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

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.

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.

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.