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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Komputer dan Informasi ILMU KELAUTAN: Indonesian Journal of Marine Sciences POSITRON Jurnal Neutrino : jurnal fisika dan aplikasinya Jurnal Pendidikan Fisika Indonesia Jurnal IPTEK Jurnal Hukum IUS QUIA IUSTUM Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIPF (Jurnal Ilmu Pendidikan Fisika) Journal of Physics: Theories and Applications Spektra: Jurnal Fisika dan Aplikasinya JRST (Jurnal Riset Sains dan Teknologi) Jurnal Pertahanan : Media Informasi tentang Kajian dan Strategi Pertahanan yang Mengedepankan Identity, Nasionalism dan Integrity Eksakta : Berkala Ilmiah Bidang MIPA Kappa Journal Journal of Environmental Science and Sustainable Development Jurnal Natural Time in Physics: Journal for Theoretical, Instrumentation, Material-Molecular, and Education Physics Aliansi : Jurnal Politik, Keamanan Dan Hubungan Internasional Scientific Contributions Oil and Gas
Siagian, Ruben Cornelius
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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 Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Other

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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 Celestial Object Characteristics: An In-depth Analysis of Quasars, Stars, and White Dwarfs Using the Sloan Digital Sky Survey (SDSS) Dataset Siagian, Ruben Cornelius; Nurahman, Arip; Sinaga, Goldberd Harmuda Duva; Ariefka, Reza; Pribadi, Pandu
TIME in Physics Vol. 2 No. 1 (2024): March
Publisher : Universitas Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/timeinphys.2024.v2i1p1-16

Abstract

This research utilizes the Sloan Digital Sky Survey (SDSS) dataset, examining 12,884 observations to explore quasars, stars, and white dwarf objects. Magnitude data and coordinates across five filter bands are analyzed, revealing unique features through statistical methods. The identification of 77,429 quasars with 15 dimensions enhances the dataset. Thorough analyses of stellar and white dwarf classes, coupled with visualization techniques, unveil variable relationships. Residual validation and Gaussian kernel density plots confirm significant class differences. Non-linear regression and a normal distribution mixture model depict complex variable relationships. A parallel coordinates plot aids in interpreting data patterns, while predictive modeling via regression exposes meaningful coefficients. Logistic regression effectively classifies astronomical objects in the SDSS training data. This research contributes to understanding celestial object characteristics, offering valuable insights for astronomers and astrophysicists in analyzing large-scale astronomical datasets.
Hawking Temperature Modification and the Physical Dynamics of Black Holes: A Study of the Influence of Internal and Cosmological Variables Sahroni, Taufik Roni; Sinaga, Goldberd Harmuda Duva; Jaya, Pratama; Purwanto, Romdhon; Muhlis, Dasep; Siagian, Ruben Cornelius
JIPF (Jurnal Ilmu Pendidikan Fisika) Vol 9, No 2 (2024): May 2024
Publisher : STKIP Singkawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26737/jipf.v9i2.4647

Abstract

The main objective of the study is to develop a model that modifies the traditional Hawking temperature by considering the influence of internal variables such as radius, mass, electric charge, angular momentum, and cosmological constant. The research method involves mathematical analysis and computational modeling based on the modified Hawking temperature equation. The results show that the modified Hawking temperature produces non-linear corrections that show the interaction between black holes and the quantum structure of spacetime. graphical representations visualize the variation of Hawking temperature with changes in area, electric charge, angular momentum, and cosmological parameters. The implications of the research extend to the understanding of the thermal properties of black holes in the context of gravitational and quantum theories. The research identifies gaps in the knowledge of the effects of cosmological parameters on black hole thermodynamics and introduces Hawking temperature modifications that have not been mapped in detail before. The study concludes that the Hawking temperature modification provides a strong foundation for further research in black hole physics, particularly in the effect of physical and cosmological parameters on the thermal properties of black holes.
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.
VISUALIZATIONS AND ANALYSES OF QUANTUM BEHAVIOR, SPACETIME CURVATURE, AND METRIC RELATIONSHIPS IN RELATIVISTIC PHYSICS Sinaga, Mardame Pangihutan; Pandara, Dolfie Paulus; Nyuswantoro, Ukta Indra; Nasution, Budiman; Siagian, Ruben Cornelius
Jurnal Neutrino:Jurnal Fisika dan Aplikasinya Vol 16, No 1 (2023): October
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/neu.v16i1.20641

Abstract

This study aims to investigate essential concepts in quantum mechanics and theoretical physics, with an emphasis on the 1+1 dimension. We examine the Dirac equation for relativistic spin-1/2 particles, the Time-Dependent Schrödinger Equation in 1+1 spacetime with flat conformal metric, and connect them to the Dirac equation. Additionally, we explore the Alcubierre Metric related to warp drive, particle modeling in a harmonic potential using the Schrödinger Equation, and the Gödel Metric Solution to depict the peculiarities of spacetime. The research aims to deepen the understanding of these concepts, identify theoretical implications, and their potential applications. This research aims to enhance the understanding of fundamental physics, assist in the development of future technologies, and provide deeper insights into the universe. Its benefits lie in contributing to theoretical understanding in physics, which can spark the development of new theories. This study is limited to physics concepts in the 1+1 dimensions, without empirical experiments or practical applications. The primary focus is on the theoretical analysis of these concepts. The results of this research have potential theoretical implications in understanding basic physics and spacetime phenomena. The simplification and connections between these concepts can aid in the development of new theories in theoretical physics. The uniqueness of this research lies in its integrative approach to quantum mechanics and theoretical physics concepts in the 1+1 dimension, which may not have been extensively explored previously. Through this research, we have investigated several key concepts in quantum mechanics and theoretical physics in the 1+1 dimension. These findings can make a significant contribution to our understanding of the universe and the potential development of new theories in physics.
Solving the Fermi equation for Modeling Nuclear Decay: Approaches, Assumptions, and Limitations in the Context of the Thomas-Dirac Distribution Siagian, Ruben Cornelius; Nasution, Budiman; Jubaidah, Jubaidah
Jurnal IPTEK Vol 27, No 2 (2023): December
Publisher : LPPM Institut Teknologi Adhi Tama Surabaya (ITATS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.iptek.2023.v27i2.4424

Abstract

This study explores a sophisticated probabilistic model for radioactive decay, emphasizing decay probability in small time intervals. Equation (1), with decay constant and time interval (dt), is central. Integration yields Equations (2), (3), and (4), describing total decay (N) over larger intervals (T). The Poisson distribution links to Equation (5), depicting decay events with average rate. In radioactive decay, the binomial distribution is relevant for independent nuclei (R). Equations (7) and (8) outline the probability of observing (N) decays, utilizing the binomial distribution and coefficient. Equation (9) simplifies via the Poisson distribution and factorial (n), notably eliminating (R-N). This reveals the efficiency of representing binomial distribution properties. The study extends to analyzing radiotracers in nuclear medicine through visualized data, revealing properties like half-life and decay constants on graphs. Graphical analysis identifies time's role in deviation from true values, offering insights into radiotracer reliability. This amalgamation of probabilistic methods and radiotracer analysis significantly contributes to understanding and applying radioactive decay concepts in diverse scientific and medical contexts.
Exploring Cosmological Dynamics: From FLRW Universe to Cosmic Microwave Background Fluctuations Nasution, Budiman; Ritonga, Winsyahputra; Siagian, Ruben Cornelius; Harahap, Veryyon; Alfaris, Lulut; Muhammad, Aldi Cahya; Laeiq, Nazish
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 24 No. 04 (2023): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
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.
Analysis of Radioactive Isotope Concentrations at Various Locations after a Nuclear Disaster: Case Study of I-131, Cs-134, and Cs-137 in the Czech Republic Rajagukguk, Michael Haratua; Siagian, Ruben Cornelius
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 1 Maret 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i1.23515

Abstract

Radioactive dispersal remains a major concern after a nuclear disaster. This study investigated the consistency of radioactive isotope concentrations (I-131, Cs-134, and Cs-137) in two cities in the Czech Republic - Prague and Usti - to determine whether sampling duration and isotope concentration variability affect contamination stability. The study used statistical analyses, including ANOVA, Kruskal-Wallis test, and Pearson and Spearman correlation tests, to examine isotope relationships and spatial variation. Data were collected over multiple time points to assess changes in contamination patterns. Findings showed that Prague exhibited higher concentrations of radioactive isotopes, but variations in sampling time did not affect contamination stability. No significant differences were observed between the two locations, and a strong correlation was found among I-131, Cs-134, and Cs-137, indicating that an increase in one isotope was consistently accompanied by an increase in the other. The sampling duration had no significant impact on the contamination levels. These results suggest that isotope contamination is stable across sites, regardless of sampling duration. A major research gap is the limited research on the relationship between consistency of isotope concentrations and sampling time across multiple sites. The study highlighted that radioactive isotope concentrations remained relatively consistent despite the large variability in measured values. Findings underscore the need for contamination management strategies that focus on globally significant sources rather than local variability. Strong correlations among isotopes offer potential predictive value for monitoring radioactive contamination in disaster situations.
Application of Machine Learning Methods for Classification of Gamma and Hadron Signals in High Energy Particle Detection Wibowo, Firdaus Andi; Yulianto, Tomi; Malun, Nicholaus Ola; Rionaldy, Rizqy; Yasin, Verdi; Siagian, Ruben Cornelius
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 2 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i2.1489

Abstract

A major challenge in particle physics is the binary classification of high-energy gamma signals against a complex hadron background. Accurate identification of these gamma signals is critical for particle detection, especially as the volume and complexity of data increases as technology advances. The research developed a machine learning-based classification model to efficiently and accurately distinguish gamma signals from hadrons. Logistic Regression, Decision Trees, Random Forests, and Artificial Neural Networks are used for classification. Principal Component Analysis (PCA) and correlation analysis identified dominant features, while Monte Carlo simulations validated the distribution of gamma and hadron spectra. This study focuses on geometric parameters such as fLength, fWidth, fAlpha, as well as photon distribution and distance effects (fDist) in gamma signal identification using K-Means clustering. The Random Forest algorithm achieved the highest accuracy of 87.96%, with an F1-score of 0.91, which defines its robustness in the classification task. PCA and correlation analysis showed fSize, fLength, and fWidth as the most influential factors in classification. Monte Carlo simulations successfully replicated the spectral distribution pattern with high experimental validation. The research presents a novel integration of geometric analysis, clustering techniques, and simulation validation in the classification of high-energy particles. Machine learning methods, in particular Random Forest, effectively distinguish the gamma signal from the hadron background. The combination of PCA and Monte Carlo simulations improves the understanding of data distribution patterns and key classification factors. This research contributes to the development of a more reliable astrophysical signal classification system with potential applications in large-scale astronomical data management.
Development of an Early Warning System Using Social Media for Flood Disaster I Ketut Kasta Arya Wijaya; Siagian, Ruben Cornelius
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5087

Abstract

This research paper introduces an innovative prototype system that uses IoT technologies to monitor floodwater levels. Integration of an ultrasonic sensor, ESP8266 microcontroller, Arduino IDE, and the ThingSpeak platform aims to establish a robust flood monitoring solution. The paper provides a thorough exploration of the system's background, the problem it addresses, the methodology employed, and the obtained results, along with insights into future research directions. The study meticulously describes the design, implementation and programming code for data collection and transmission within the system. Through extensive field testing and meticulous data analysis, the paper evaluates the precision and effectiveness of the proposed flood monitoring solution. In particular, research underscores the advantages of IoT, emphasizing real-time data collection, logging, and analysis as essential components for efficient flood management. Additionally, the paper elucidates step-by-step instructions for configuring Telegram notifications through the ThingSpeak React app, enhancing the practical applicability of the developed system. The research effectively highlights the potential of IoT in flood monitoring, showcasing its superior accuracy and effectiveness compared to traditional methods. By demonstrating the feasibility and advantages of IoT in the context of flood monitoring, this study contributes valuable information, enriching existing knowledge, and paving the way for future advances in the field. Research encourages the continued exploration of advanced techniques to strengthen flood monitoring and management strategies. Ultimately, this work presents a comprehensive IoT-based prototype for floodwater monitoring, offering valuable information and fostering the promising role of IoT technologies in this critical domain.
Co-Authors Alatif, Yahya Halim Aldi Cahya Muhammad Ali Azhar Andri Wahyudi Ariefka, Reza Aunzo, Jr., Rodulfo T. Bayu Dwi Anggono Budiman Nasution Dominikus Rato Firdaus, Anas Noor Goldbert Harmuda Duva Sinaga Harahap, Veryyon Hassan, Rohana I Gede Widhiana Suarda Jaya, Pratama Jubaidah, Jubaidah Karim, Mohammad Alfin Kasim, Mohd Shahir Laeiq, Nazish Lulut Alfaris Malun, Nicholaus Ola Muannif Ridwan Muhlis, Dasep Nasution, Habibi Azka Novyar Satriawan Fikri, KMS Nurahman, Arip Nyuswantoro, Ukta Indra Nyuswantoro, Ukta Indra Nyuswantoro Pandara, Dolfie Paulus Pribadi, Pandu Purwanto, Romdhon Putra, Steinly Suwanto Rahdiana, Nana Rajagukguk, Michael Haratua Rancak, Gendewa Tunas Rionaldy, Rizqy Sahroni, Taufik Roni Sahroni, Taufik Roni, Mr. Sinaga, Mardame Pangihutan Siregar, Vivi Ariani Suhara, Ade Taufik Roni Sahroni Ukta Indra Nyuswantoro Verdi Yasin Wardana, Febriansyah Wibowo, Arnowo Hari Wibowo, Firdaus Andi winsyahputra Ritonga Yulianto, Tomi