The Effect of Size of Deep Learning Models in Science Learning

Authors

  • Tomi Apra Santosa Akademi Teknik Adikarya

DOI:

https://doi.org/10.31004/riggs.v4i2.947

Keywords:

Deep Learning Model; Meta-analysis; Effect Size; Science Learning

Abstract

The study found out the influence of the deep learning model in science learning. This research is a type of quantitative research with a meta-analysis approach. The source of research data is 12 national journals indexed by SINTA, DOAJ or EBSCO. The eligibility criteria are that the research must be a quantitative method, the experimental method, the journal is published in 2022-2025, the research must be relevant, the participants come from elementary, junior high, high school and college students. Data analysis is quantitative analysis with the help of JASP applications.  The results of this study can be concluded that there is a significant influence of the deep learning model in science learning with an effect size value of 0.915 in the high effect size category. These findings explain that the deep learning model is effectively applied at the elementary school to university education levels.

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References

Abdullah, A., Wijayanti, A., Suryono, W., Ika, M., Sarnoto, A. Z., Hiola, S. F., Ruchiat, A., Sari, W. D., & Santosa, T. A. (2024). Qualitative Study : Comparison of Implementation of The Effectiveness of the Ethno-Religious-Based SAVI Model in Improving Problem-Solving Skills in PAI Learning. Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini, 8(5), 1245–1256. https://doi.org/10.31004/obsesi.v8i5.6192

Agrawal, A., & Choudhary, A. (2019). Deep materials informatics: Applications of deep learning in materials science. MRS Communications, 9(3), 779–792. https://doi.org/10.1557/mrc.2019.73

Ahmed, N. K., Atiya, A. F., El Gayar, N., & El-Shishiny, H. (2010). An empirical comparison of machine learning models for time series forecasting. Econometric Reviews, 29(5), 594–621. https://doi.org/10.1080/07474938.2010.481556

Ali, M., Nurhayati, R., Wantu, H. M., Amri, M., & Santosa, T. A. (2024). The Effectiveness of Jigsaw Model Based on Flipped Classroom to Improve Students ’ Critical Thinking Ability in Islamic Religious Education Learning. Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini, 8(5), 1069–1078. https://doi.org/10.31004/obsesi.v8i5.6190

Asnur, L., Jalinus, N., Faridah, A., Apra, T., Ambiyar, R. D., & Utami, F. (2024). Video-blogs ( Vlogs ) -based Project : A Meta Analysis. 14(5), 1553–1557.

Asrizal, A., Khairi, U., Yulkifli, Murtiani, M., & Mardian, V. (2022). Effect of Blended Learning Model (BLM) on Student Achievements: A Meta-Analysis. Jurnal Penelitian Pendidikan IPA, 8(5), 2451–2459. https://doi.org/10.29303/jppipa.v8i5.2260

Babu Vimala, B., Srinivasan, S., Mathivanan, S. K., Mahalakshmi, Jayagopal, P., & Dalu, G. T. (2023). Detection and classification of brain tumor using hybrid deep learning models. Scientific Reports, 13(1), 1–17. https://doi.org/10.1038/s41598-023-50505-6

Badawi et al. (2023). Integration of Blended Learning and Project-Based Learning (BPjBL) on Achievement of Students’ learning goals: A Meta-analysis study. Pegem Journal of Education and Instruction, 13(4). https://doi.org/10.47750/pegegog.13.04.32

Blier, L., & Ollivier, Y. (2018). The description length of deep learning models. Advances in Neural Information Processing Systems, 2018-December(NeurIPS), 2216–2226.

Borenstein, M., Hedges, L., & Rothstein, H. (2007). Introduction to Meta-Analysis. www.Meta-Analysis.com

Chandra, A., Tünnermann, L., Löfstedt, T., & Gratz, R. (2023). Transformer-based deep learning for predicting protein properties in the life sciences. ELife, 12, 1–25. https://doi.org/10.7554/eLife.82819

Dewanto, D., Wantu, H. M., Dwihapsari, Y., Santosa, T. A., & Agustina, I. (2023). Effectiveness of The Internet of Things (IoT)-Based Jigsaw Learning Model on Students’ Creative Thinking Skills: A- Meta-Analysis. Jurnal Penelitian Pendidikan IPA, 9(10), 912–920. https://doi.org/10.29303/jppipa.v9i10.4964

Edy Nurtamam, M., Apra Santosa, T., Aprilisia, S., Rahman, A., & Suharyat, Y. (2023). Meta-analysis: The Effectiveness of Iot-Based Flipped Learning to Improve Students’ Problem Solving Abilities. Jurnal Edumaspul, 7(1), 2023–1492.

Elfira, I., & Santosa, T. A. (2023). Literature Study : Utilization of the PjBL Model in Science Education to Improve Creativity and Critical Thinking Skills. Jurnal Penelitian Pendidikan IPA, 9(1), 133–143. https://doi.org/10.29303/jppipa.v9i1.2555

Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019). Deep learning: new computational modelling techniques for genomics. Nature Reviews Genetics, 20(7), 389–403. https://doi.org/10.1038/s41576-019-0122-6

Fintz, M., Osadchy, M., & Hertz, U. (2022). Using deep learning to predict human decisions and using cognitive models to explain deep learning models. Scientific Reports, 12(1), 1–12. https://doi.org/10.1038/s41598-022-08863-0

Fitri, A. D., & Asrizal. (2023). Development of Physics E-Module Integrated with PBL Model and Ethnoscience to Improve Students’ 21st Century Skills. Jurnal Penelitian Pendidikan IPA, 9(12), 10610–10618. https://doi.org/10.29303/jppipa.v9i12.5877

Goh, G. B., Hodas, N. O., & Vishnu, A. (2017). Deep learning for computational chemistry. Journal of Computational …. https://doi.org/10.1002/jcc.24764

Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2), 199–237. https://doi.org/10.1080/08993408.2015.1033142

Hariyadi, S., Santosa, T. A., & Sakti, B. P. (2023). Effectiveness of STEM-Based Mind Mapping Learning Model to Improve Students ’ Science Literacy in the Era of Revolution. Jurnal Penelitian Pendidikan IPA, 9(10), 791–799. https://doi.org/10.29303/jppipa.v9i10.5125

Hohman, F., Head, A., Caruana, R., DeLine, R., & Drucker, S. M. (2019). Gamut: A design probe to understand how data scientists understand machine learning models. Conference on Human Factors in Computing Systems - Proceedings, 1–13. https://doi.org/10.1145/3290605.3300809

Hussain, M., Azlan, M., Zainuri, A., Nuryazmin, Zulkifli, M., Rafeizah, Rahman, A., & Anesman. (2023). Effect of an Inquiry-Based Blended Learning Module on Electronics Technology Students’ Academic Achievement. Journal of Technical Education and Training, 15(2), 21–32. https://doi.org/10.30880/jtet.2023.15.02.003

Ichsan, I., Suharyat, Y., Santosa, T. A., & Satria, E. (2023). Effectiveness of STEM-Based Learning in Teaching 21 st Century Skills in Generation Z Student in Science Learning: A Meta-Analysis. Jurnal Penelitian Pendidikan IPA, 9(1), 150–166. https://doi.org/10.29303/jppipa.v9i1.2517

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415

Li, Z., Wei, X., Hassaballah, M., Li, Y., & Jiang, X. (2024). A deep learning model for steel surface defect detection. Complex and Intelligent Systems, 10(1), 885–897. https://doi.org/10.1007/s40747-023-01180-7

Luciana, O. (2022). Interelasi keterampilan bahasa Indonesia lisan dan tulisan bagi tenaga kerja asing di PT. XYZ Purwakarta. Jurnal Ilmiah Wahana Pendidikan, 8(September), 336–350. http://jurnal.peneliti.net/index.php/JIWP/article/view/2296%0Ahttps://jurnal.peneliti.net/index.php/JIWP/article/download/2296/1893

Markiano Solissa, E., Haetami, H., Via Yustita, V., Santosa, T. A., & Syafruddin, S. (2023). Effect Size Discovery Learning Model on Students Critical Thinking Skills. Edumaspul: Jurnal Pendidikan, 7(2), 2083–2093. https://doi.org/10.33487/edumaspul.v7i2.6507

Morgan, D., & Jacobs, R. (2020). Opportunities and Challenges for Machine Learning in Materials Science. Annual Review of Materials Research, 50, 71–103. https://doi.org/10.1146/annurev-matsci-070218-010015

Ningsih, W., Prayitno, B. A., & Santosa, S. (2023). The effectiveness of environment-oriented e-books based on problem-based learning for problem-solving skills. JPBI (Jurnal Pendidikan Biologi Indonesia), 9(3), 511–520. https://doi.org/10.22219/jpbi.v9i3.25603

Oktarina, K., Suhaimi, Santosa, T. A., Razak, A., Irdawati, Ahda, Y., Lufri, & Putri, D. H. (2021). Meta-Analysis: The Effectiveness of Using Blended Learning on Multiple Intelligences and Student Character Education During the Covid-19 Period. International Journal of Education and Curriculum Application, 4(3), 184–192. http://journal.ummat.ac.id/index.php/IJECA/article/view/5505

Rajput, D., Wang, W. J., & Chen, C. C. (2023). Evaluation of a decided sample size in machine learning applications. BMC Bioinformatics, 24(1), 1–17. https://doi.org/10.1186/s12859-023-05156-9

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1

Rudin, C., & Wagstaff, K. L. (2014). Machine learning for science and society. Machine Learning, 95(1), 1–9. https://doi.org/10.1007/s10994-013-5425-9

Santosa, T. A., Ali, M., Safar, M., Amri, M., Ruchiat, A., & Sjoraida, D. F. (2025). Inquiry-Based Learning and Critical Thinking Skills of Higher Education Students in the Era of Revolution 5 . 0 : A Meta-analysis. CUESTIONES DE FISIOTERAPIA, 54(3), 5156–5166.

Shahinfar, S., Meek, P., & Falzon, G. (2020). “How many images do I need?” Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring. Ecological Informatics, 57. https://doi.org/10.1016/j.ecoinf.2020.101085

Sharma, S., & Chaudhary, P. (2023). Machine learning and deep learning. Quantum Computing and Artificial Intelligence: Training Machine and Deep Learning Algorithms on Quantum Computers, 71–84. https://doi.org/10.1515/9783110791402-004

Shoaib, M., Shah, B., EI-Sappagh, S., Ali, A., Ullah, A., Alenezi, F., Gechev, T., Hussain, T., & Ali, F. (2023). An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in Plant Science, 14(March), 1–22. https://doi.org/10.3389/fpls.2023.1158933

Sullivan, E. (2022). Understanding from machine learning models. British Journal for the Philosophy of Science, 73(1), 109–133. https://doi.org/10.1093/bjps/axz035

Tamur, M., Juandi, D., & Kusumah, Y. S. (2020). The effectiveness of the application of mathematical software in indonesia; a meta-analysis study. International Journal of Instruction, 13(4), 867–884. https://doi.org/10.29333/iji.2020.13453a

Tamur, M., Subaryo, S., Ramda, A. H., Nurjaman, A., Fedi, S., & Hamu, A. (2021). the Effect of Jigsaw Type of Cooperative Learning on Critical Thinking Ability of Junior High School Students. Journal of Honai Math, 4(2), 173–182. https://doi.org/10.30862/jhm.v4i2.201

Uluk, E., Masruchiyah, N., Nurhayati, R., Agustina, I., Sari, W. D., Santosa, T. A., Widya, U., Klaten, D., & Yogyakarta, U. N. (2024). Effectiveness of Blended Learning Model Assisted By Scholoogy to Improve Language Skills of Early Childhood Education Teachers. Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini, 8(6), 1363–1374. https://doi.org/10.31004/obsesi.v8i6.6226

Utomo, W., Suryono, W., Santosa, T. A., & Agustina, I. (2023). The Effect of STEAM-Based Hybrid Based Learning Model on Students ’ Critical Thinking Skills. Jurnal Penelitian Pendidikan IPA, 9(9), 742–750. https://doi.org/10.29303/jppipa.v9i9.5147

Wantu, H. M., Muis, A., Zain, A., Hiola, S. F., Agustina, I., Santosa, T. A., Yastanti, U., & Nugraha, A. R. (2024). Effectiveness of Think-Pair-Share and STEM Models on Critical Thinking in Early Childhood Education. Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini, 8(5), 1320–1330. https://doi.org/10.31004/obsesi.v8i5.6202

Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viegas, F., & Wilson, J. (2020). The what-if tool: Interactive probing of machine learning models. IEEE Transactions on Visualization and Computer Graphics, 26(1), 56–65. https://doi.org/10.1109/TVCG.2019.2934619

Winiasri, L., Santosa, T. A., Yohandri, Y., Razak, A., Festiyed, F., & Zulyusri, Z. (2023). Ethno-Biology Learning Model Based on Design Thinking to Improve Students’ Critical Thinking Skills. Jurnal Penelitian Pendidikan IPA, 9(9), 7767–7774. https://doi.org/10.29303/jppipa.v9i9.4213

Youna Chatrine Bachtiar, Mohammad Edy Nurtamam, Tomi Apra Santosa, Unan Yasmaniar Oktiawati, & Abdul Rahman. (2023). the Effect of Problem Based Learning Model Based on React Approach on Students’ 21St Century Skills: Meta-Analysis. International Journal of Educational Review, Law And Social Sciences (IJERLAS), 3(5), 1576–1589. https://doi.org/10.54443/ijerlas.v3i5.1047

Yulianti, T. A. S. & S. (2020). INDENTIFIKASI FAMILI ZINGIBERACEAE DI KAWASAN HUTAN GUNUNG BUA KERINCI. Ekologia : Jurnal Ilmiah Ilmu Dasar Dan Lingkungan Hidup, 20(1), 40–44. https://journal.unpak.ac.id/index.php/ekologia

Zulkifli, Z., Satria, E., Supriyadi, A., & Santosa, T. A. (2022). Meta-analysis: The effectiveness of the integrated STEM technology pedagogical content knowledge learning model on the 21st century skills of high school students in the science department. Psychology, Evaluation, and Technology in Educational Research, 5(1), 32–42. https://doi.org/10.33292/petier.v5i1.144

Zulyusri, Z., Santosa, T. A., Festiyed, F., Yerimadesi, Y., Yohandri, Y., Razak, A., & Sofianora, A. (2023). Effectiveness of STEM Learning Based on Design Thinking in Improving Critical Thinking Skills in Science Learning: A Meta-Analysis. Jurnal Penelitian Pendidikan IPA, 9(6), 112–119. https://doi.org/10.29303/jppipa.v9i6.3709

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Published

15-06-2025

How to Cite

[1]
T. A. Santosa, “The Effect of Size of Deep Learning Models in Science Learning”, RIGGS, vol. 4, no. 2, pp. 3198–3205, Jun. 2025.

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