Prediksi Harga Saham Syariah Menggunakan Algoritma Long Short-Term Memory (LSTM)

Budiprasetyo, Gunawan; Hani’ah, Mamluatul; Aflah, Darin Zahira

doi:10.25077/teknosi.v8i3.2022.164-172

Cited by 6 publications

(8 citation statements)

References 8 publications

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“…To overcome this, normalization is needed to address the wide price range. The normalization process is done by converting the closing price values into values within the interval range of 0-1 based on the xnorm formula [26]. Equation 1 expresses the normalization formula.…”

Section: Normalizationmentioning

confidence: 99%

Comparing Long Short-Term Memory and Random Forest Accuracy for Bitcoin Price Forecasting

Ula,

Ilhadi,

Sidek

2024

matrik

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Bitcoin’s daily value fluctuations are very dynamic. Understanding its rapid and intricate price movements demands advanced techniques for processing complex data. This research aims to compare the accuracy of two machine learning methods, Random Forest (RF) and Long Short-Term Memory (LSTM), in predicting Bitcoin price. This research employs RF and LSTM algorithms to forecast Bitcoin prices using a two-year Yahoo Finance dataset. The evaluation metrics used were accuracy based on Mean Absolute Percentage Error (MAPE) and computational power (CPU-Z). As a result of this research, the LSTM model demonstrates higher accuracy compared to the RF model. MAPE reveals LSTM’s precision of 99.8% and RF’s accuracy of 90.1%. Regarding computational time and resources, RF shows slightly better performance than LSTM. The visual comparison further emphasizes LSTM’s better performance in predicting Bitcoin prices, highlighting its potential for informed decision-making in cryptocurrency trading. This research contributes valuable insights into the effectiveness, strengths, and weaknesses of LSTM and RF models in predicting cryptocurrency trends.

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Section: Normalizationmentioning

confidence: 99%

Comparing Long Short-Term Memory and Random Forest Accuracy for Bitcoin Price Forecasting

Ula,

Ilhadi,

Sidek

2024

matrik

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“…Salah satu jenis investasi yaitu investasi saham syariah. Saham syariah di Indonesia diperkenalkan pada 2011 dimana prinsip yang digunakan merujuk pada prinsip syariah dalam hukum islam yang disepakati oleh OJK (Otoritas Jasa Keuangan) dan MUI (Majelis Ulama Indonesia) [1]. Sebagai negara dengan mayoritas penduduk muslim terbesar di dunia, Indonesia meru-akan harga saham, maka para investor perlu lebih cermat dalam melakukan investasi.…”

Section: Pendahuluanunclassified

Prediksi Harga Saham Syariah menggunakan Bidirectional Long Short Term Memory (BiLSTM) dan Algoritma Grid Search

Puteri,

Darmawan,

Ruchjana

2024

Jambura J. Math

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Sharia stocks are one of the investment instruments in the Islamic capital market. In the capital market, it is known that stock prices are very volatile. This makes investors need to carry out a strategy for making the right decision in investing, one of which can be done by predicting stock prices. In this study, predictions were made using historical data on the closing price of Islamic shares of PT. Telkom Indonesia Tbk with the Bidirectional Long Short Term Memory (BiLSTM) method. In building the best prediction model, it is necessary to choose the right parameters and one way to do this is to use the grid search algorithm. Based on the results of the test analysis, it was found that the smallest Mean Absolute Percentage Error (MAPE) value was found in the BiLSTM model in the distribution of data with a percentage of 90% training data and 10% testing data and parameter values obtained based on parameter tuning using grid search, including the number of neurons 25, 100 epochs, 4 batches, and 0.2 dropouts. The MAPE obtained in this study was 10.83% and based on the scale on the MAPE value criteria, this shows that the resulting prediction model is accurate. As for the test results from the comparisons made on the BiLSTM and LSTM models using grid search as a tuning parameter and models without using a grid search or it can be called a trial and error approach as a tuning parameter, it is found that the model with better predictive performance is found in BiLSTM using a grid search. compared to other models.

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“…Saham syariah diluncurkan pertama kali di Indonesia pada tahun 2011. Dalam saham syariah telah diatur sesuai dengan prinsip syariah pada hukum islam dimana prinsip yang digunakan telah disepakati oleh OJK bersama MUI [2].…”

Section: Pendahuluanunclassified

“…Proses denormalisasi berfungsi untuk mengembalikan hasil keluaran yang masih dalam bentuk range 0-1 menjadi sesuai dengan nilai aktual dari data. Perhitungan proses denormalisasi mengacu pada [2] dengan menggunakan persamaan sebagai berikut:…”

Section: Denormalisasiunclassified

Implementasi Long Short Term Memory (LSTM) dan Bidirectional Long Short Term Memory (BiLSTM) Dalam Prediksi Harga Saham Syariah

Puteri

2023

Euler J. Ilm. Mat. Sains dan Teknol.

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The development of the stock market in Indonesia is currently growing quite rapidly. This can be seen based on the number of investors who have increased every year. In 2011, sharia stocks were launched for the first time in Indonesia, and it can be seen that the price of the stock is not always stable or can experience increases or decreases. For investors, a strategy is needed to predict stock prices in order to make the right decisions in investing. In this study, stock prediction was carried out using the Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) methods. The data used in this study is historical closing price data for three Sharia stocks, namely PT Aneka Tambang Tbk, PT Unilever Indonesia Tbk, and PT Indofood Sukses Makmur Tbk. In building the best predictive model in this study based on tuning parameters such as epoch, batch, neurons, as well as an optimizer and dropout regulation techniques to prevent overfitting of the model. The test results show that from the three stock data used, the smallest MAPE value is obtained in the BiLSTM model. The MAPE values obtained for each stock data in this study are sequentially 2,59%, 1,77%, and 1,05%. Based on the MAPE value criteria, the prediction model is included in the very accurate criteria.

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Prediksi Harga Saham Syariah Menggunakan Algoritma Long Short-Term Memory (LSTM)

Cited by 6 publications

References 8 publications

Comparing Long Short-Term Memory and Random Forest Accuracy for Bitcoin Price Forecasting

Comparing Long Short-Term Memory and Random Forest Accuracy for Bitcoin Price Forecasting

Prediksi Harga Saham Syariah menggunakan Bidirectional Long Short Term Memory (BiLSTM) dan Algoritma Grid Search

Implementasi Long Short Term Memory (LSTM) dan Bidirectional Long Short Term Memory (BiLSTM) Dalam Prediksi Harga Saham Syariah

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