Model Hybrid Menggunakan Dekomposisi-Neural Network Untuk Data Indeks Harga Saham Gabungan
DOI:
https://doi.org/10.36982/jiig.v13i3.2696Abstract
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The development of Covid-19 has worsened the economy not only nationally but also globally. Since its spread, the price movement of the Jakarta Composite Index (IHSG) has continued to be volatile. JCI price volatility shows risk and uncertainty in investing. Volatility is used as a barometer to determine portfolio management strategies for financial actors. Therefore, financial actors should find a strategy to be able to predict JCI price movements to reduce risks and gain profits. One way that can be done is to predict the JCI price as a reference in investing. This study uses a hybrid model between the decomposition model and the Neural Network (NN) in predicting JCI price volatility. The decomposition uses two approaches, namely additive and multiplicative, the two approaches will then be combined with NN and the NN algorithm used is Feed Forward Neural Network (FFNN) where the results of the decomposition in the form of seasonal, trend, and random data are used as input in the FFNN architecture. The FFNN architecture in this study differs from the hidden layer nodes and the epochs used. Furthermore, the prediction results from the model are compared with a single NN. The performance of each architecture will be measured using the Mean Absolute Error (MAE) and Mean Square Error (MSE). The results show that the hidden layer with more nodes can provide good performance while the epoch used provides good performance depending on the learning process carried out. The prediction results using the hybrid model can outperform the performance of a single NN.
Keywords : time series, volatilitas, studi perbandingan, kecerdasan buatan, statistik.
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