Utsna Rosalin Maulidya
Department of Mathematics, Universitas Airlangga

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Exchange Rate Prediction of BRICS Countries against US Dollar Based on Multiresponse Fourier series Estimator M. Fariz Fadillah Mardianto; Utsna Rosalin Maulidya; Bryan Given Christiano Ginzel; Mochamad Rasyid Aditya Putra; Elly Pusporani; Nor Hamizah Miswan
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.
Assessment of Dietary Intervention Effects on Food Intake in Mus musculus using Repeated Measures ANOVA Suliyanto Suliyanto; Dita Amelia; Aini Divayanti Arrofah; Rindiani Ahmada Alisiah; Nuzulia Anida; Utsna Rosalin Maulidya
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.v9i4.32467

Abstract

The prevalence of type 2 diabetes, metabolic syndrome, along with obesity that causes disturbances in the body's metabolic processes are the main triggers of chronic liver disease or in scientific language called Non-Alcoholic Fatty Liver Disease (NAFLD), getting out of control. This makes managing this disease an increasingly serious global health challenge. One of the main factors influencing this condition is a high-fat diet and an unhealthy lifestyle. Therefore, evaluation of high-fat diet programs on metabolic parameters such as food intake patterns is important as a preventive measure. This study aims to analyze the differences in food intake levels with seven different types of dietary treatments for 28 days, which were tested on mice (Mus musculus) which have physiological and biochemical characteristics that almost resemble humans. The method used was analysis of variance (ANOVA) for longitudinal data to evaluate the dynamics of food consumption across diet groups and observation periods. The results showed that the type of dietary treatment significantly influenced food intake patterns over time, indicating that diet composition plays a crucial role in shaping eating behavior. These findings highlight the importance of both diet type and treatment duration in influencing consumption patterns. However, since this study has not yet identified the most effective dietary regimen, future research is recommended to investigate diet types with high variability, while considering additional factors such as age, sex, and physiological characteristics, as well as extending the observation period to better understand long-term impacts.