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All Journal Journal of Informatics Management and Information Technology Bulletin of Informatics and Data Science
Indri Fatma
STIKOM Tunas Bangsa, Pematangsiantar
Computer Science & IT Electrical & Electronics Engineering Engineering

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Estimasi Laju Pertumbuhan Penduduk Menggunakan Metode Regresi Linier Berganda Pada BPS Simalungun Fica Oktavia Lusiana; Indri Fatma; Agus Perdana Windarto
Journal of Informatics Management and Information Technology Vol. 1 No. 2 (2021): April 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstract

The Central Statistics Agency (CSA) is a non-departmental government agency that reports directly to the president. The use of data collection is for state data collection for the needs of economic strategies, infrastructure, and so on. So that the CSA institution must be able to predict the estimated rate of population growth. Particularly one of the CSA institutions in North Sumatra, CSA Simalungun, has experienced problems in estimating the population growth rate. Multiple linear regression model is the development of a simple linear regression model. If the simple linear regression model consists of only one independent variable and one dependent variable, then in multiple linear regression the number of independent variables is more than one and one dependent variable. The stages carried out in the data mining process begin with data selection from source data to target data, the preprocessing stage to improve data quality, transformation, data mining and interpretation and evaluation stages which produce output in the form of new knowledge which is expected to make a better contribution.
Analisis Metode K-Medoids Cluster Dalam Mengelompokkan Siswa Yang Berprestasi Indri Fatma; Heru Satria Tambunan; Fitri Rizki
Bulletin of Informatics and Data Science Vol 1, No 1 (2022): May 2022
Publisher : PDSI

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Abstract

Cluster analysis of outstanding students using data mining, namely the K-Medoid Cluster Algorithm. Previously, the school still used the manual method in determining students who excel at the school, so it took a long time and the results were not accurate. K-Medoid Cluster is one of the algorithms used for data classification or grouping, the authors apply the K-Medoid Cluster algorithm in grouping students with high achievement in order to get more accurate, fast, and effective results.
Co-Authors Agus Perdana Windarto Fica Oktavia Lusiana Fitri Rizki Heru Satria Tambunan