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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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Journal : Jurnal Penelitian Pendidikan IPA (JPPIPA)

 1 
Study Review of the Speed of Light in Space-Time for STEM Student Lulut Alfaris; Ruben Cornelius Siagian; Eko Pramesti Sumarto
Jurnal Penelitian Pendidikan IPA Vol 9 No 2 (2023): February
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i2.2757

Abstract

The author of this article aims to review the theory of relativity and its implications for physics education by using visual aids and a programming approach. The article will cover the concept of the speed of light in space-time in the context of relativity, and provide illustrations that explain the relationships in the context of general relativity. The focus of the article will be to introduce students to complex concepts and encourage their interest in the topic. The author reports success in teaching the basic concept of the speed of light in space-time to both elementary STEM students and high school students. While the theory of relativity has been taught at the secondary school level in some education systems, there is a lack of research on the effectiveness of using visual aids and a programming approach to enhance students' understanding of the concept. This article aims to fill the gap by evaluating the impact of this approach on students' understanding of the theory of relativity
Basic Mechanics of Lagrange and Hamilton as Reference for STEM Students Budiman Nasution; Lulut Alfaris; Ruben Cornelius Siagian
Jurnal Penelitian Pendidikan IPA Vol 9 No 2 (2023): February
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i2.2920

Abstract

This paper discusses the use of Lagrangian and Hamiltonian dynamics as alternative approaches for understanding the motion of objects in classical mechanics. These approaches, which are based on different mathematical techniques, can provide a deeper understanding of the principles of classical mechanics and the motion of objects, but may not be covered in high school physics curricula or undergraduate STEM courses. The review paper approach is used to combine information from a variety of sources, and the material is conceptualized to aid reader understanding. These advanced topics may be of interest to advanced high school students who are interested in exploring topics beyond the high school physics curriculum, and can be studied independently by those with a strong foundation in classical mechanics and familiarity with advanced mathematical concepts.
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.
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