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Kota mataram,
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INDONESIA
Jurnal Pendidikan Fisika dan Teknologi
Published by Universitas Mataram
ISSN : 26145618     EISSN : 24076902     DOI : -
Core Subject : Science, Education,
Education Physics
Jurnal Pendidikan Fisika dan Teknologi (JPFT) merupakan wadah publikasi ilmiah bagi dosen, guru, mahasiswa, dan peneliti bidang fisika dan pembelajarannya, termasuk teknologi terapan dan teknologi pembelajaran yang sesuai. Terbit perdana pada tahun 2015 dan mulai tahun 2017 JPFT terbit 2 kali dalam setahun yaitu pada bulan Juni dan Desember.
Arjuna Subject : -
Articles 492 Documents
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Designing Sustainable City: Feasibility of Gamification-Integrated STEM-Based Science Learning Tools Nur'aini, Anugrah Putri; Efwinda, Shelly; Zahidsyahtya, Muhammad Amiq; Sari, Erna; Nuryadin, Atin; Sulaeman, Nurul Fitriyah
Jurnal Pendidikan Fisika dan Teknologi (JPFT) Vol 12 No 1 (2026): January-June (In Press)
Publisher : Department of Physics Education, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpft.v12i1.11296

Abstract

– This study aims to develop and validate STEM-based learning tools integrated with Game-Based Learning themed "Sustainable City" to address the lack of student engagement in STEM education. Employing the Educational Design Research (EDR) method, this research focuses on the design and construction phase involving the development of student worksheets and teaching modules. The product feasibility was assessed by five experts through content and graphic validation using a 46-item instrument with a 4-point Likert scale. Data were analyzed using percentage analysis and Aiken’s V index. The results demonstrate that the content feasibility achieved an average score of 93% (Very Valid) with an Aiken’s V of 0.90, highlighting strong validity in integrating the EDP and SDGs. The graphic feasibility obtained an average score of 87% (Very Valid). However, the specific finding revealed a critical gap in the "Cover Design" indicator, which scored the lowest (75%). Overall, the STEM-EDP materials are declared academically feasible by experts, although they require graphic improvements. However, the effectiveness of these tools has not been practically tested in the field. Therefore, further development must integrate academic validity with classroom trials to ensure its positive impact on student motivation. 
Impact of SMOTE Oversampling on Classifying Band Gap Types in Imbalanced ABO₃ Perovskite Oxides Maharani, Desvita; Ramadhani, Johana Oktavia; Rizqi, Aliyah Zahratu; Akrom, Muhamad
Jurnal Pendidikan Fisika dan Teknologi (JPFT) Vol 12 No 1 (2026): January-June (In Press)
Publisher : Department of Physics Education, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpft.v12i1.11479

Abstract

This study investigates the impact of the Synthetic Minority Over-sampling Technique (SMOTE) on the classification of direct and indirect band gap types in imbalanced ABO₃ perovskite oxide datasets. In the dataset used, the direct band gap class constitutes approximately 84% of the samples, while the indirect class represents only 16%, leading conventional classification models to become biased toward the majority class. To address this issue, SMOTE was employed to balance the class distribution, and its performance was evaluated using several machine learning algorithms, including Multi-Layer Perceptron (MLP), Extra Trees, CatBoost, and Gradient Boosting. Model performance was assessed using 5-fold stratified cross-validation, with particular emphasis on F1-macro and recall metrics to ensure adequate evaluation of the minority class. The results show that although SMOTE did not significantly improve overall accuracy (baseline: 0.89; SMOTE: 0.88), it enhanced the models’ ability to recognize the minority class. Notable improvements in F1-macro were observed, increasing from 0.76 to 0.78 for MLP and from 0.75 to 0.78 for CatBoost. These findings highlight the importance of using F1-macro as a more informative evaluation metric than accuracy for imbalanced datasets and provide methodological insights for developing more robust predictive models in materials informatics.

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