Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer dan Informasi International Journal of Educational Research Excellence (IJERE)
Wibowo, Firdaus Andi
Unknown Affiliation
Arts Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
Enhancing Operational Team Capacity for Gen-Z Retention in a Digital Transformation Context Wibowo, Firdaus Andi; Yulianto, Tomi
International Journal of Educational Research Excellence Vol. 5 No. 1 (2026): January-June
Publisher : PT Inovasi Pratama Internasional

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

Abstract

The rapid adoption of digital transformation necessitates a strategic approach to Human Capital Management (HCM). Organizations must align workforce competencies with transformation goals to retain and develop Gen-Z talent, a digitally adept and innovation-driven demographic. This study employs the HC Framework-8 elements to analyze organizational structure, identify skill gaps, and implement strategies that enhance Gen-Z retention in the digital transformation era. The findings suggest that competency-based planning, targeted learning programs, and flexible engagement strategies are crucial in sustaining a digitally ready workforce.
Co-Authors Malun, Nicholaus Ola Rionaldy, Rizqy Siagian, Ruben Cornelius Yasin, Verdi Yulianto, Tomi