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All Journal Journal of Physics: Theories and Applications Jurnal Pendidikan Fisika
Sulistya, Eko
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Education Materials Science & Nanotechnology Physics

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IMPLEMENTATION OF THE RESONANCE METHOD TO MEASURE THE SPEED OF SOUND USING PVC PIPES AND SMARTPHONE Sulistya, Eko
Jurnal Pendidikan Fisika Vol 11, No 2 (2024): Jurnal Pendidikan Fisika
Publisher : Universitas negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpf.v11i2.22380

Abstract

The objective of this study is to replace the conventional approach that involved using water and vertically-held pipes in the measurement of sound speed. In this new method, the sound source is generated by an Android application called Tone Generator, utilizing a smartphone. The study investigates frequency variations in the range of 500 Hz to 1000 Hz with interval of 100 Hz. By graphing the length of the organ pipe against the harmonic number (n), where n is odd number, we observed a linear relationship. From this linear relationship, the speed of sound in the experimental setup was calculated. The speed of sound was determined to be 346 m/s with a standard deviation of 2 m/s, which is in close agreement with the reference speed of sound at 25 degrees Celsius. This innovative approach offers a convenient and accurate way to measure the speed of sound, making use of readily available smartphone applications and simplifying the experimental setup compared to traditional methods.
Predicting Newtonian cooling with machine learning: a comparative analysis of gradient boosting and random forest models Sulistya, Eko
Journal of Physics: Theories and Applications Vol 9, No 2 (2025): Journal of Physics: Theories and Applications
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jphystheor-appl.v9i2.105932

Abstract

This study investigates the use of artificial intelligence, specifically machine learning models, to predict temperature reduction in Newtonian cooling experiments involving varying volumes of water. Two regression models, Gradient Boosting Regression and Random Forest Regressor, were utilized to learn from empirical data. The findings indicate that both models are capable of accurately predicting cooling behavior, with the Random Forest model demonstrating superior accuracy for the dataset used. The machine learning models effectively represent the theoretical model of Newton’s Law of Cooling, which is characterized by an exponential decay curve. Furthermore, the cooling constant for each volume was estimated using curve fitting techniques. This research underscores the potential of AI in modeling complex physical processes, particularly in real-world scenarios where the relationships between physical variables are intricate and challenging to express analytically. With sufficient data, AI can adeptly predict variable changes based on fluctuations in others. As technology continues to advance, AI is poised to assume an increasingly critical role in experimental and industrial applications involving complex physical systems. The novelty of this study lies in its comparative analysis to identify the optimal machine learning model—Gradient Boosting Regression or Random Forest Regressor—for accurately predicting Newtonian cooling behavior. Additionally, this research introduces an automated data acquisition approach using a datalogger, significantly enhancing precision and practicality compared to traditional manual methods involving a stopwatch and thermometer.
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