<![CDATA[A home is a place to return to, rest, and take shelter. Most people spend a large portion of their time at home. The amount of time we spend in our homes makes it important for us to pay attention to the health conditions of our home. A home that is not well-maintained can become a breeding ground for diseases. The solution that I propose is to create a system that can determine a home's health score and also measure the criteria in each room. The system that I have created uses a Artificial Neural Network algorithm and an ESP-32 microcontroller as the processor unit. The system will take in features captured by each sensor, including light intensity (BH1750), air temperature (DHT11), air humidity (DHT11), and carbon monoxide levels in the air (MQ-7). The results of the algorithm and measurements will be displayed on a 20x4 LCD screen, showing the measured features, the obtained score, and the confidence of the classification. The training data used in this system consists of a total of 345 data from 4 classes, with each class consisting of approximately 80 data. The testing was carried out in rooms in a volunteer's home, with a total of 20 rooms. The results of the testing show that 18 were correct and 2 were incorrect, resulting in a system accuracy of 90% with an average computation time of 0.148 seconds.]]>
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