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All Journal Jurnal Ilmu dan Teknologi Kelautan Tropis ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ILMU KELAUTAN: Indonesian Journal of Marine Sciences Scientific Journal of Informatics Jurnal Penelitian Pendidikan IPA (JPPIPA) Spektra: Jurnal Fisika dan Aplikasinya Journal of Physics and Its Applications Jurnal Kelautan Nasional Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Jurnal Ilmu Fisika Jurnal Pendidikan Fisika dan Teknologi Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Kumparan Fisika Kappa Journal Authentic Research of Global Fisheries Application Journal (Aurelia Journal) Indonesian Review of Physics (IRiP) MARLIN : Marine and Fisheries Science Technology Journal Journal of Environmental Science and Sustainable Development TECHNO-SOCIO EKONOMIKA Community Development Journal: Jurnal Pengabdian Masyarakat Zona Laut : Jurnal Inovasi Sains dan Teknologi Kelautan Indo-MathEdu Intellectuals Journal Geosfera: Jurnal Penelitian Geografi Jurnal Pengabdian Masyarakat dan Riset Pendidikan Scientific Contributions Oil and Gas
Lulut Alfaris
Department Of Marine Technology, Politeknik Kelautan Dan Perikanan Pangandaran, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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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

Abstract

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.
Inverse Kinematic Algorithm with Newton-Raphson Method iteration to Control Robot Position and Orientation based on R programming language Budiman Nasution; Lulut Alfaris; Ruben Cornelius Siagian
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.82781

Abstract

 The homogeneous transform program is a function used to calculate the homogeneous transformation matrix at a specific position and orientation of a three-link manipulator. The homogeneous transformation matrix is a 4x4 matrix used to represent the position and orientation of an object in three-dimensional space. In the program, the rotation matrix R is calculated using the Euler formula and stored in a 4x4 matrix along with the position coordinates. The Jacobian matrix function calculates the Jacobian matrix at a specific position and orientation of a three-link manipulator using the homogeneous transformation matrix. The Euler formula used in the program is based on the rotation matrices for rotations around the x, y, and z-axes. The output of these functions can be useful for future research in developing advanced manipulators with improved accuracy and flexibility. Research gaps in exploring the limitations of these functions in real-world applications, particularly in scenarios involving complex manipulator configurations and environmental factors.
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

Abstract

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

Abstract

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

Abstract

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
Rancang Bangun Model Uji Kapal General Cargo 8202 DWT untuk Pengujian Hidrostatis Yuni Ari Wibowo; Lulut Alfaris; Anas Noor Firdaus; Nunik Wijayanti
Zona Laut : Jurnal Inovasi Sains Dan Teknologi Kelautan Volume 4, Nomor 3, Edisi November 2023
Publisher : Departemen Teknik Kelautan Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62012/zl.v4i3.31271

Abstract

The development of Indonesia's maritime industry cannot be separated from the growth of sea transportation facilities, in this case, namely the growth of the fleet of ships. However, despite this growth, ship accidents are still a crucial issue. One of the causes is sinking caused by poor ship stability. Transfer of cargo on board from loading-unloading activities causes changes in the stability of the ship. In general, ship stability can be analyzed using a numerical approach with hydrostatic analysis, but to accommodate non-linear behavior, model-test experiments are needed. This research focuses on the design of the model test of the General Cargo 8202 DWT ship. The model-test was made with a 1:60 scale which has a model length (L) of 1.80m, breadth (B) of 0.3m, height (D) of 0.23m and a draft (T) of 0.12m. The model-test is designed by modeling the linesplane and then compiling it into a 3D model. Each station on the ship is patterned on wood, cut and arranged to form a ship pattern, then covered with multiplex and fiber. The design procedure for the model-test made refers to the International Towing Tank Conference (ITTC) standard. Pond testing was carried out to identify the draft and inclination of the ship at 3 loading conditions: lightweight, ballasted load and full load. Based on the test results, the model-test’s draft was in accordance with the principal dimensions and the inclination tended to be stable.
SYNTHESIS AND CHARACTERIZATION OF ENVIRONMENTALLY FRIENDLY PAPERCRETE AS A NEW MORTAR FOR BRICK PANEL INSTALLATION Goldberd Harmuda Duva Sinaga; Ruben Cornelius Siagian; Mia Endang Sari Sinaga; Lulut Alfaris
TECHNO-SOCIO EKONOMIKA Vol 16 No 1 (2023): Jurnal Techno-Socio Ekonomika - April
Publisher : LPPM Universitas Sangga Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32897/techno.2023.16.1.2103

Abstract

This research focuses on the development of lightweight and soundproof building materials using waste paper or pulp as a component, with the aim of improving quality of life and reducing environmental pollution. Papercrete, made from unused waste paper, has environmentally friendly characteristics and can be used as the main structure of buildings. The study investigated the effect of pulp on the stress behavior of panels and tested the reusability of paper as a lightweight and eco-friendly brick material. The research method involved experimental procedures, using specialized equipment and materials to determine the properties and performance of different types of concrete mixes. Results indicate that papercrete has potential as an eco-friendly construction material with physical and mechanical properties suitable for reinforcing brick panels. Further research is recommended to investigate the effect of paper chip size on concrete strength under different humidity and temperature conditions, and to analyze the environmental impact of using papercrete as a construction material. The findings suggest that papercrete holds promise as a new and sustainable construction material. Testing papercrete in real-world scenarios could provide valuable insights into its durability and suitability for different applications.
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.
EXPLORING THE INTERCONNECTEDNESS OF COSMOLOGICAL PARAMETERS AND OBSERVATIONS: INSIGHTS INTO THE PROPERTIES AND EVOLUTION OF THE UNIVERSE Nasution, Budiman; Siagian, Ruben Cornelius; Nurahman, Arip; Alfaris, Lulut
Spektra: Jurnal Fisika dan Aplikasinya Vol. 8 No. 1 (2023): SPEKTRA: Jurnal Fisika dan Aplikasinya, Volume 8 Issue 1, April 2023
Publisher : Program Studi Fisika Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/SPEKTRA.081.03

Abstract

This research aims to investigate the relationship between Confidence Interval, Hubble Parameter, Comoving Distance, and Distance-Volume Relationship, which are important equations in cosmology. The Confidence Interval equation is used to estimate the range of values for the difference between the mean redshift and Hubble parameter. The Hubble Parameter equation is used to measure the expansion rate of the universe, while the Comoving Distance equation is used to calculate the distance between two objects in the expanding universe, and the Distance-Volume Relationship equation is used to calculate the distance between an observer and a cosmic object based on the object's redshift. This study seeks to address several research questions, including the accuracy of estimating parameters using these equations and the potential for developing more precise equations. The study employs cosmological data analysis using the R program to analyze existing data and gain a better understanding of cosmological parameters. The results of this research contribute to our understanding of the nature and evolution of the universe, providing insights into the distribution of matter and the role of dark matter and dark energy in shaping the universe's evolution. By examining the relationship between cosmological parameters, this study enables us to make predictions about cosmic phenomena and improve the accuracy of future measurements. The findings of this research have implications for cosmological research and can aid in the development of more accurate models and theories in the field of cosmology. Overall, this study provides valuable insights into the fundamental equations in cosmology and their relationships, advancing our understanding of the universe's dynamics and evolution.
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 Abdul Rahman Afriana Kusdinar Aldi Cahya Muhammad Anas Noor Firdaus Andri Wahyudi Ariefka, Reza Arif Baswantara Arip Nurahman Arip Nurahman Arip Nurahman Arip Nurahman Aunzo, Jr., Rodulfo T. Budiman Nasution Dolfie Paulus Pandara Eko Pramesti Sumarto Eko Pramesti Sumarto Firdaus, Anas Noor Froilan Delute Mobo Gendewa Tunas Rancak Gendewa Tunas Rancak Ghulab Nabi Ahmad Godwin Latuputty Goldbert Harmuda Duva Sinaga Hakim, Muhammad Romdonul Harahap, Veryyon Hareva, Batih Shendy Capri Hassan, Rohana Indah Indah Karim, Mohammad Alfin Kennedi Sembiring Laeiq, Nazish Martin Anjar Ginanjar Martin Anjar Ginanjar Ma’muri Ma’muri Mia Endang Sari Sinaga Nasution, Habibi Azka Nazish Laeiq Nazish Laeiq Nazish Laeiq Nazish Laeiq Nunik Wijayanti Nurahman, Arip Nyuswantoro, Ukta Indra Nyuswantoro, Ukta Indra Nyuswantoro Pandara, Dolfie Paulus Prayitno, Muhammad Riyono Edi Putri, Miranda Putriara Tresa Fitira Rahdiana, Nana Raihan Natawisastra Rancak, Gendewa Tunas Rikha Bramawanto Rikha Bramawanto Riyanto, Raditya Danu Ruben Cornelius Siagian Ruben Cornelius Siagian Ruben Cornelius Siagian Ruben Cornelius Siagian Ruben Cornelius Siagian Ruben Cornelius Siagian Sahroni, Taufik Roni, Mr. Sembiring, Kennedi Siagian , Ruben Cornelius Siagian, Ruben Cornelius Suhara, Ade Suhernalis Taufik Roni Sahroni Ukta Indra Nyuswantoro Ukta Indra Nyuswantoro Ukta Indra Nyuswantoro Ukta Indra Nyuswantoro Ukta Indra Nyuswantoro Ukta Indra Nyuswantoro Ukta Indra Nyuswantoro Verdi Yasin Wanri Lumbanraja Wibowo, Yuni Ari Winsyahputra Ritonga winsyahputra Ritonga