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 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.

Computational astronomy is a very important branch in today's era, where physicists or researchers can use computers to process statistics in astronomical physics. researchers can process abstract data from raw data and can convert data into data visualizations. Computational physics astronomy is a sophisticated and well-established method, this branch of science can provide and process data, solve complex problems, and is very helpful for statisticians and computer scientists. Astronomical physicists have many problems, among others; there is a problem that is hierarchical, and complex, so that this paper will provide a basis for methods for optimizing methods in processing statistical data on physics. The author's hope is that astronomical physicists can perform an important and effective processing of astronomical data optimally and effectively.

This study aims to analyze the correlation of temperature and humidity based on climatological data from Kuningan, West Java province, Indonesia, using non-linear regression and numerical analysis using the bisection method. In studying the education curriculum in Indonesia, most of them have studied linear regression, but only within certain limits, namely linear and multiple. It is still rare for students to use non-linear regression in their analysis. This paper will explain the analysis using non-linear regression as a source of data and equations in numerical analysis so that it can be applied in the context of the numerical bisection method. As for data processing and numerical analysis, Microsoft Excel is used. The authors combined the two variables in the non-linear analysis and used geometric equations for the numerical quotient analysis. The author displays the results of numerical graphs and real-time data obtained from the source "Data access viewer NASA."

The hyper-redundant type of robot is a type of robot that in carrying out its duties in the field of kinematics its degrees of freedom exceed the required minimum degrees. The advantage will be increased capability in operation and performance, if the degrees of freedom are excessive, even in unorganized and complex systems and environments. Algebraic approach method in inverse kinematics algorithm analysis can use; analytic algebra, jacobian basis, analytic KI, exponential multiplication, grobner, and conformal geometry. Iterative approach method in inverse kinematics algorithm analysis can use; genetic algorithm, fuzzy logic, ANFIS, and evolutionary algorithm. The geometric approach method in the inverse kinematics algorithm analysis can use; capital method. The purpose of this study is to analyze a virtual 2 arm robot, which will use axis manipulation in three dimensions using an inverse kinematics solution, using a geometric approach. How to step along on the z axis by rotating and using the reverse kinematics solution to the desired location. The visualization results will be repeated so as to ensure the effectiveness of the algorithm. As for this algorithm will provide a single solution, and this algorithm will prevent and reduce singularities if the link is lower.