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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Komputer dan Informasi ILMU KELAUTAN: Indonesian Journal of Marine Sciences Jurnal Neutrino : jurnal fisika dan aplikasinya Jurnal Pendidikan Fisika Indonesia Jurnal IPTEK Jurnal Fisika FLUX Journal of Mechanical Engineering Science and Technology Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIPF (Jurnal Ilmu Pendidikan Fisika) Jurnal Penelitian Pendidikan IPA (JPPIPA) Spektra: Jurnal Fisika dan Aplikasinya JRST (Jurnal Riset Sains dan Teknologi) Eksakta : Berkala Ilmiah Bidang MIPA Kappa Journal Journal of Environmental Science and Sustainable Development Jurnal Ilmu Pendidikan Dasar Indonesia Jurnal Natural Time in Physics: Journal for Theoretical, Instrumentation, Material-Molecular, and Education Physics Aliansi : Jurnal Politik, Keamanan Dan Hubungan Internasional
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 Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Other

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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.
Nonparametric Regression Analysis of BE4DBE2 Relationship with n and z Variables using Naive Bayes and SVM Classification on Nuclear Data Siagian, Ruben Cornelius; Alfaris, Lulut; Muhammad, Aldi Cahya; Nyuswantoro, Ukta Indra Nyuswantoro; Rancak, Gendewa Tunas
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research article describes several analyses of nuclear data using various statistical methods. The first analysis uses linear regression to investigate the relationship between the independent variables (n and z) and the response variable (BE4DBE2). The second analysis uses a nonparametric regression model to overcome the assumptions of normality and linearity in the data. The third analysis uses the Naive Bayes method to classify nuclear data based on variables n and z. The fourth analysis uses a decision tree to classify nuclear data based on the same variables. Finally, the article describes an SVM analysis and a K-means analysis to classify and group nuclide data. The article presents clear and organized descriptions of each analysis, including visual representations of the results. The findings of each analysis are discussed, providing valuable insights into the relationships between the variables and the response variable. The article demonstrates the usefulness of statistical methods in analyzing nuclear data.
Analysis of particle dynamics, event horizon, and thermodynamic properties of black holes in the Ghasemi-Nodehi-Bambi metric SINAGA, GOLDBERD HARMUDA DUVA; SIAGIAN, RUBEN CORNELIUS
Jurnal Natural Volume 25 Number 2, June 2025
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v25i2.43643

Abstract

Black holes, as complex astrophysical objects, are strongly influenced by their metric parameters. This research focuses on the Ghasemi-Nodehi-Bambi (G-N-B) metric, an extension of the Kerr solution, which introduces additional parameters to more accurately describe the properties of black holes. The research investigates the impact of parameter variations in the G-N-B metric on black hole thermodynamics, particle dynamics, and event horizon structure. Analytical and numerical methods are applied to examine the quadratic equations governing event horizons, particle motion, and black hole temperature and entropy. The research explores the variation of parameters (M) and (a) within a certain range to evaluate their influence on the gravitational distribution and the effect of frame attraction. The distribution of (M) values was visualized on a logarithmic scale to highlight the sensitivity of the system to parameter changes. The study found that variations in the G-N-B metric parameter significantly affect the event horizon, with the likelihood of extreme black holes or naked singularities forming depending on the discriminant of the quadratic equation. Particle motion is affected by parameters (M) and (a), which alter the gravitational field and orbital stability. The black hole temperature and entropy show significant changes: an increase in (M) increases gravity and surface temperature, while an increase in (a) decreases temperature due to rotational effects. The research improves the understanding of black holes beyond the Kerr model, especially in terms of black hole thermodynamics and time-space structure.
Learning Physical Education, Sports, and Early Childhood Health Based on Educational Games Siagian, Ruben Cornelius; Wibowo, Rizky Tri; Fadhilah, Dimas Imam; Lubis, Putri Rahmadani; Simamora, Ursulla Duti Oktria; Isnan, M Abdurrahman Ridwan; Silalahi, Christeven
Jurnal Ilmu Pendidikan Dasar Indonesia Vol 1 No 3 (2022): JUNI
Publisher : Pusat Pengembangan Pendidikan dan Bakat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51574/judikdas.v1i3.460

Abstract

Physical education is an inseparable part of national education which aims to develop the abilities of students through physical activities. Physical education planning is carried out carefully to meet the behavioral development, growth, and needs of each child. Physical education not only develops psychomotor abilities, but also develops students' cognitive and affective abilities. Physical education learning starts early to start organic, motor, intellectual and emotional development. This study uses a descriptive qualitative approach by collecting and downloading an article or journal that conducts research directly in the field. The population in this study, which was taken from a journal, involved 3 teachers and 35 early childhood children, which was intended to obtain research results from the implementers and recipients of learning. Data collection techniques used open interviews, observation and documentation during the data collection process. The data analysis technique uses data reduction, data presentation and conclusion drawing which aims to provide research results that are in accordance with the reality in the field. Early childhood physical education currently shows complex problems, in terms of learning, facilities, social conditions and government policies. However, the purpose of education must be implemented properly to develop cognitive, affective and psychomotor abilities of early childhood. Nature-based educational activities are very suitable to be applied because apart from being able to practice corners during the pandemic, PAUD students can play comfortably and have fun.
Approximative Relationship Between The Energy Function (E) and Hubble Function (H) in Cosmology: Practical and Theoretical Analysis Sahroni, Taufik Roni; Pandara, Dolfie Paulus; Wibowo, Arnowo Hari; Alatif, Yahya Halim; Wardana, Febriansyah; Kasim, Mohd Shahir; Siagian, Ruben Cornelius
Jurnal Pendidikan Fisika Indonesia Vol 20, No 1 (2024)
Publisher : Department of Physics, Faculty of Mathematics and Natural Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpfi.v20i1.43488

Abstract

This research delves into the approximate relationship between the energy function (E) and the Hubble function (H) within cosmological. Utilizing the Friedmann equation, it establishes a link between the universe's scale factor and the Hubble function. Through Taylor series approximation, the study derives an approximation of the energy function, under specific assumptions and approximations. Asymptotic analysis investigates the behavior of variables y and s, shedding light on function limits and behaviors. The study incorporates an interactive 3D scatter plot visualization to elucidate the relationship between cosmological parameters and physical systems, aiding in a comprehensive understanding of dynamics. Practical recommendations emphasize increasing data points for accuracy and validating with observational data, while theoretical suggestions advocate exploring higher-order terms and considering additional physical factors.
Analysis of Solar Flux and Sunspot Correlation Case Study: A Statistical Perspective Siagian, Ruben Cornelius; Alfaris, Lulut; Nasution, Budiman; Nasution, Habibi Azka
Kappa Journal Vol 7 No 1 (2023): April
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/kpj.v7i1.12238

Abstract

This analysis examines the relationship between the number of solar flares and the number of sunspots in 2005 using 11 observations in months 2 to 12. The number of solar currents measures the intensity of the radiation emitted by the Sun, while the number of sunspots measures the number of sunspots on the surface of the Sun. Multivariate linear regression analysis was used to analyze the relationship between Solar Current Rate and Number of Sunspots. The results of the analysis show that the coefficient of the Amount of Solar Current is 1.1239 with a significant t value of 2.510 (probability that there is no effect on the Number of Sunspots is 3.33%). The linear regression model has good results with an F-statistic value of 6.301 and a p-value of 0.0333, with an R-squared value of 0.4118 which indicates that 41.18% of the variation in the number of sunspots is influenced by variations in the amount of solar currents. The corrected R-squared value is 0.3464 indicating that there are still variations in the number of sunspots that cannot be explained by variations in the number of solar currents. ARIMA analysis results show an MA coefficient of 0.7351 with an average value of 45.9542 and a s.e value of 0.2590 and 6.1550 respectively. The AIC, AICc, and BIC values are 92.97, 96.4, and 94.16. The error results in the training set show that the ME value is 0.2615561, the RMSE value is 12.16969, the MAE value is 9.03306, the MPE value is -15.14689, the MAPE value is 30.42013, and the MASE value is 0.674109. The ACF1 value in the exercise set is 0.0808969.
Separation of Variables Method in Solving Partial Differential Equations and Investigating the Relationship between Gravitational Field Tensor and Energy-Momentum Tensor in Einstein's Theory of Gravity Siagian, Ruben Cornelius; Alfaris, Lulut; Nurahman, Arip; Muhammad, Aldi Cahya; Nyuswantoro, Ukta Indra; Nasution, Budiman
Kappa Journal Vol 7 No 2 (2023): Agustus
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/kpj.v7i2.20921

Abstract

This research delves into the study of partial differential equations (PDEs) and gravitational fields in spacetime. It focuses on solving PDEs using the Separation of Variables method and explores the relationship between the gravitational field tensor and the energy-momentum tensor, leading to the final equation for the gravitational field tensor. The research also investigates Einstein's theory of gravity and the energy-momentum tensor integral, providing the general solution for the gravitational potential and its implications. Additionally, the mean integration of the gravitational wave tensor is analyzed, yielding an expression for the tensor strain of gravitational waves over an infinitely long period. The components of the gravitational wave tensor and their effect on gravitational sources are examined. Furthermore, the propagation of electromagnetic fields in spacetime is studied using the Retarded Green's Function. The primary objectives of this research are to understand and explore mathematical techniques for solving PDEs and analyzing gravitational fields and their interactions in spacetime. The integration of multiple theoretical concepts related to PDEs, gravitational fields, and electromagnetic fields enhances our understanding of fundamental physics principles. This contributes to the advancement of theoretical physics and opens avenues for potential practical applications, such as gravitational wave detection and electromagnetic field propagation in complex media. In conclusion, this research provides valuable insights into fundamental physics principles and fosters a deeper understanding of their interconnections and implications
Non-Linear Force Dynamics in Black Hole Accretion Disks Using Logarithmic and Trigonometric Approximations Situmorang, Adi Suarman; Sinaga, Juli Antasari Br; Rintaningrum, Ratna; Siagian, Ruben Cornelius
Jurnal Penelitian Pendidikan IPA Vol 11 No 2 (2025): February
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i2.10291

Abstract

Accretion disks are material structures that form when gas, dust or plasma is pulled towards a black hole by its strong gravity. In rotating (Kerr) black holes, the dynamics of the accretion disk becomes more complex due to frame-dragging effects that affect particle trajectories and energy release patterns. This study aims to develop an algorithm for calculating the force F(x) on particles in the accretion disk by considering the non-linear interaction between the black hole spin parameter and the relative position of the particles. The developed algorithm uses logarithmic and trigonometric approaches to improve the accuracy of the force calculation. The results show that variations in the spin parameter and relative position significantly affect the force distribution in the accretion disk. Visualization of the force interaction reveals the existence of non-linear patterns that contribute to the system dynamics. The main contribution of this research is the refinement of force calculation models that previously did not fully incorporate the combined effects of logarithmic and trigonometry in particle interactions. The proposed approach offers a more accurate predictive tool to explore the physical processes around black holes, and supports the interpretation of observational data from telescopes and gravitational wave detectors
IMPACT OF URBAN DEVELOPMENT ON UV EXPOSURE: A CLUSTERING AND MACHINE LEARNING ASSESSMENT Sahroni, Taufik Roni, Mr.; Yasin, Verdi; Alfaris, Lulut; Ariefka, Reza; Siagian, Ruben Cornelius; Karim, Mohammad Alfin; Rahdiana, Nana; Suhara, Ade
Journal of Environmental Science and Sustainable Development Vol. 7, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The relocation of Indonesia's capital city is anticipated to promote inclusive economic growth while embracing cultural diversity. However, this transition may affect ultraviolet (UV) radiation exposure patterns. The study investigated variations in UV exposure in the IKN region, focusing on urban development factors such as land use and population density that affect public health, sun protection, and skin cancer prevention. The research hypothesized that UV radiation is significantly correlated with these factors. UV Index data from 2010-2023, a hierarchical clustering method, identifies complex data patterns without determining the number of clusters. XGBoost, a machine learning model, was used for handling high-dimensional data and strong non-linear interactions, outperforming Random Forest in predicting Ultraviolet A variables. Analysis of variance (ANOVA) showed significant inter-group differences, which were validated by Tukey HSD post-hoc tests. Results showed that Cluster 4 was the region with the highest UV exposure. In contrast, Cluster 5 recorded the lowest, with exposure levels ranging from 6.61 to 15.82, a considerable difference of 9.21. The findings underscore the role of geographic and environmental factors in shaping UV exposure patterns, with implications for public health. Areas with high UV exposure face higher risks, including skin cancer and premature ageing. The predictive accuracy of the XGBoost model highlights its usefulness in addressing UV-related health risks. The study advocates for improved UV protection strategies and informed health policies to mitigate climate change impacts and promote sustainable urban development. The findings suggest that the development of data-driven early warning systems for UV radiation exposure could be implemented to improve public health policy and safety.
Co-Authors Adi Suarman Situmorang, Adi Suarman Alatif, Yahya Halim Aldi Cahya Muhammad Ariefka, Reza Aunzo, Jr., Rodulfo T. Budiman Nasution Fadhilah, Dimas Imam Firdaus, Anas Noor Goldbert Harmuda Duva Sinaga Harahap, Veryyon Hassan, Rohana Isnan, M Abdurrahman Ridwan Jaya, Pratama Jubaidah, Jubaidah Karim, Mohammad Alfin Kasim, Mohd Shahir Laeiq, Nazish Lubis, Putri Rahmadani Lulut Alfaris Malun, Nicholaus Ola Muhlis, Dasep Nasution, Habibi Azka Nurahman, Arip Nyuswantoro, Ukta Indra Nyuswantoro, Ukta Indra Nyuswantoro Pandara, Dolfie Paulus Pribadi, Pandu Purwanto, Romdhon Rahdiana, Nana Rajagukguk, Michael Haratua Rancak, Gendewa Tunas Ratna Rintaningrum, Ratna Rionaldy, Rizqy Sahroni, Taufik Roni Sahroni, Taufik Roni, Mr. Siahaan, Kevin William Andri Silalahi, Christeven Simamora, Ursulla Duti Oktria Sinaga, Juli Antasari Br Sinaga, Mardame Pangihutan Suhara, Ade Wardana, Febriansyah Wibowo, Arnowo Hari Wibowo, Firdaus Andi Wibowo, Rizky Tri winsyahputra Ritonga Yasin, Verdi Yulianto, Tomi