COMPARISON OF WEIGHTED MOVING AVERAGE AND PROPHET METHOD IN PREDICTING STOCK PRICES

Alfoin, Gilbert (2022) COMPARISON OF WEIGHTED MOVING AVERAGE AND PROPHET METHOD IN PREDICTING STOCK PRICES. Other thesis, Universitas Katholik Soegijapranata Semarang.

[img] Text
16.K1.0021-Gilbert Alfoin_COVER_a.pdf
Download (1MB)
[img] Text
16.K1.0021-Gilbert Alfoin_BAB I_a.pdf
Download (1MB)
[img] Text
16.K1.0021-Gilbert Alfoin_BAB II_a.pdf
Restricted to Registered users only
Download (1MB)
[img] Text
16.K1.0021-Gilbert Alfoin_BAB III_a.pdf
Download (1MB)
[img] Text
16.K1.0021-Gilbert Alfoin_BAB IV_a.pdf
Download (1MB)
[img] Text
16.K1.0021-Gilbert Alfoin_BAB V_a.pdf
Download (1MB)
[img] Text
16.K1.0021-Gilbert Alfoin_BAB VI_a.pdf
Download (1MB)
[img] Text
16.K1.0021-Gilbert Alfoin_DAPUS_a.pdf
Download (1MB)
[img] Text
16.K1.0021-Gilbert Alfoin_LAMP_a.pdf
Download (1MB)

Abstract

Stocks are one of the favorite investment methods of Indonesian people. This is because stocks are "high risk high return" investments. That is an investment that provides high returns even though it has a high risk as well. To find a good stock, we can do technical analysis. But doing technical analysis is not easy because it takes time and enough experience to be able to do the right technical analysis. To overcome difficulties in conducting technical analysis. An appropriate algorithm is needed to predict stock prices. And a program that can work automatically in running the algorithm. So that's why I created a program that can run Weighted Moving Average and Prophet automatically. Later these two algorithms will be compared for their accuracy in predicting stock prices. The final result of this study is the performance of the Weighted Moving Average and Prophet in predicting stock prices. With this research, readers can understand how the Weighted Moving Average and Prophet work. And it is easier to predict stock prices because it can be done automatically.

Item Type: Thesis (Other)
Subjects: 000 Computer Science, Information and General Works > 004 Data processing & computer science
Divisions: Faculty of Computer Science > Department of Informatics Engineering
Depositing User: mr AM. Pudja Adjie Sudoso
Date Deposited: 22 Mar 2022 04:19
Last Modified: 22 Mar 2022 04:19
URI: http://repository.unika.ac.id/id/eprint/28238

Actions (login required)

View Item View Item
Unika Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.