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All Journal Jurnal Ilmu dan Teknologi Kelautan Tropis IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 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 TECHNO-SOCIO EKONOMIKA Zona Laut : Jurnal Inovasi Sains dan Teknologi Kelautan
Lulut Alfaris
Department Of Marine Technology, Politeknik Kelautan Dan Perikanan Pangandaran
Aerospace Engineering Agriculture, Biological Sciences & Forestry 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 Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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A Monte Carlo Density Distribution Model Study to Analyze Galaxy Structure, Mass Distribution, and Dark Matter Phenomena Budiman Nasution; Ruben Cornelius Siagian; Winsyahputra Ritonga; Lulut Alfaris; Aldi Cahya Muhammad; Arip Nurahman
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 Budiman Nasution; Winsyahputra Ritonga; Ruben Cornelius Siagian; Veryyon Harahap; Lulut Alfaris; Aldi Cahya Muhammad; Nazish Laeiq
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 23 No. 04 (2023): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464) In Progress
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.
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.
Co-Authors Aldi Cahya Muhammad Aldi Cahya Muhammad Aldi Cahya Muhammad Aldi Cahya Muhammad Aldi Cahya Muhammad Aldi Cahya Muhammad Aldi Cahya Muhammad Aldi Cahya Muhammad Arif Baswantara Arip Nurahman Arip Nurahman Arip Nurahman Arip Nurahman Arip Nurahman Arip Nurahman Batih Shendy Capri Hareva Budiman Nasution Eko Pramesti Sumarto Eko Pramesti Sumarto Firdaus, Anas Noor Froilan Delute Mobo Gendewa Tunas Rancak Gendewa Tunas Rancak Ghulab Nabi Ahmad Godwin Latuputty Goldberd Harmuda Duva Sinaga Goldberd Harmuda Duva Sinaga Habibi Azka Nasution Indah Indah Ma’muri Ma’muri Mia Endang Sari Sinaga Miranda Putri Nazish Laeiq Nazish Laeiq Nazish Laeiq Nunik Wijayanti Pandara, Dolfie Paulus Putriara Tresa Fitira Raditya Danu Riyanto Raihan Natawisastra Rikha Bramawanto Rikha Bramawanto Ruben Cornelius Siagian Ruben Cornelius Siagian Ruben Cornelius Siagian Ruben Cornelius Siagian Ruben Cornelius Siagian Suhernalis Suhernalis Suhernalis Suhernalis Ukta Indra Nyuswantoro Ukta Indra Nyuswantoro Ukta Indra Nyuswantoro Ukta Indra Nyuswantoro Ukta Indra Nyuswantoro Veryyon Harahap Wanri Lumbanraja Wibowo, Yuni Ari Winsyahputra Ritonga Winsyahputra Ritonga Winsyahputra Ritonga Winsyahputra Ritonga