Agung Hot Iman, . (2023) UJI PERFORMA ALGORITMA LONG SHORT-TERM MEMORY UNTUK PREDIKSI HARGA SAHAM. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
Predicting stock prices is an interesting but difficult activity in the business world. Stock price predictions need to be based on previous stock prices. Stock data can be used to analyze using an algorithm to obtain information on future predictive values. In this study, using long short-term memory (LSTM) and backpropagation on PT Telekomunikasi Indonesia's stock data from the official IDX IDX website for the period January 2020 to December 2022 with closing data as a feature. The best result from the LSTM model is the Epoch 300 model with an MSE of 236.345 and a computing time of 7.2 seconds. The best results from the Backpropagation model is the epoch 300 model with an MSE of 537,116 and a computation time of 4.6 seconds. The best model produced is the LSTM model with epoch 300. The results of the stock price fluctuation graph from this study show that future stock price movements will experience a decline.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 1910511032] [Pembimbing: Iin ernawati] [Penguji 1: Ermatita] [Penguji 2: Ika Nurlaili Isnainiyah] |
| Uncontrolled Keywords: | Prediction, LSTM, Stock. |
| Subjects: | Q Science > QA Mathematics 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: | Agung Hot Iman |
| Date Deposited: | 02 Aug 2023 09:44 |
| Last Modified: | 02 Aug 2023 09:44 |
| URI: | http://repository.upnvj.ac.id/id/eprint/25157 |
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