Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal Journal of Mathematical and Fundamental Sciences Spektra: Jurnal Fisika dan Aplikasinya
Jaelani, Anton Timur
Unknown Affiliation
Aerospace Engineering Astronomy Chemistry Earth & Planetary Sciences Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mathematics Physics

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Comparative Study of Activation Functions and Image Resolution on ResNet-34 for Spiral Galaxy Spin Classification Arwinata, Hafiz Indra; Kusuma, Sultan Hadi; Jaelani, Anton Timur
Spektra: Jurnal Fisika dan Aplikasinya Vol. 10 No. 3 (2025): SPEKTRA: Jurnal Fisika dan Aplikasinya, Volume 10 Issue 3, December 2025
Publisher : Program Studi Fisika Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/SPEKTRA.103.03

Abstract

This study investigates the application of the Residual Network (ResNet-34) architecture for classifying spiral galaxy spin directions, specifically focusing on the comparative performance of activation functions and cross-dataset generalizability using data derived from the Dark Energy Spectroscopic Instrument Legacy Survey (DESI LS) and the Hyper Suprime Cam Subaru Strategic Program (HSC-SSP) surveys. The methodology ensures robustness by training each model configuration across 10 independent runs. The results demonstrate the clear superiority of the Rectified Linear Unit (ReLU) over the Hyperbolic Tangent (Tanh); ReLU-based models achieved a mean peak accuracy of 94.7% and required only less than 60 epochs to converge, significantly faster than Tanh's 120 epochs. Crucially, we found that models trained on lower-resolution DESI LS images exhibited superior robustness and generalizability compared to high-resolution-trained models, suggesting that low-resolution training acts as effective implicit regularization. This research provides critical design recommendations for efficient machine learning pipelines, particularly for upcoming facilities like the 3.8-meter telescope at Timau National Observatory (TNO), ensuring model stability and transferability across diverse survey conditions.
Gravitational Lens Parameters Estimation at Intermediate Redshifts Using Convolutional Neural Networks Setiawan, Muhammad Doni; Jaelani, Anton Timur
Spektra: Jurnal Fisika dan Aplikasinya Vol. 10 No. 3 (2025): SPEKTRA: Jurnal Fisika dan Aplikasinya, Volume 10 Issue 3, December 2025
Publisher : Program Studi Fisika Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/SPEKTRA.103.05

Abstract

Strong gravitational lensing serves as a powerful astrophysical probe, enabling studies of dark matter, galaxy structure, and cosmological parameters. The number of strong gravitational lensing candidates at the galaxy scale is expected to reach O ~ 5 with ongoing and future wide-field galaxy surveys. Current modeling techniques largely rely on conventional fitting methods, such as least squares or maximum likelihood using Markov Chain Monte Carlo, which despite their effectiveness, are computationally expensive and require manual inspection. This motivates the development of faster yet accurate parameter estimation techniques. In this work, we construct a representative training dataset and develop an efficient Convolutional Neural Network to estimate lens parameters: the Einstein radius, axis ratio, and position angle. We utilize data from Public Data Release 3 of the Hyper Suprime-Cam Subaru Strategic Program, selecting lens galaxies in the range 0.3 ≤ z ≤ 0.9 based on the strong-lens probability distribution. We find that the choice of loss function and regularization strategy is critical. To enhance model generalization, we leverage SpatialDropout, which outperforms standard methods by addressing the spatial correlation inherent in convolutional features. Furthermore, prediction accuracy and convergence speed are strongly affected by the distribution of the training data, highlighting the importance of an appropriate loss function. Our optimized model demonstrates robust performance, achieving a Mean Absolute Error of 0.092 arcsec for the Einstein radius, providing a scalable framework for automated analysis in future wide-field surveys.
Variability Survey of Southern Open Clusters NGC 6134, NGC 6416, Collinder 394, and Alessi 10 Mandey, Denny; Ramadhan, Dhimaz Gilang; Yusuf, Muhammad; Perhati, Teduh; Ramadhan, Sahlan; Pramono, Theofilus Hobba; Anin, Chornelis J’Be; Premadi, Premana Wardayanti; Jaelani, Anton Timur; Jatmiko, Agus Triono Puri; Arifyanto, Mochamad Ikbal
Journal of Mathematical and Fundamental Sciences Vol. 57 No. 3 (2026): (In Progress)
Publisher : Directorate for Research and Community Services (LPPM) ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.math.fund.sci.2026.57.3.1

Abstract

We present the results of a brightness variability survey of four southern open clusters, namely NGC 6134, NGC 6416, Collinder 394, and Alessi 10, which were observed using three telescopes following a time series photometric scheme during the dry season in 2022. The purpose of these observations was to detect variability events and their types of variability in star members of these four clusters. Data analysis was performed using a differential photometry method to produce light curves. Several new variable star candidates were discovered in the observed clusters. More observational data are needed to examine the consistency of the variabilities, estimate relevant periodicity, and determine the type of variable star.
Pre-selection of Globular Clusters for Detecting Intermediate-Mass Black Holes via Microlensing Zahra, Fatimah; Jaelani, Anton Timur
Spektra: Jurnal Fisika dan Aplikasinya Vol. 11 No. 1 (2026): SPEKTRA: Jurnal Fisika dan Aplikasinya, Volume 11 Issue 1, April 2026
Publisher : Program Studi Fisika Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/SPEKTRA.111.02

Abstract

Intermediate-mass black holes (IMBHs) are the key to understanding the formation of supermassive black holes (SMBHs) that are typically found in centers of massive galaxies. In general definition, IMBHs are black holes masses ranging from 102 M⨀ to 105 M⨀. Black hole seeding theory suggests that IMBHs formed at high redshifts (z ≥ 0) through gravitational collapse of Population III stars, direct collapse of protogalaxy clouds, or stellar collisions inside high-density clusters. These IMBHs evolve into SMBHs through accretion and mergers. However, not all of these IMBHs are successful in forming SMBHs. Those that fail are hypothesized to be observable in the local universe, particularly in globular clusters and dwarf galaxies. IMBHs have yet to be definitively detected, however several strong candidates have been located in the cores of globular clusters. This study describes the preliminary selection stage of globular cluster candidates with the aim to look further into the possibility of detecting IMBHs via microlensing. The selection process was based on several physical properties of the globular clusters including mass, metallicity, and central velocity dispersion. Since background star density strongly affects microlensing detection probability, we restricted the sample to clusters located in the direction of the Galactic center. We utilized Gaia DR3 data to estimate background star density for selected globular clusters using proper motion filtering and Theil-Sen regression method. These estimates provide the foundation for further subsequent simulations of microlensing detection probabilities associated with central IMBHs.
Co-Authors Anin, Chornelis J’Be Arifyanto, Mochamad Ikbal Arwinata, Hafiz Indra Fatimah Zahra Jatmiko, Agus Triono Puri Kusuma, Sultan Hadi Mandey, Denny Muhammad Yusuf Perhati, Teduh Pramono, Theofilus Hobba Premadi, Premana Wardayanti Ramadhan, Dhimaz Gilang Ramadhan, Sahlan Setiawan, Muhammad Doni