Widiarto, Kevin Avicenna and , Endang Wahyu Pamungkas, S.Kom, M.Kom. (2024) Implementasi Algoritma LSTM untuk Prediksi Harga Saham pada Situs Yahoo Finance dengan Menerapkan Microservice. Skripsi thesis, Universitas Muhammadiyah Surakarta.
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Abstract
The stock market is one of the most popular stock monitoring investment instruments in the world. However, the stock market is a big risk that can lead to fluctuations in stock prices that can change drastically and briefly that cannot be predicted accurately and with certainty. Our research uses the LSTM algorithm which is designed to overcome the problem of fluctuating stock prices by predicting stock prices using several features as training and testing. LSTM (Long Short Term Memory), also known as Long Term reminder and Short Term reminder, is one of the categories of deep learning which is an advanced complex model from the Recurrent Neural Network (RNN) category that is able to handle sequence data. The purpose of this research is to implement the LSTM algorithm in order to predict future stock prices, then how to optimise the model with a stock price prediction case scenario using the Yahoo Finance site and apply the microservice concept to facilitate implementation in development and production environments. After designing the model, we built the model using Tensorflow, displaying the prediction value and error value. After building the model, we then implement the model in the form of a web-based interactive using the Streamlit library displaying the original value and the predicted value and packaged using a docker container to help other users use the application without having to install several dependencies.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | LSTM, saham, prediksi, Streamlit, Docker |
| Subjects: | T Technology > TZ Technical Information |
| Divisions: | Fakultas Ilmu Komunikasi dan Informatika > Teknik Informatika |
| Depositing User: | KEVIN AVICENNA WIDIARTO |
| Date Deposited: | 20 May 2024 03:47 |
| Last Modified: | 24 May 2024 03:52 |
| URI: | http://eprints.ums.ac.id/id/eprint/124206 |
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