Ardiansyah, Muhammad Dafa (2023) PERAMALAN HARGA SAHAM INDEX NASDAQ COMPOSITE DENGAN METODE CONVOLUTIONAL NEURAL NETWORK – LONG SHORT TERM MEMORY. Undergraduate thesis, UPN Veteran Jawa Timur.
|
Text (Cover)
Cover.pdf Download (1MB) | Preview |
|
|
Text (BAB 1)
BAB I.pdf Download (86kB) | Preview |
|
![[img]](http://repository.upnjatim.ac.id/style/images/fileicons/text.png) |
Text (BAB 2)
BAB II.pdf Restricted to Registered users only until 22 November 2025. Download (857kB) |
|
|
Text (BAB 3)
BAB III.pdf Restricted to Registered users only until 22 November 2025. Download (373kB) |
|
|
Text (BAB 4)
BAB IV.pdf Restricted to Registered users only until 22 November 2025. Download (2MB) |
|
|
Text (BAB 5)
BAB V.pdf Download (10kB) | Preview |
|
|
Text (Daftar Pustaka)
Daftar Pustaka.pdf Download (146kB) | Preview |
|
|
Text (Lampiran)
Lampiran.pdf Restricted to Registered users only until 22 November 2025. Download (4MB) |
Abstract
In the era following the COVID-19 pandemic, many individuals are investing in shares as a means for financial recovery. However, many of them do not understand how the stock market works and how to predict stock prices. This final project aims to develop a stock price forecasting model for the Nasdaq Composite index using a combined deep learning method, namely Convolutional Neural Network – Long Short Term Memory (CNN-LSTM). The trials in this paper went through various paths such as retrieving data taken from Yahoo Finance and then cleaning it. In making a model there are many variations of configurations, all these things are done to produce an optimal model, with an optimal model the results provided will be more accurate, the model will be able to handle the dataset according to the case. The results of this trial show that the CNN-LSTM method provides predictions with a minimum error rate using evaluation measurements of RMSE 0.0188 and MAPE 1.53% and takes an execution time of 47.08 seconds. The CNN-LSTM algorithm has been proven to be used as an option when considering making trading decisions.
| Item Type: | Thesis (Undergraduate) | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Contributors: |
|
||||||||||||
| Subjects: | Q Science > QA Mathematics > QA76.6 Computer Programming Q Science > QA Mathematics > QA76.87 Neural computers T Technology > T Technology (General) |
||||||||||||
| Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
| Depositing User: | Muhammad Dafa Ardiansyah | ||||||||||||
| Date Deposited: | 23 Nov 2023 04:19 | ||||||||||||
| Last Modified: | 23 Nov 2023 04:19 | ||||||||||||
| URI: | http://repository.upnjatim.ac.id/id/eprint/18782 |
Actions (login required)
 |
View Item |