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During this process of monitoring the contents (volume) of the patient's urinary catheter manually, the nurse must go around one by one to the patient's inpatient room to ensure the contents (volume) of the patient's urinary catheter. This process takes quite a long time while patients who use urinary catheters at the hospital are not small. In addition, if the patient's urinary catheter monitoring process is still done manually, sometimes the medical team or nurses often miss or forget, that the urine bag (catheter) is full. The purpose of this study is to design an Internet Of Things Based Urine Bag Monitoring. The research method used in this study was experimental. The population in this study was 65 TREM study program students with a sample of 40 respondents. Based on the average score table of the Simplicity, Interactivity and Usability surveys. The results of the score calculation using the Likert scale from the Simplicity, Interactivity and Usability factors of urine bag monitoring testing that has been carried out using questionnaires to respondents have shown a high score that is almost close to the value of 5 based on calculations carried out using the Likert scale which indicates that this urine bag monitoring tool can be well received by respondents. It is expected to be developed as material for further research related to monitoring urine bags using applications that can usealarms on users' smartphones and can also use batteries on urine bag monitoring tools

Traditional markets are characterized by fast-paced and diverse transactions, necessitating a reliable product monitoring system to enhance the efficiency of stock management and transactions. This study develops a monitoring system based on a loadcell sensor and a TCS230 color sensor to automatically classify product weight and type. The loadcell is used to measure product weight with high accuracy, while the TCS230 detects the color characteristics of products to distinguish between different types of commodities, such as various varieties of chili peppers. The development process includes sensor calibration, dataset collection, and the training and evaluation of a classification model. Experimental results show that the classification accuracy exceeds 90%, demonstrating the effectiveness of combining weight and color data for market product recognition.