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All Journal International Journal of Informatics and Computation
Hamzah
Universitas Respati Yogyakarta
Computer Science & IT

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Journal : International Journal of Informatics and Computation

 1 
Effective Stock Prediction Model Using MACD Method Hamzah; Sugeng Winardi
International Journal of Informatics and Computation Vol. 4 No. 2 (2022): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v4i2.51

Abstract

Stock market predictions help investors to optimize benefits in the financial markets. Various papers have proposed different techniques in stock market forecasting, but no model can provide accurate predictions. In this study, we discuss how to predict stock prices using a MACD (Moving Average Convergence/Divergence Oscillator) method. We collect the dataset, preprocess it, extract features, evaluate the model, and then deploy the MACD method to develop a stock price prediction model. In this study, we collect several features, including date, open, high, low, close, and volume, to conduct the training and testing process. The results of the experiments reveal good accuracy and a low error rate. As a result, it has the potential to be a promising solution for dealing with accurate and dynamic prices. Based on the experimental result, our proposed model can obtain a transaction profit rate of 40.00% and an average profit per transaction of 1.42%.
Robust Stock Price Prediction using Gated Recurrent Unit (GRU) Hamzah; Sugeng Winardi; Poly Endrayanto Eko Chrismawan; Rainbow Tambunan
International Journal of Informatics and Computation Vol. 5 No. 1 (2023): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v5i1.56

Abstract

Forecasting the direction of price movement of the stock market could yield significant profits. Traders use technical analysis, which is the study of price by scrutinizing past prices, to forecast the future price of the nickel stock price. Therefore, in this study, we propose Gated Recurrent Units (GRU) to predict nickel stock price trends. This research aims to produce an accurate nickel stock price trend prediction model. The research method utilized historical data on nickel stock prices from Yahoo Finance. The research results show that the model developed accurately predicted nickel stock price trends. From the RMSE, MAE, and MSE analysis results, the RMSE value was 0.0123, the MAE value was 0.0089, and the MSE value was 0.0002 on the test data.
Stock Price Prediction in Indonesia's Mining Sector Using a Hybrid Conv1D-LSTM Model Hamzah
International Journal of Informatics and Computation Vol. 6 No. 1 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i1.85

Abstract

This study presents a novel approach to forecasting stock prices in Indonesia's mining sector by leveraging a hybrid model combining Convolutional Neural Networks (Conv1D) and Long Short-Term Memory (LSTM) networks. Given the volatile nature of stock markets and the specific characteristics of the mining industry, accurate prediction models are essential for investors and analysts. The hybrid Conv1D-LSTM model integrates the feature extraction capabilities of Conv1D with the sequence learning strengths of LSTM, providing a robust framework for time series forecasting.
Co-Authors Poly Endrayanto Eko Chrismawan Rainbow Tambunan Sugeng Winardi