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All Journal Indonesian Review of Physics (IRiP)
Siagian , Ruben Cornelius
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Astronomy Earth & Planetary Sciences Education Materials Science & Nanotechnology

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Assessing the Efficacy of the UV Index in Predicting Surface UV Radiation: A Comprehensive Analysis Using Statistical and Machine Learning Methods Ervianto, Edy; Marpaung, Noveri Lysbetti; Raisal, Abu Yazid; Hutabarat, Sakti; Hassan, Rohana; Siagian , Ruben Cornelius; Nurhalim, Nurhalim; Amri, Rahyul
Indonesian Review of Physics Vol. 6 No. 2 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/irip.v6i2.8216

Abstract

The study investigated the relationship between the UV Index and measurements of ultraviolet A (UVA) and ultraviolet B (UVB) radiation to evaluate the effectiveness of the UV Index in predicting and understanding UV radiation at the surface. The implications of this study are significant for public health policies and UV protection strategies. This study used a variety of statistical analyses and modelling techniques, including ANOVA, Naive Bayes classification, decision trees, artificial neural networks, support vector machines (SVM), and k-means clustering, to examine relationships and predict UV Index values. ANOVA analysis showed a significant relationship between the UV Index and UVA and UVB measurements. Prediction models such as Naive Bayes classification, decision trees, and artificial neural networks showed variability in their accuracy. Notably, SVM showed a high degree of accuracy in predicting UV Index values, while k-means clustering effectively clustered the data based on similarities in UV Index and UV measurements. These findings confirm that the UV Index is a reliable indicator for predicting and understanding UV radiation levels at the Earth's surface. This research underscores the importance of developing more accurate and precise UV Index prediction models. Further investigation is essential to understand the temporal variations and environmental impacts on the UV Index, as well as the broader implications of UV exposure on public health. This study lays a strong foundation for the development of early warning systems and more effective UV protection strategies, ultimately improving public health outcomes and safety measures against UV radiation.
A Monte Carlo Density Distribution Model Study to Analyze Galaxy Structure, Mass Distribution, and Dark Matter Phenomena Nasution, Budiman; Siagian , Ruben Cornelius; Ritonga, Winsyahputra; Alfaris, Lulut; Muhammad, Aldi Cahya; Nurahman, Arip
Indonesian Review of Physics Vol. 6 No. 1 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/irip.v6i1.8240

Abstract

This research uses the Monte Carlo density distribution model to study the structure and mass distribution of galaxies and the dark matter phenomenon. Through computer simulations, the research developed a mathematical model with parameters such as rho0, rc, beta, and others, to describe the structure and mass distribution of galaxies. The results show that the model can reproduce various galaxy structures, including groups, clusters and filaments, and influence the behavior and characteristics of individual galaxies. This research provides a deeper understanding of dark matter and its impact on the evolution of the universe. It has implications for improving our understanding of dark matter and the use of Monte Carlo density distribution models to study galaxies. This study provides new insights into the evolution of galaxies and their relationship with dark matter in cosmology. Using both physics and mathematical concepts, this research helps to understand the phenomenon of dark matter and the structure of galaxies, and provides a basis for further research on dark matter and galaxy evolution.
Co-Authors Abu Yazid Raisal Aldi Cahya Muhammad Budiman Nasution Edy Ervianto Hassan, Rohana Lulut Alfaris Noveri Lysbetti M Nurahman, Arip Nurhalim Nurhalim, Nurhalim Rahyul Amri Sakti Hutabarat winsyahputra Ritonga