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Data collection using thermocouple sensors in low-cost data acquisition is prone to noise interference, which could reduce the data quality. Noise sources such as cold junction compensators, electromagnetic interference, and Johnson noise can significantly affect the reliability and accuracy of conventional measurements. This study aims to improve the quality of thermocouple sensor readings on low-cost data acquisition using calibration method based on deep learning and the denoising process using a wavelet transform. This taken approach successfully increase the accuracy value of 97.67% with a mean absolute error (MAE) of 0.2. The precision also increases of 262.7% as indicated by the result of signal-to-noise ratio (SNR) with a value of 105.29 dB. Comparative analysis was carried out against National Instruments® device and it was found that deep-wavelet method had a lower and higher of MAE and SNRdB values of 16.67% and 0.8% respectively. This study shows that the denoising-calibration method with deep-wavelet can improve the accuracy and reliability of data from low-cost thermocouple devices. [ABSTRACT FROM AUTHOR]

The design of the sterilization box is based on the ATmega2560 microcontroller which utilizes the concept of 254 nanometer UV-C shortwave radiation and disinfectant spray to increase the effectiveness of the sterilization box. In order to prevent transmission through touch from users through physical contact via sterilization box, researchers designed a system that can make users access sterilization box without the need to make physical contact with the device, namely by using IoT system with the help of applications that can be accessed through the user's smart device. Based on the results of the analysis and experiment that has been carried out, it is concluded that the sterilization box in this study can work optimally at a distance of less than 700 cm. This tool can operate using smartphone media via bluetooth HC-05 module, so it can minimize the process of spreading Covid-19 that occurs through physical contact.