Research Article
Learning Natural Frequency And Resonance Using Wasted Water Bottle
@INPROCEEDINGS{10.4108/eai.7-8-2019.2288413, author={Eko Nursulistiyo}, title={Learning Natural Frequency And Resonance Using Wasted Water Bottle }, proceedings={Proceedings of the 4th Progressive and Fun Education International Conference, Profunedu 2019, 6-8 August 2019, Makassar, Indonesia}, publisher={EAI}, proceedings_a={PROFUNEDU}, year={2019}, month={10}, keywords={natural frequency resonance water bottle}, doi={10.4108/eai.7-8-2019.2288413} }- Eko Nursulistiyo
Year: 2019
Learning Natural Frequency And Resonance Using Wasted Water Bottle
PROFUNEDU
EAI
DOI: 10.4108/eai.7-8-2019.2288413
Abstract
The water bottle can be used as learning media to teach physics. Finding its natural frequency and develop a procedure to show resonance phenomena are needed to use it in teaching and learning in the class. In this research, three bottles had been used. "Fresh tea" bottle has a natural frequency in 186.3±0.6 Hz. While Syrup bottle has a natural frequency in 133.8±0.6 Hz and "Pristine" bottle has a natural frequency in 203.4±1.2 Hz. The step to perform a resonance phenomenon as follows: a). Open tone generator and click sweep generator, b). Change sweep to tone by click sweep button on the bottom left, c). Change the frequency by clicking the frequency in Hz and input the frequency, d). Play by clicking the bottom middle button, e). Move mobile phone speaker to the top of bottle mouth, f). Check the difference of sound volume at the top of the bottle and in other position. The relation between resonance frequency (y) and the square of one per length of the empty collum in the bottle (x) follow equation y = 2298 x - 310.2. R² is equal to 0.952, which shows that the equation produced in the graphic is strongly fit to the data. The relation between resonance frequency (y) and the square of one per length of the empty collum (x) in bottle y = 4317 x + 47.33 . R² is equal to 0.996, which shows that the relation the equation produced in the graphic is strongly fit to the data.