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Peningkatan Kompetensi Guru SMP di Indralaya Melalui Pelatihan Media Simulasi Elektronika Berbasis Tinkercad Khoirunnisa, Rini; Murniati; Kistiono; Amri, Iful
Jurnal Pendidikan dan Pengabdian Masyarakat Vol. 9 No. 1 (2026): Februari
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppm.v9i1.10926

Abstract

Tujuan dari kegiatan pengabdian kepada masyarakat ini adalah untuk meningkatkan kemampuan guru dalam pemahaman konsep elektronika dasar serta penggunaan media simulasi TinkerCad dalam pembelajaran IPA SMP di Indralaya. Metode pelaksanaan termasuk sosialisasi, pelatihan teori, kelas praktik simulasi rangkaian, pendampingan proyek sederhana, dan evaluasi melalui pretest dan posttest menggunakan instrumen pilihan ganda dengan 20 soal. Hasil menunjukkan peningkatan dari rata-rata pretest sebesar 37,5 menjadi 83,6 pada posttest. Pelatihan ini terbukti efektif dalam meningkatkan pemahaman guru karena nilai N-Gain rata-rata sebesar 0,71 termasuk kategori tinggi menurut Hake. Pelatihan ini disarankan untuk diterapkan secara berkelanjutan sebagai alternatif praktikum di sekolah karena peningkatan ini menunjukkan bahwa penggunaan TinkerCad dapat membantu guru mempelajari rangkaian listrik secara visual dan interaktif dan meningkatkan literasi teknologi dalam pembelajaran IPA.
A Computational Physics–Based Machine Learning Modelling of Multiphase Flow Dynamics for Crude Oil Percentage Prediction Using Water Cut and Sediment Indicators Pebralia, Jesi; Amri, Iful; Amanda, Dwi Rahmah; Kurniawan, Muhammad Aziz
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.18.1.80-92.2026

Abstract

Existing crude oil percentage prediction methods often rely on direct measurements and historical data, neglecting the coupled multiphase characteristics of oil–water–sediment systems, which limits predictive accuracy. This study develops a computational physics–based machine learning model integrating key multiphase production parameters, including water cut, basic sediment, and BS&W, using samples from PT. Pertamina Puspa Field Jambi. Data were split into two sets: one for model development and one for validation to prevent overfitting. Linear Regression, Support Vector Machine (SVM), and Random Forest algorithms were applied, with Linear Regression achieving the best performance. For the test dataset, the model yielded a Mean Absolute Error of 0.022168, a Mean Squared Error of 0.001227, and an accuracy of 0.99877, demonstrating precise capture of multiphase interactions. The proposed computational physics–based modelling framework provided improved predictive reliability and consistency. Correlation analyses indicated a coefficient of determination (R²) of 0.99 and a perfect negative correlation (r = −1) between BS&W and oil content, showing that higher BS&W corresponds to lower oil percentage. This framework offers improved predictive reliability and consistency for crude oil quality assessment.