Mokhammad Raviandra, . (2024) PENERAPAN METODE LONG SHORT TERM MEMORY (LSTM) PADA PREDIKSI HARGA SAHAM BUMI RESOURCES MINERALS TBK. DAN ASTRINDO NUSANTARA INFRASTRUKTUR TBK. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
This research aims to predict the share prices of two coal mining sector companies, namely PT Bumi Resources Minerals Tbk. (BRMS) and PT Astrindo Nusantara Infrastruktur Tbk. (BIPI). This research uses the Long Short-Term Memory (LSTM) method, a type of Recurrent Neural Network (RNN) that is effective in processing and predicting time series data. The stock data used in this research was obtained from the Yahoo Finance page, including daily data such as opening price, highest price, lowest price, closing price and trading volume. The LSTM model is implemented with a variety of epochs to optimize prediction results. The research results show that the LSTM model can predict stock prices with fairly accurate accuracy with an RMSE value of 0.72 and MAE 0.01635 for BRMS shares and an RMSE value of 0.24 and MAE 0.01604 for BIPI shares. The result of the error rate for BRMS shares is 4.92 and for BIPI shares it is 4.38.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2010511137] [Pembimbing 1 : Ika Nurlaili Isnainiyah ] [Pembimbing 2 : Indra Permana Solihin ] [Penguji 1: Bayu Hananto ] [Penguji 2: Muhammad Panji Muslim ] |
| Uncontrolled Keywords: | Stock, Long Short Term Memory (LSTM), Prediction, Time Series. |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | MOKHAMMAD RAVIANDRA |
| Date Deposited: | 30 Jul 2024 07:49 |
| Last Modified: | 05 Sep 2024 06:51 |
| URI: | http://repository.upnvj.ac.id/id/eprint/31553 |
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