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The Effect of the STEM Learning Model on Student’s Critical Thinking in Indonesia: Meta-Analysis Baso Intang Sappaile; Abdul Rahman; Ilwandri Ilwandri; Tomi Apra Santosa; Ichsan Ichsan; Jedithjah Naapia Tamedi Papia
Edumaspul: Jurnal Pendidikan Vol 7 No 1 (2023): Edumaspul: Jurnal Pendidikan
Publisher : Universitas Muhammadiyah Enrekang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33487/edumaspul.v7i1.6129

Abstract

This study aims to determine the effect of STEM learning models on students' critical thinking skills in Indonesia. This type of research is a meta-analysis. Data sources came from 13 national and international journals. Data sources were searched from Google Scholar, ScienceDirect, ProQuest, Eric Journal, and Springer. The process of selecting data sources was carried out systematically and thoroughly. The collection technique was direct observation. Inclusion criteria in the study are 1) Data sources come from journals and proceedings indexed by SINTA, Scopus, and WOS; 2) The type of research is experimental or quasi-experimental; 3) Research related to the STEM model on critical thinking skills; 4) Journal publications 2015-2023; 5) Research has data sources that can be calculated effect size (ES) values. The results showed the average effect size (ES) value of 0.968 high criteria. This finding shows that the STEM model has a very large influence on students' critical thinking skills. The STEM model helps students be more creative and innovative in the learning process
Multi-Stage Computer Vision Framework with Ensemble Learning for Real-Time Glass Packaging Defect Detection in Industrial Applications Jonah Alfred Mekel; Rick Resa Wahani; Motulo, Firmansyah Reskal; Alfred Noufie Mekel; Tineke Saroinsong; Tammy Tinny V. Pangow; Jerry Heisye Purnama; Jedithjah Naapia Tamedi Papia
Frontier Advances in Applied Science and Engineering Vol. 3 No. 2 (2025)
Publisher : Tinta Emas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/faase.v3i2.572

Abstract

Transparent glass packaging inspection presents significant challenges for automated quality control systems due to optical complexities including reflections, refractions, and low-contrast defect patterns. This research develops a comprehensive multi-stage computer vision framework integrating specialized algorithmic modules with ensemble machine learning for real-time defect detection in industrial glass packaging lines. The framework implements four specialized detection stages: (1) meniscus-corrected liquid level measurement using dual-camera validation and polynomial surface fitting, (2) seal integrity assessment through Circular Hough Transform combined with geometric, texture, and color feature extraction, (3) lid positioning evaluation via calibrated geometric centroid analysis with tolerance-based classification, and (4) multi-method contamination detection integrating color aberration analysis, histogram-based particle detection, and morphological operations. The system employs an ensemble classification architecture combining modified MobileNetV2 convolutional neural network with Random Forest classifier, optimized for edge computing deployment. Industrial validation at PT AQUWAR Bintang Semesta demonstrated 91.6% overall detection accuracy with 347 milliseconds average processing time per container across 2,847 test samples spanning multiple defect categories. The modular framework architecture enables independent optimization of detection stages while maintaining real-time processing capabilities, providing a robust foundation for transparent packaging quality control in high-volume manufacturing environments.