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All Journal E-Dimas: Jurnal Pengabdian kepada Masyarakat Physics Communication
Elgana Septiana
Universitas PGRI Semarang
Education Social Sciences

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Pelatihan Pembuatan Bukti Karya Guru Sesuai Platform Merdeka Mengajar di SMA Muhammadiyah 1 Semarang Joko Siswanto; Aris Tri Jaka Harjanta; Ibnu Fatkhu Royana; Harto Nuroso; Mei Fita Asri Untari; Dwi Prasetiyawati Diyah Hariyanti; Elgana Septiana; Rizal Ashari
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 15, No 4 (2024): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v15i4.20540

Abstract

Era digital menuntut para pendidik untuk memiliki kemampuan beradaptasi dengan teknologi yang mendukung proses pembelajaran dan peningkatan kinerja. Salah satu upaya yang dilakukan adalah melalui Platform Merdeka Mengajar (PMM), sebuah inovasi dari Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi Republik Indonesia untuk mendukung implementasi kurikulum merdeka. Namun, hadirnya PMM tidak dapat digunakan secara maksimal oleh guru SMA Muhammadiyah 1 Semarang, karena belum mengerti dan memahami dalam pengoperasian dan pemanfaatannya terutama terkait dengan bukti karya. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk menyelesaikan permasalahan SMA Muhammadiyah 1 Semarang dalam hal membuat bukti karya pembelajaran sehingga mendukung peningkatan kinerja guru yang profesional. Hasil menunjukkan bahwa pelatihan ini mampu meningkatkan pemahaman PMM dan kemampuan guru dalam membuat bukti karya pembelajaran, yang ditunjukkan dengan semua guru (100%) menghasilkan modul ajar, bahan ajar, artikel ilmiah, dan praktik baik.
Comparison of Physics-Informed Neural Networks (PINNs) and Experimental Reality in Fluid Viscosity Dynamics Elgana Septiana; Joko Saefan; Joko Siswanto
Physics Communication Vol. 10 No. 1 (2026): February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/pc.v10i1.45531

Abstract

Determining fluid viscosity using the conventional falling-sphere method is frequently confronted with challenges related to experimental variability and measurement instrument limitations. As an alternative, Physics-Informed Neural Networks (PINNs) offer a computational approach capable of integrating physical laws into the neural network architecture. This study aims to evaluate the predictive accuracy of the PINNs model regarding the velocity of a falling sphere, as well as to compare it with the analytical solution of Stokes flow and experimental data. Data collection was conducted using a viscometer equipped with five infrared sensors, while the PINNs model was trained by balancing the experimental data loss and the physics loss derived from the equation of motion. The comparative results demonstrate that PINNs generally succeed in modeling the dynamics of the sphere's motion, convergently reaching terminal velocity. Nevertheless, two primary modeling limitations were identified. First, the model experiences an overshoot during the initial phase of motion due to the network's spectral bias effect when responding to drastic velocity changes. Second, the experimental data reveal a persistent velocity deceleration in the final phase that both the analytical and PINNs predictions failed to capture. This empirical anomaly indicates the occurrence of a shift in boundary-layer separation alongside the thixotropic effects of the fluid. In conclusion, PINNs prove to be a promising approach for bridging the gap between computational modeling and experimentation; however, this architecture still requires the inclusion of dynamic parameters to fully accommodate the complexity of fluids under real-world conditions.
Co-Authors Aris Tri Jaka Harjanta, Aris Tri Dwi Prasetiyawati Diyah Hariyanti Harto Nuroso Ibnu Fatkhu Royana Joko Saefan Joko Siswanto Mei Fita Asri Untari Rizal Ashari