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Prof. Dr. Muhayatun Santoso
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Atom Indonesia
Published by Badan Riset dan Inovasi Nasional
ISSN : 01261568     EISSN : 23565322     DOI : -
Core Subject : Science,
Energy Physics
Atom Indonesia is dedicated to publishing and disseminating the results of research and development in nuclear science and technology. The scope of this journal covers experimental and analytical research in nuclear science and technology. The topics include nuclear physics, reactor physics, radioactive waste, fuel element, radioisotopes, radiopharmacy, radiation, and neutron scattering, as well as their utilization in agriculture, industry, health, environment, energy, material science and technology, and related fields.
Arjuna Subject : Fisika dan Astronomi - Fisika Nuklir dan Molekuler, dan Ooptik
Articles 107 Documents
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Calibration Model Development and Performance Evaluation of Portable Multichannel Analyzer for In-Situ Nuclear Forensics M S. F. Husein; S Sihana; R. M. Subekti; K. Indriana; E. Noerpitasari; S Susanto; L. N. Thanh
Atom Indonesia Vol 52, No 2 (2026): AUGUST 2026
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/aij.2026.1584

Abstract

The rapid response to nuclear security incidents necessitates precise, field-deployable instrumentation capable of quantifying nuclear material. This study evaluated the Portable Multichannel Analyzer (PMCA) equipped with a NaI(Tl) detector as a first-responding tool for nuclear forensics. Tertiary standards of natural and depleted uranium (0.5–30 g) were measured under varying acquisition times (60–1200 s) to establish calibration curves and determine performance metrics. Key performance indicators, including Relative Standard Deviation (RSD), uncertainty, and detection limits, were compared across linear and polynomial models. The PMCA demonstrated high precision with RSDs 3 g. Polynomial calibration curves achieved superior accuracy (R² > 0.99), significantly reducing uncertainty compared to linear models. Benchmark comparisons with HPGe spectrometry and destructive potentiometric titration validated PMCA measurements, showing
Cover Atom Indonesia Vol 52 No 2 atom indonesia
Atom Indonesia Vol 52, No 2 (2026): AUGUST 2026
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/aij.2026.2056

Abstract

Preface Atom Indonesia Vol 52 No 2 atom indonesia
Atom Indonesia Vol 52, No 2 (2026): AUGUST 2026
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/aij.2026.2057

Abstract

Core and/or No-Core Shell Model Calculations of the Nuclear Structure for the 6Li, 32S, 92Mo, and 90Zr Nuclei A. A. Mohaimeed; S. R. Sahib
Atom Indonesia Vol 52, No 2 (2026): AUGUST 2026
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/aij.2026.1644

Abstract

The present paper calculates the energy levels and electron scattering form factors of several medium and light nuclei. This calculation involves many components that need to be taken into account to ensure both feasibility and accuracy, including mathematics methods, quantum mechanical theory, and the theoretical framework and formulas of the nuclear shell model. In this study, the energy levels of 6Li nuclei were calculated using the no-core shell model with WBT, WBM, and WBP effective residual interactions. The core shell model was employed to calculate the energy levels for the 32S and 92Mo nuclei. Based on the no-core shell model energy level calculations, the theoretical elastic (C0) electron scattering form factors of 6Li were obtained, while the core shell model was used to calculate the longitudinal C2 electron scattering form factor for the 32S, 92Mo, and 90Zr nuclei using the Tassie Model (TM). Potentials of the SKX(Skyrm Interaction\ force) and HO (Harmonic Oscillator) were utilized calculate the radial single-particle matrix elements’ wave functions. The Shell model code NuShell-X@MSU was utilized in this present work. The energy level calculations of the 6Li nuclei with wbm and wbt interactions show good agreement with experimental data compared to the wbp interaction. The core shell model calculations for the energy levels of 32S and 92Mo nuclei show reasonable agreement with the experimental data. In addition, the longitudinal C2 inelastic electron scattering form factors of 92Mo, 90Zr, and 32S nuclei exhibit good agreement with experimental results.
Acknowledgement Atom Indonesia Vol 52 No 2 atom indonesia
Atom Indonesia Vol 52, No 2 (2026): AUGUST 2026
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/aij.2026.2058

Abstract

Application of Radiolabeled Technique in Indonesia: A Receptor Binding Assay (RBA) Approach for Analysing Paralytic Shellfish Poisoning (PSP) Toxins in Green Mussels from Lampung Bay U. Sugiharto; E. Rohaeti; H. Purwaningsih; A. A. Lubis; T. R. D. Larasati; D. Shintianata; F. P. Andini; S. S. Meiliastri; M Muawanah
Atom Indonesia Vol 52, No 2 (2026): AUGUST 2026
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/aij.2026.1710

Abstract

The Receptor Binding Assay (RBA), using radiolabeled [³H]-saxitoxin as a tracer, was applied to detect Paralytic Shellfish Poisoning (PSP) toxins in green mussels (Perna viridis) from Lampung Bay, Indonesia. Sampling was conducted during Harmful Algal Bloom (HAB) events in 2012, 2014, and 2015, which coincided with mass fish mortality linked to red tide phenomena. The RBA achieved a markedly lower detection limit (0.2 µg STX eq. /100 g) than the conventional Mouse Bio-Assay (MBA) of 40 µg STX eq. /100 g and offered greater sensitivity, specificity, and faster analysis without ethical constraints. Environmental parameters such as nutrients, water quality, and plankton community composition were also monitored to elucidate ecological drivers of toxin production. The findings represented that RBA was a reliable and efficient tool for PSP toxin monitoring, providing early-warning capability for HAB-related risks in tropical marine ecosystems.
Dose Distribution Prediction by Machine Learning (Random Forest): Impact of Data Specificity in Cervical Cancer IMRT Y. A. Lestari; A. Munandar; W. E. Wibowo; B. B. Patrianesha; P. Prajitno; D. S. K. Sihono
Atom Indonesia Vol 52, No 2 (2026): AUGUST 2026
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/aij.2026.1630

Abstract

Creating optimal radiotherapy plans is time-consuming and relies heavily on expert judgment to balance target coverage and Organ at Risk (OAR) protection. This study aims to fill the gap in existing radiotherapy approaches by integrating the Random Forest (RF) to predict dose determination in cervical cancer using Intensity Modulated Radiotherapy (IMRT). A retrospective analysis was conducted using 173 randomly selected cases and 102 specific data (stage I to IIIC1r cervical cancer, without prior surgery, and Whole Pelvic Non-Extended Field). Predictions were based on geometric relationships between organs and absorbed doses, with the model trained using decision trees and hyperparameter tuning via Random Search (RS). Model performance was evaluated using Mean Squared Error (MSE), Nash Sutcliffe Efficiency (NSE), and P-values. The ML-RF model showed improved performance with specific data, achieving the largest MSE reduction in bladder Dmax (0.017 Gy to 0.007 Gy). All parameters showed P-values above 0.05, indicating no significant differences in mean values between the predicted and clinical data. However, NSE values varied across parameters, with good performance for Right Femoral Dmax (NSE = 0.530) and Left Femoral Dmax (NSE = 0.554), while lower agreement was observed for PTV CI (NSE = –0.167) and PTV HI (NSE = –0.137), suggesting challenges in capturing distribution patterns. These results demonstrate the model’s ability to closely predict clinical dose distributions and underscore the value of incorporating specific clinical criteria. This approach may help reduce clinician workload and support planning standardization, while maintaining the importance of expert clinical judgment. Future studies should explore larger datasets, refined inclusion criteria, diverse treatment approaches, and beam weighting prediction to further improve model accuracy.

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