would fill out questionnaire data. This method is a popular method used in cluster analysis, which aims to group data into several homogeneous groups based on the similarity of the attributes possessed. there are 4 data and group 3 there are 6 data. The explanation of the 3 groups is as follows: 1. Cluster 1 There are 12 data 6.4 1.9 1.6 Based on the above calculations it can be seen that in cluster 1 the teacher performance assessment data in the learning styles of SMK Paba Binjai students in the Subject group ( X) is Computer and Network Engineering, for the Student Evaluation Learning group (Y) is Achieved, and in the Learning Style group (Z) is Auditory. 2. Cluster 2 There are 4 data 7.75 2.5 1.5 Based on the above calculations it can be seen that in cluster 2 the data on teacher performance assessment in the learning styles of Paba Binjai Vocational High School students in the Subject group (X) is Software Engineering, for the group Student evaluation (Y) is quite achieved, and in the learning style group (Z) is auditory. 3. Cluster 3 There are 6 data 6.5 1.5 1.6 Based on the above calculations it can be seen that in cluster 3 the teacher performance assessment data in the learning styles of Paba Binjai Vocational High School students in the Subject group (X) are Computer and Network Engineering, for the student evaluation group (Y) is achieved, and in the learning style group (Z) enough is auditory. This assessment is used as a consideration for school principals in deciding teachers who lack integrity and produce a group of teachers who have very good, good, moderate, and poor teaching quality.
Co-Authors Imran Lubis Rizki Ananda Serasi Ginting, Budi Zul'Aini, Nurul Hasanah