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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Ilmu Fisika BULETIN FISIKA
Siregar, Syahril
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Astronomy Earth & Planetary Sciences Energy Materials Science & Nanotechnology Medicine & Pharmacology Physics Other

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KEMAMPUAN PEMECAHAN MASALAH DAN BERFIKIR KRITIS SISWA MELALUI MODEL PROBLEM BASED LEARNING DI SDN 106232 PENGGALANGAN Gurning, Parida Herianti; Hasratuddin; Puryati; Siregar, Syahril
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 03 (2025): Volume 10 No. 3 September 2025 In Order
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i03.29492

Abstract

This study was motivated by the low mathematics learning outcomes of fifth-grade students at SD Negeri 106232 Penggalangan, which are believed to stem from weak critical thinking and problem-solving skills. The purpose of this research was to evaluate the effectiveness of the Problem Based Learning (PBL) model in enhancing these two competencies. A quantitative approach was employed using a quasi-experimental method, involving two groups: an experimental class taught using the PBL model and a control class receiving conventional instruction. A total of 42 students were selected as samples through total sampling from both classes. The research instruments consisted of validated tests measuring critical thinking and problem-solving abilities. Data were analyzed using a two-way ANOVA test. The findings revealed a significant difference between the two groups, with students in the experimental class demonstrating higher levels of critical thinking and problem-solving skills. Furthermore, an interaction was found between the learning model and students’ initial mathematical ability in influencing learning outcomes. The study concludes that the Problem Based Learning model is effective in improving elementary school students’ critical thinking and problem-solving skills and can serve as an innovative instructional strategy
Impact of Training Dataset Size on the Accuracy of L-SVR Single-Time-Point Renal Dosimetry for [¹⁷⁷Lu]Lu-PSMA-617 Therapy Wicaksono, Abdurrahman Aziz; Jabar, Jaja Muhammad; Siregar, Syahril; Hardiansyah, Deni
BULETIN FISIKA Vol. 27 No. 1 (2026): BULETIN FISIKA
Publisher : Departement of Physics Faculty of Mathematics and Natural Sciences, and Institute of Research and Community Services Udayana University, Kampus Bukit Jimbaran Badung Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/BF.2026.v27.i01.p07

Abstract

Radiopharmaceutical therapy (RPT) using [¹⁷⁷Lu]Lu-PSMA-617 requires accurate dosimetry to evaluate organs-at-risk (OAR), specifically the kidneys. Single-time-point (STP) dosimetry simplifies clinical workflows by reducing SPECT/CT acquisition. Machine learning (ML) offers a potential solution, yet clinical implementation is hindered by the scarcity of sufficient training datasets for ML-based studies. This study investigated the relationship between training dataset size and time-integrated activity (TIA) estimation accuracy. A Linear Support Vector Regression (L-SVR) model was trained on synthetic virtual patients (VPs, 5,000 total) simulated from a published PBMS NLMEM renal biokinetics at five imaging times (t=1.8 h, 18.7 h, 42.6 h, 66.2 h, and 160.3 h). Time-activity-curve (TAC) and reference TIA (rTIA) were calculated for each VP. Random sampling was performed in increasing dataset sizes. Sample sizes were sub-sampled to training (80%) and testing (20%) datasets. L-SVR was trained on STP data at 42.6 h post-injection (best-time-point of PBMS NLMEM study) from the training dataset and tested by generating estimated TIA (eTIA) with input from the testing dataset. Performance was evaluated by calculating root-mean-square-error (RMSE) and mean-absolute-percentage-error (MAPE) of the eTIA to rTIA. Results showed that the accuracy of eTIA from ML STP dosimetry depends on training size: small samples (n=10) yielded poor performance (RMSE>85.98%, MAPE>89.1%). Accuracy improved significantly at n=500 (RMSE=14.07%) and plateaued beyond n=1,000 (peak RMSE=13.07%). Results indicate that the L-SVR model of the study requires sample sizes of n>200, with optimal gains up to n=2,000. This study suggests synthetic data as a methodological bridge between limited clinical datasets and data-intensive ML approaches.
Hierarchical Tissue-Based MRI Features with Explainable Machine Learning for Alzheimer’s Disease Classification Ceesay, Muhammed B; Saputro, Adhi Harmoko; Siregar, Syahril
Jurnal Ilmu Fisika Vol 18 No 1 (2026): March 2026
Publisher : Jurusan Fisika FMIPA Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jif.18.1.93-104.2026

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by multiscale structural brain degeneration. Many MRI-based machine learning approaches rely on coarse volumetric measures or black-box models with limited anatomical interpretability. This study aims to localize anatomically meaningful brain regions that discriminate AD from cognitively normal (CN) subjects using a hierarchical tissue-based (HTB) MRI framework. The method models gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) volumetric changes at lobar, gyral, and 246 fine-grained subregions defined by the Brainnetome atlas. T1-weighted MRI scans from 454 participants (227 AD, 227 CN) obtained from ADNI and MIRIAD were preprocessed using AC-PC alignment, N4 bias correction, skull stripping, and nonlinear registration to MNI space. A total of 561 HTB features were extracted to train Random Forest and XGBoost classifiers using five-fold stratified cross-validation with Bayesian hyperparameter optimization. The XGBoost model achieved the best performance (Accuracy: 79.74%, ROC-AUC: 85.07%), comparable to recent atlas-based MRI classification studies, while providing improved multiscale anatomical interpretability. SHAP analysis revealed consistent hierarchical atrophy patterns in hippocampal subregions, medial amygdala, and areas 35/36 and 28/34, demonstrating that hierarchical anatomical modeling with explainable machine learning enables transparent localization of clinically meaningful AD biomarkers without reliance on black-box architectures.
Co-Authors Adhi Harmoko Saputro Ceesay, Muhammed B Gurning, Parida Herianti Hardiansyah, Deni Hasratuddin Jabar, Jaja Muhammad Puryati Wicaksono, Abdurrahman Aziz