## 3 Materials and Methods  
### System Overview  
An overview of the system in this research is depicted in Figure 1. The entire developed system is divided into five components called modules. Each module encompasses several processes. These components include hardware, network, database, data analysis, and outputs. The initial stages of the research start from the hardware component and continue through the process to obtain the final results.  
Figure 1: System overview.  
In the hardware component, various electronic components are used to collect data. These electronic components include sensors, actuators, power supply, communication or network devices, and microcontrollers. These electronic components are connected to the microcontroller according to their respective I/O signals. The sensors carry out the process of reading environmental data values inside the incubator, and the microcontroller processes the acquired values. The microcontroller can use these data to send commands to the actuators or transmit the data to the database via the network.  
An IoT Broker is used and installed on a server in the network component. The data sent by the hardware will enter this IoT broker. Data communication in this system uses the publish/subscribe method, where each piece of data sent has a corresponding topic [23]. Additionally, each device has a unique ID or identity for identification purposes. WiFi is the communication medium during the data transmission from hardware to the IoT Broker.  
The data collected in the IoT broker are then processed for storage in the database. The data storage process is conducted in real-time from the broker to the database using a socket service created with Python. However, before the data are stored in the database, they are filtered to ensure no data loss or inappropriate data occurs during storage. Subsequently, the data stored in the database are used as a dataset in the analysis stage to create a model to predict energy consumption and temperature distribution patterns within the incubator.  
The dataset contains features or attributes such as temperature, humidity, and electric current values in the analysis stage. Data patterns in this dataset are recorded every second. Therefore, energy consumption is calculated for each second in the analysis process. Suppose you want to calculate the total energy consumption for a specific period. In that case, you can multiply the electric current data by the electrical voltage (220 V) with the desired duration in seconds. For example, if you want to find energy consumption per minute, the result can be multiplied by 60 s.  
The next step in the process is modeling, using deep learning methods to analyze the sequential data from the incubator. This analysis process combines two techniques, 1D-CNN and LSTM, to develop a predictive model for electric energy consumption [24]. Before that, a data preprocessing process is performed, which includes steps such as calculating the average values based on time and scaling. The purpose is to optimize and prepare the data to create an accurate and reliable predictive model.  
Furthermore, in the next phase, an analysis of the temperature distribution patterns within the incubator is conducted, aiding in the optimization of infant care. The outcome of this analysis will manifest as visualizations of the temperature distribution conditions within the incubator. Additionally, a web application has been developed to be accessed from anywhere via the Internet. This web application allows users to monitor and control the incubator's conditions in real-time.