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All Journal Jurnal Matematika Sains dan Teknologi ComTech: Computer, Mathematics and Engineering Applications
Adelia Putri Pangestika
IPB University
Agriculture, Biological Sciences & Forestry Computer Science & IT Engineering Mathematics Other

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Comparison of the Symmetric and Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models in Forecasting the 2018-2023 Jakarta Composite Index Yenni Angraini; Adelia Putri Pangestika; I Made Sumertajaya
ComTech: Computer, Mathematics and Engineering Applications Vol. 15 No. 1 (2024): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v15i1.10610

Abstract

The Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) method assumes a homogeneous residual variance, but data with high volatility can cause violations of this assumption. Hence, it is interesting to compare the forecasting accuracy of symmetric and asymmetric Autoregressive Conditional Heteroskedasticity (ARCH) models in various data conditions. The research aimed to compare the accuracy of the symmetric ARCH/ Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and asymmetric TGARCH models in forecasting weekly Jakarta Composite Index (JCI) data on January 1st, 2018, to April 24th, 2023, by involving the influence of COVID-19 as a covariate variable and applying several validation scenario models to training and testing data. Based on the best-selected model, forecasting was carried out from May 1st, 2023, to July 3rd, 2023. The data used were weekly JCI opening data from January 1st, 2018, to April 24th, 2023, with the COVID-19 period as a covariate variable. The analysis results show that symmetric and asymmetric methods can handle violations of the heteroscedasticity assumption in the ARIMAX model. The best model produced based on four data validation scenarios is the asymmetric ARIMAX(3,1,3)-TGARCH(1,2) model with an average MAPE value of 3.158%. In this model, the COVID-19 variable significantly influences the JCI movement. Forecasting is done with forecasting results that are stable with confidence intervals that widen in each period.
The Impact of Data Splitting on ANN Performance in Predicting Foreign Tourist Visits to Inodnesia Akbar Rizki; Muhammad Dzakwan Alifi; Haidar Ramdhani; Lilis Indra Purnama; Shalma Kaisya Candradewi; Farid Yafi Suwandi; Adelia Putri Pangestika
Jurnal Matematika Sains dan Teknologi Vol. 26 No. 1 (2025)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v26i1.11104.2025

Abstract

The data sharing stage is an important step in model building using Artificial Neural Network (ANN) methods to avoid the risk of overfitting and underfitting that can affect model performance. Proper data division aims to ensure that the model can generalize well to data that has never been seen before. Generally, data sharing is done by dividing the dataset into two main parts, namely training and testing data. However, to better address overfitting, there are also those who divide the data into three parts, namely training, testing, and validation. This study aims to evaluate the performance of ANN modelling using these two ways of dividing data. The model is evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics to measure prediction error. The data used is data on foreign tourist arrivals to Indonesia, which has a fluctuating pattern and is influenced by calendar effects. The results show that the data division type with two groups generally produces a smaller MAPE value than the data division into three groups. However, the model with two parts of data is not able to capture the seasonal pattern in the data. On the other hand, the model with three parts of data can overcome this problem better. The best model was obtained with the proportion of training data, validation data, and test data of 80%, 10%, and 10%, respectively, which resulted in a MAPE value of 24.45%.
Co-Authors Akbar Rizki Farid Yafi Suwandi Haidar Ramdhani I Made Sumertajaya Lilis Indra Purnama Muhammad Dzakwan Alifi Shalma Kaisya Candradewi Yenni Angraini