Development of an Internet of Things (IoT) Based Grating Diffraction Experimental Device with Real-Time Light Intensity Data Acquisition
DOI:
https://doi.org/10.23917/saintek.v2i2.16343Keywords:
diffraction grating, Internet of Things, light sensor, microcontroller, data acquisition system, optical sensor, NodeMCU ESP8266, optics experimentAbstract
This study aims to develop an Internet of Things (IoT)-based diffraction grating experimental apparatus and to examine its feasibility and performance in physics laboratory activities. The research employs the Research and Development (R&D) method with the ADDIE development model, which includes the stages of analysis, design, development, implementation, and evaluation. The experimental apparatus developed consists of a laser diode light source, a diffraction grating, an LDR light sensor, a stepper motor as an automatic scanning system, and a NodeMCU ESP8266 microcontroller that functions as an IoT-based control and data acquisition system. The validation of the apparatus was conducted by subject-matter experts and media experts using an assessment instrument based on a Likert scale. The validation results indicate that the experimental apparatus falls into the very feasible category for use in physics laboratory activities. The performance testing of the apparatus shows that the system is capable of detecting the distribution of light intensity and the positions of diffraction maxima consistently. The calculation of the wavelength based on experimental data produces values in the range of 647–652 nm, which are close to the theoretical value of the light source of 650 nm with a low level of relative error. In addition, the use of an IoT-based system allows the data acquisition process to be conducted automatically and visualized in real time, thereby improving the efficiency and objectivity of measurements. The results of this study indicate that the developed experimental apparatus has the potential to support the modernization of physics laboratories and improve the quality of digital data based laboratory learning.
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