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The Orbital Properties of Black Holes: Exploring the Relationship between Orbital Velocity and Distance Ruben Cornelius Siagian; Lulut Alfaris; Aldi Cahya Muhammad; Ukta Indra Nyuswantoro; Gendewa Tunas Rancak
Journal of Physics and Its Applications Vol 5, No 2 (2023): May 2023
Publisher : Diponegoro University Semarang Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpa.v5i2.17860

This research explores the concept of black holes in the physics of general relativity, including its formation and properties. The study focuses on the relationship between the orbital velocity and orbital distance of objects around a black hole, which is measured in units of the speed of light (c) and kiloparsecs (kpc), respectively. Using observational techniques, the study produces a plot showing the relationship between orbital velocity and orbital distance, which follows Kepler's law modified by the Newtonian theory of gravity and general relativity. The study also highlights the effective potential of particles in orbit around a black hole, which combines the effects of kinetic energy and gravitational potential. The effective potential shows the gravitational and relativistic properties of black holes, such as the photon orbit radius, ISCO, and the spin parameter. The resulting plot demonstrates the characteristics of the Milky Way black hole and how its spin parameter and Schwarzschild radius affect the orbital properties of surrounding particles. The study concludes that the closer the orbital distance is to the black hole, the more the orbital velocity increases, and particles with high spin parameters and small Schwarzschild radii are unlikely to escape the black hole's gravity.

Relationship between Solar Flux and Sunspot Activity Using Several Regression Models Ruben Cornelius Siagian; Lulut Alfaris; Ghulab Nabi Ahmad; Nazish Laeiq; Aldi Cahya Muhammad; Ukta Indra Nyuswantoro; Budiman Nasution
Jurnal Ilmu Fisika Vol 15 No 2 (2023): September 2023
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jif.15.2.146-165.2023

This study examines the correlation and prediction between sunspots and solar flux, two closely related factors associated with solar activity, covering the period from 2005 to 2022. The study utilizes a combination of linear regression analysis and the ARIMA prediction method to analyze the relationship between these factors and forecast their values. The analysis results reveal a significant positive correlation between sunspots and solar flux. Additionally, the ARIMA prediction method suggests that the SARIMA model can effectively forecast the values of both sunspots and solar flux for a 12-period timeframe. However, it is essential to note that this study solely focuses on correlation analysis and does not establish a causal relationship. Nonetheless, the findings contribute valuable insights into future variations in solar flux and sunspot numbers, thereby aiding scientists in comprehending and predicting solar activity's potential impact on Earth. The study recommends further research to explore additional factors that may influence the relationship between sunspots and solar flux, extend the research period to enhance the accuracy of solar activity predictions and investigate alternative prediction methods to improve the precision of forecasts.

Physics Visualization of Schwarzschild Black Hole through Graphic Representation of the Regge-Wheeler Equation using R-Studio Approach Budiman Nasution; Winsyahputra Ritonga; Ruben Cornelius Siagian; Lulut Alfaris; Aldi Cahya Muhammad; Ukta Indra Nyuswantoro; Gendewa Tunas Rancak
Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Vol. 20 No. 1 (2023): Sainmatika : Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam
Publisher : Universitas PGRI Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31851/sainmatika.v20i1.11845

This study aims to visualize the vibrations of black holes using the Regge-Wheeler equation in Cartesian coordinates. Black holes are astrophysical objects with extremely strong gravity, and understanding the vibrations around them provides insights into the nature and structure of black holes. The Regge-Wheeler equation is used to model these vibrations. In this study, the goal is to generate visual images that visualize the vibrations of black holes, including their frequencies, amplitudes, and possible vibration modes. Complex mathematical and computational methods were employed to create these visualizations. The findings of this research result in an intuitive and accurate visualizations of black hole vibrations. By observing the patterns and distributions of vibrations in visual form, complex concepts can be more easily understood and interpreted. These visualizations provide a better understanding of the characteristics of black hole vibrations and can serve as learning and comprehension tools for scientists and researchers. The accomplishment of this research addresses a deficiency in prior studies that lacked informative and intuitive visualizations of black hole vibration phenomena. The visualizations produced in this study make a significant contribution to our understanding of black hole vibration phenomena. The enhanced visualizations allow researchers to perceive patterns and distributions of vibrations more clearly, paving the way for new insights into the nature of black holes. The implications of this research are an improved understanding of black hole vibrations and a broader dissemination of knowledge about this phenomenon to the general public. The generated images can help communicate complex concepts more effectively, enhancing awareness and interest in black hole research.

Relationship Between BE4DBE2 and Variables n and z: A Comprehensive Analysis Using Linear Regression, Nonparametric Regression, Naive Bayes Classification, Decision Tree Analysis, SVM Analysis, K-Means Clustering, and Bayesian Regression Budiman Nasution; Winsyahputra Ritonga; Ruben Cornelius Siagian; Paulus Dolfie Pandara; Lulut Alfaris; Aldi Cahya Muhammad; Arip Nurahman
Jurnal Penelitian Pendidikan IPA Vol. 9 No. 11 (2023): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i11.4483

This research employed various statistical techniques, including linear regression, nonparametric regression, Naive Bayes classification, decision tree analysis, Support Vector Machine (SVM) analysis, k-means clustering, and Bayesian regression, to analyze nuclear data. The research aims to explore the relationships between variables, predict binding energy, classify nuclear data, and identify similar groups. The research results revealed that linear regression indicated a significant influence of the intercept and predictor variable 'n' on the variable 'BE4DBE2,' while the variable 'z' was not significant. However, the overall model had limited explanatory power. Nonparametric regression with smoothing functions effectively modeled the relationship between 'BE4DBE2' and variables 'n' and 'z,' explaining approximately 11% of the variability in the response variable. Classification using Naive Bayes successfully categorized nuclear data based on 'n' and 'z,' revealing their relationship. Decision tree analysis evaluated the performance of this classification model and provided insights into accuracy, agreement, sensitivity, specificity, precision, and negative predictive value. SVM analysis successfully built an accurate SVM model with a linear kernel, classifying nuclear data while depicting decision boundaries and support vectors. K-means clustering grouped nuclear data based on 'n' and 'z,' revealing distinct characteristics and enabling the identification of similar clusters. The Bayesian regression model predicted binding energy using 'n' and 'z' as independent variables, capturing the Gaussian distribution of 'BE4DBE2' and providing statistical measures for parameter estimation. Ccomprehensives nuclear data analysis using various statistical approaches provides valuable insights into relationships, predictions, classification, and clustering, contributing to the advancement of nuclear science and facilitating further research in this field.

Relationship Between BE4DBE2 and Variables n and z: A Comprehensive Analysis Using Linear Regression, Nonparametric Regression, Naive Bayes Classification, Decision Tree Analysis, SVM Analysis, K-Means Clustering, and Bayesian Regression Budiman Nasution; Winsyahputra Ritonga; Ruben Cornelius Siagian; Paulus Dolfie Pandara; Lulut Alfaris; Aldi Cahya Muhammad; Arip Nurahman
Jurnal Penelitian Pendidikan IPA Vol 9 No 11 (2023): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i11.4483

This research employed various statistical techniques, including linear regression, nonparametric regression, Naive Bayes classification, decision tree analysis, Support Vector Machine (SVM) analysis, k-means clustering, and Bayesian regression, to analyze nuclear data. The research aims to explore the relationships between variables, predict binding energy, classify nuclear data, and identify similar groups. The research results revealed that linear regression indicated a significant influence of the intercept and predictor variable 'n' on the variable 'BE4DBE2,' while the variable 'z' was not significant. However, the overall model had limited explanatory power. Nonparametric regression with smoothing functions effectively modeled the relationship between 'BE4DBE2' and variables 'n' and 'z,' explaining approximately 11% of the variability in the response variable. Classification using Naive Bayes successfully categorized nuclear data based on 'n' and 'z,' revealing their relationship. Decision tree analysis evaluated the performance of this classification model and provided insights into accuracy, agreement, sensitivity, specificity, precision, and negative predictive value. SVM analysis successfully built an accurate SVM model with a linear kernel, classifying nuclear data while depicting decision boundaries and support vectors. K-means clustering grouped nuclear data based on 'n' and 'z,' revealing distinct characteristics and enabling the identification of similar clusters. The Bayesian regression model predicted binding energy using 'n' and 'z' as independent variables, capturing the Gaussian distribution of 'BE4DBE2' and providing statistical measures for parameter estimation. Ccomprehensives nuclear data analysis using various statistical approaches provides valuable insights into relationships, predictions, classification, and clustering, contributing to the advancement of nuclear science and facilitating further research in this field.

Classification of Spiral and Non-Spiral Galaxies using Decision Tree Analysis and Random Forest Model: A Study on the Zoo Galaxy Dataset Lulut Alfaris; Ruben Cornelius Siagian; Aldi Cahya Muhammad; Ukta Indra Nyuswantoro; Nazish Laeiq; Froilan Delute Mobo
Scientific Journal of Informatics Vol 10, No 2 (2023): May 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i2.44027

Purpose: The goal of this research is to create a precise prediction model that can differentiate between spiral and non-spiral galaxies using the Zoo galaxy dataset. Decision tree analysis and random forest models will be used to construct the model, and various conditions within the dataset will be employed to classify the data accurately. The model's performance will be evaluated using a confusion matrix, and the probability of predicting spiral galaxies will be analyzed. The research will also investigate the differences in Total Power among signal types and identify Peak Frequency and Bandwidth values consistent across all signal types. This study is expected to provide important insights into galaxy classification and signal characteristics, specifically in the fields of astronomy and astrophysics.Methods: This study utilized the decision tree analysis research method to create a predictive model for identifying spiral galaxies using the Zoo galaxy dataset. The research approach focused on analyzing data before constructing a prediction model. The study did not involve random sampling, making it an observational study. Decision tree analysis was employed to classify galaxies into homogeneous groups, and a random forest model was used to classify galaxy types. This research provides insights into how decision tree analysis can be utilized to comprehend galaxy classification and can serve as a foundation for future research. To strengthen the conclusions, combining this research with other approaches such as experiments or random sampling can be considered.Result: This study developed a predictive model for classifying galaxies based on their Spiral type using decision tree analysis on the Zoo galaxy dataset. The model divided the data into specific groups based on certain conditions, and the results demonstrated exceptional accuracy of the random forest model in categorizing galaxy types. In addition, the study investigated various signal types in galaxies and found variations in Total Power, but consistent values for Peak Frequency and Bandwidth at 2 in all signals. These findings provide valuable insights into galaxy classification and signal characteristics, which could have practical applications in communication, signal processing, and analysis. The utilization of decision tree analysis and random forest models for galaxy classification and signal analysis represents an innovative approach in this field.Novelty: The novelty of this research lies in the new approach to categorizing galaxy types using decision tree and random forest models. Previously, the approach used to categorize galaxy types was through visual methods and observations via telescopes. This new approach provides a new and potentially more efficient way of processing galaxy image data, resulting in faster and more accurate categorization. Moreover, this research contributes to the development of signal analysis applications such as Total Power, Peak Frequency, and Bandwidth, which were previously only used in the fields of astronomy and astrophysics. However, they have the potential for wider applications in the fields of communication, signal processing, and analysis beyond astronomy

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

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.

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

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

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.

Risk Factors for Contamination of Pesticide Residues in Women's Breast Milk Farmers in Agricultural Ar-eas Intan Rachmawati; Sri Hernawati; Erma Sulistyaningsih; Aldi Cahya Muhammad
STRADA : Jurnal Ilmiah Kesehatan Vol. 10 No. 1 (2021): May
Publisher : Universitas STRADA Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30994/sjik.v10i1.675

Pesticides are all chemicals, serve as controls for pests and weeds' growth. This study aimed to identify the pesticide residue contamination in women farmers' breast milk in agricultural areas and analyze the risk factors. Quantitative descriptive research method with cross-sectional approach. A sample of 10 female farmers was selected using the purposive sampling technique. Data on risk factors were obtained by interview using a questionnaire, and pesticide residues in breast milk were measured by GC-MS/MS and LC-MS/MS. Spearman rank correlation tests and ordinal logistic regression are used to analyze data. The results showed that all of the respondents' breast milk was contaminated with organochlorine pesticide p'p DDE (> 0.001 mg/kg). Statistical test results prove that nutritional status / BMI with pesticide residue concentration has a significant relationship (p-value = 0.000). In conclusion, excess body mass index is a risk factor for pesticide residue contamination in female farmers in agricultural areas.

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