MEMPREDIKSI DATA SAHAM BANK MANDIRI MENGGUNAKAN METODE ALGORITMA REGRESI LINEAR DENGAN BANTUAN RAPID MINER

LAILA SARI, NPM 2009100029 (2024) MEMPREDIKSI DATA SAHAM BANK MANDIRI MENGGUNAKAN METODE ALGORITMA REGRESI LINEAR DENGAN BANTUAN RAPID MINER. Tugas_Akhir (Artikel) INFORMATIKA Fakultas Sains dan Teknologi Universitas Labuhanbatu, 12 (2). pp. 124-131. ISSN 2615-1855 (e-ISSN)/ 2303-2863 (p-ISSN)

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Abstract

Indonesia has been growing rapidly, one of which can be seen from the economy and technology in Indonesia, at this time the community is almost entirely using machine power technology as a helper of daily life, and the community has also processed a lot of its finances by way of stock investment, with stock investment, the community believes that stocks are invested safer and more profitable. A stock can be defined as a mark of participation or ownership of an individual investor or institutional investor or trader on their investment or a certain amount of funds invested in a company. Linear regression algorithm is one of the methods used to predict stock data in Bank Mandiri. Linear regression algorithm tries to model the relationship between two variables by matching the linear equation of the stock data to be studied. One variable is considered the explanatory variable and the other variable is called the dependent variable. Prediction a process for systematically estimating Bank Mandiri stock data that will appear in the future using data obtained from the past. Thus the company can easily find out the stock data in the future. Keywords : Stocks, Linear Regression Algorithm, Bank Mandiri, Prediction.

Item Type: Article
Uncontrolled Keywords: Stocks, Linear Regression Algorithm, Bank Mandiri, Prediction.
Subjects: Z Bibliography. Library Science. Information Resources > ZA Information resources
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions: Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User: Unnamed user with email repository@ulb.ac.id
Date Deposited: 05 Sep 2024 03:43
Last Modified: 05 Sep 2024 03:43
URI: http://repository.ulb.ac.id/id/eprint/1020

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