<![CDATA[Delays in establishing a diagnosis of pneumonia in toddlers can increase toddler morbidity and mortality. Early pneumonia detection tools are very necessary to be able to provide appropriate intervention. The objective is to develop an early-detection tool for pneumonia in toddlers using the ARI Programme Respiratory Rate Time based on the Internet of Things by calculating respiratory frequency and oxygen saturation. The research methods use Research and Development (R&D) was carried out on toddlers with cough complaints in the working area of the Grogol Health Center, Sukoharjo Regency from December 2023 to January 2024. The sample consisted of 100 toddlers who were selected using consecutive sampling. The dependent variables are respiratory frequency and oxygen saturation. The independent variables are ARI based on IoT, ARI Timer, and pulse oximeter. Other data is collected through observation sheets. Data were analyzed using independent t-test analysis using the SPSS version 26. The results of the validation test assessment by 6 experts obtained a total average score of 95.48% with very valid assessment criteria, which means the tool is suitable for use. The results of the Independent T-Test show that there is no difference between IoT-based ARI (Mean=39.28; SD=9.05) and Timer ARI (Mean±SD= 39.29±9.07), this result is not statistically significant (p=0.994) and does not exist. the difference between IoT-based ARI (Mean±SD= 94.90±2.55) and pulse oximeter (Mean±SD= 95.15±2.61), this result is not statistically significant (p=0.494). The conclusion is an ARI Programme Respiratory Rate Timer based on the Internet of Things tool can be developed for the early detection of pneumonia in toddlers and is suitable for use to determine the respiratory frequency and oxygen saturation in toddlers.]]>
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