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All Journal JOIV : International Journal on Informatics Visualization JTAM (Jurnal Teori dan Aplikasi Matematika)
Miswan, Nor Hamizah
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Computer Science & IT Mathematics

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Visualization Tools for Backward Elimination Technique in Multiple Regression Time Series Modelling of CO2 Emissions in Malaysia Mansor, Mahayaudin M.; Ibrahim, Nurain; Zakaria, Roslinazairimah; Suhaila, Jamaludin; Miswan, Nor Hamizah; Shaadan, Norshahida
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3012

Abstract

Understanding multiple regression time series modelling is crucial because the procedures involve intricate statistical methods. This study incorporates a flowchart that clearly illustrates the steps for modelling a response variable affected by several explanatory variables via the backward elimination technique. The first objective of this study is to utilise ten graphical tools, comprising charts and tables, for visual assessment to support formal evaluations in model diagnostics using R programming. The aim is to provide comprehensive insights and improve the overall understanding of the modelling procedures. The visualisation tools include criteria for multicollinearity, goodness-of-fit, and underlying assumptions of normality, homoscedasticity, zero serial correlation, and volatility in the residuals. The second objective involves implementing modelling procedures to obtain a well-specified model in a real-world context, demonstrating its practical value and implications. In this instance, the selected response variable is carbon dioxide (CO2) emissions, significantly contributing to global warming. In Malaysia, CO2 emissions increased continuously from 1990 to 2022, with an alarming average annual growth rate of 4.9%. The visual diagnostics have helped guide the elimination of some explanatory variables in the initial model and refined the models, resulting in a well-specified final model that is parsimonious and explains 98.6% of the variability in CO2 emissions. The final model suggests that high fossil fuel use and GDP per capita are contributing factors to increased CO2 emissions in Malaysia. The study recommends government action and investment in renewable energy to reduce CO2 emissions by 45% by 2030 and achieve net-zero emissions by 2050.
Exchange Rate Prediction of BRICS Countries against US Dollar Based on Multiresponse Fourier series Estimator Mardianto, M. Fariz Fadillah; Maulidya, Utsna Rosalin; Ginzel, Bryan Given Christiano; Putra, Mochamad Rasyid Aditya; Pusporani, Elly; Miswan, Nor Hamizah
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 2 (2026): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i2.36983

Abstract

The dominance of the US dollar (USD) as the global reserve currency has begun to face structural challenges since the 2007-2008 financial crisis, which triggered the strengthening of the BRICS alliance. Although this alliance now controls 35% of the world's GDP and is actively pursuing de-dollarization, analysis of the volatility of their collective currencies is often limited to univariate parametric models that fail to capture inter-country dependencies and complex periodic fluctuation patterns. This study aims to fill this gap by applying a nonparametric multiresponse Fourier series regression to simultaneously model the interdependence of the five major BRICS currencies against the USD. Using weekly secondary data from June 2009 to February 2025 (817 observations) from investing.com, this study positions time as the predictor and the exchange rates of the five BRICS currencies as the response. The analysis results show that the best estimation model is obtained through a sine function without a trend component with an optimal oscillation parameter k=1, based on a minimum Generalized Cross Validation (GCV) value of 0.000702363. The prediction results from the training data produce a MAPE value of 4.7521%, which classifies the analysis as highly accurate. These findings strategically support the validation of the de-dollarization movement, providing a predictive instrument for developing countries to reduce their dependence on the USD, as well as strengthening the bargaining position of Eastern economies in a more multipolar international financial order.
Co-Authors Elly Pusporani Ginzel, Bryan Given Christiano Ibrahim, Nurain M. Fariz Fadillah Mardianto Mansor, Mahayaudin M. Maulidya, Utsna Rosalin Putra, Mochamad Rasyid Aditya Shaadan, Norshahida Suhaila, Jamaludin Zakaria, Roslinazairimah