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All Journal ComEngApp : Computer Engineering and Applications Journal ILMU KELAUTAN: Indonesian Journal of Marine Sciences Journal of Mechanical Engineering Science and Technology Eksakta : Berkala Ilmiah Bidang MIPA Indonesian Review of Physics (IRiP)
Muhammad, Aldi Cahya
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Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio Alfaris, Lulut; Firdaus, Anas Noor; Nyuswantoro, Ukta Indra; Siagian, Ruben Cornelius; Muhammad, Aldi Cahya; Hassan, Rohana; Aunzo, Jr., Rodulfo T.; Ariefka, Reza
ILMU KELAUTAN: Indonesian Journal of Marine Sciences Vol 29, No 2 (2024): Ilmu Kelautan
Publisher : Marine Science Department Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ik.ijms.29.2.273-284

Abstract

This research investigates the comparative predictive efficacy of two leading machine learning methodologies, specifically the XGBoost and Random Forest models, in estimating ocean temperature dynamics in the TS Gulf Stream and Labrador Current regions along the east coast of North America. Using annual temperature datasets and relevant oceanographic parameters, the data is carefully processed, cleaned and sorted into training and test subsets via the RStudio Platform. The performance evaluation model is carried out using predetermined machine learning assessment criteria, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared. The results show the superiority of the XGBoost model compared to Random Forest in terms of prediction accuracy and minimizing prediction errors. The XGBoost model shows lower MSE values and higher R-squared values than the Random Forest model, indicating its better capacity in explaining data variations. XGBoost consistently provides more accurate predictions and shows higher sensitivity in identifying important factors influencing ocean temperature fluctuations than Random Forest. This research significantly improves understanding and prognostic capabilities regarding ocean temperature dynamics in the TS Gulf Stream and Labrador Current regions. Empirical evidence underlines the efficacy of the XGBoost model in predicting ocean temperatures in the studied region. Continuous model evaluation and parameter refinement for both methodologies is critical to establishing standards for optimal prediction performance. The findings of this research have implications for the fields of oceanography and climate science, and offer potential pathways to comprehensively understand and mitigate the impacts of climate change on marine ecosystems.
Heat Conduction in Cylindrical Coordinates with Time-Varying Conduction Coefficients: A Practical Engineering Approach Alfaris, Lulut; Siagian, Ruben Cornelius; Muhammad, Aldi Cahya; Nasution, Budiman
Journal of Mechanical Engineering Science and Technology (JMEST) Vol 7, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um016v7i22023p157

Abstract

This research aims to develop a mathematical method for expressing the Laplace operator in cylindrical coordinates and applying it to solve heat conduction equations in various scenarios. The method commences by transforming Cartesian coordinates into cylindrical coordinates and identifying the necessary substitutions. The result is the expression of the Laplace operator in cylindrical coordinates, which is subsequently employed to address heat conduction equations within cylindrical coordinates. Various cases encompassing different initial and boundary conditions, as well as variations in the conduction coefficient over time, are meticulously considered. In each instance, precise mathematical solutions are determined and subjected to thorough analysis. This study carries substantial implications for comprehending heat transfer within cylindrical coordinate systems and finds relevance in a wide array of scientific and engineering contexts. The research's findings can be harnessed for technology development, heating system design, and heat transfer modeling across diverse applications, including mechanical engineering and materials science. Therefore, the research's contribution holds paramount significance in advancing our understanding of heat transfer within cylindrical coordinates and in devising more efficient and accurate solutions for an array of heat-related issues within the realms of science and engineering.
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.
Exploring Cosmological Dynamics: From FLRW Universe to Cosmic Microwave Background Fluctuations Nasution, Budiman; Ritonga, Winsyahputra; Siagian, Ruben Cornelius; Harahap, Veryyon; Alfaris, Lulut; Muhammad, Aldi Cahya; Laeiq, Nazish
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 24 No. 04 (2023): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol23-iss04/427

Abstract

This study explores key aspects of cosmology, starting with the foundational FLRW equations that describe the universe's evolution, emphasizing its homogeneity and isotropy. We incorporate mass viscosity into these equations, shedding light on its role in shaping the universe. Observations of Type Ia supernovae inform our understanding of cosmological parameters, including the Hubble rate and dark energy's effects on cosmic expansion. Cosmic Microwave Background fluctuations are analyzed for insights into cosmic structure. Baryon Acoustic Oscillations provide additional data for estimating critical parameters. We also examine the Hubble Parameter to understand its relation to cosmological parameters. Lastly, we introduce statefinder analysis, unveiling the universe's behavior through key indicators like "r" and "s." This study offers comprehensive insights into cosmology and the universe's evolution.
Nonparametric Regression Analysis of BE4DBE2 Relationship with n and z Variables using Naive Bayes and SVM Classification on Nuclear Data Siagian, Ruben Cornelius; Alfaris, Lulut; Muhammad, Aldi Cahya; Nyuswantoro, Ukta Indra Nyuswantoro; Rancak, Gendewa Tunas
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

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Abstract

This research article describes several analyses of nuclear data using various statistical methods. The first analysis uses linear regression to investigate the relationship between the independent variables (n and z) and the response variable (BE4DBE2). The second analysis uses a nonparametric regression model to overcome the assumptions of normality and linearity in the data. The third analysis uses the Naive Bayes method to classify nuclear data based on variables n and z. The fourth analysis uses a decision tree to classify nuclear data based on the same variables. Finally, the article describes an SVM analysis and a K-means analysis to classify and group nuclide data. The article presents clear and organized descriptions of each analysis, including visual representations of the results. The findings of each analysis are discussed, providing valuable insights into the relationships between the variables and the response variable. The article demonstrates the usefulness of statistical methods in analyzing nuclear data.
Co-Authors Ariefka, Reza Aunzo, Jr., Rodulfo T. Budiman Nasution Firdaus, Anas Noor Harahap, Veryyon Hassan, Rohana Laeiq, Nazish Lulut Alfaris Nurahman, Arip Nyuswantoro, Ukta Indra Nyuswantoro, Ukta Indra Nyuswantoro Rancak, Gendewa Tunas Siagian , Ruben Cornelius Siagian, Ruben Cornelius winsyahputra Ritonga