## 4 Results and Discussion  
Figure 11 presents an overview of the hardware implementation used in the incubator system. Figure 11 displays the main incubator box, which includes temperature and humidity sensors on each side. Subsequently, in Figure 11, the implementation of the heater device is shown. This device generates heat and distributes it throughout the incubator using a blower fan. Finally, Figure 11 illustrates the hardware implementation for the electronic components that control the incubator. These electronic components include a microcontroller, current sensor, WiFi module, and power supply. Figure 11: Hardware prototype: (**a**) neonatal incubator box; (**b**) actuator heater and blower; (**c**) electronic components. Figure 10: Output design. Figure 12 shows the results of the responsive web application implementation, making it accessible on various screen sizes, including mobile phones. In the application, the main page is a dashboard, as seen in Figure 12a. This dashboard page displays data from the incubator in real-time as graphs. Figure 12b also indicates the stored data from the database in a tabular format. This page provides various features for users, including the ability to sort data, perform searches, and download data in Excel, CSV, PDF, and JSON formats. Based on the data stored in the database, which have been converted into a dataset and displayed in Figure 13, there are several primary attributes. These attributes include \(T\)1_ (as shown in Figure 13a), \(T\)2_ (Figure 13b), \(T\)3_ (Figure 13c), \(T\)4_ (Figure 13d), \(H\)1_ (Figure 13e), \(H\)2_ (Figure 13f), \(H\)3_ (Figure 13g), \(H\)4_ (Figure 13h), and _Ampere_ (Figure 13i). New data based on the Ampere data were also generated to measure energy consumption. The energy (W) calculation process follows Equation (2). The results of these energy data are displayed in Figure 13j. There is a significant drop in temperature in the data T_1, T_2, T_3, and T_4_ (Figure 13a-d). This was caused by the incubator's box being opened, allowing hot air to escape. The box was intentionally opened to observe the data pattern, and once the incubator box was closed again, the temperature returned to normal. Meanwhile, there is an increase in humidity in the data H_1, H_2, H_3, and H_4_ (Figure 13e-h). Table 7 presents statistical information from the dataset. The statistical results are categorized into variables: count, mean, std, min, and max. The total number of data entries for each attribute is 65.535. The average temperature value in the dataset ranges from 31 \({}^{\circ}\)C to 39 \({}^{\circ}\)C. The average energy consumption is 161.47 Joule, with a minimum value of 99 Joule and a maximum of 202.40 Joule. Furthermore, Table 8 displays the correlation coefficient values from the dataset. Noticeably, the correlation coefficient values for the Joule and Ampere attributes are identical. This is because the Joule value is calculated based on the Ampere attribute. Some attributes show negative values in the correlation coefficient to the Joule attribute. The correlation between the \(T\)1 and \(T\)3 data attributes is 0.69, indicating a strong relationship. Similarly, the correlation between \(T\)2 and \(T\)3 is 0.35, suggesting that the temperature flows from \(T\)1 and \(T\)2 to \(T\)3, as observed in Figure 14. Furthermore, the humidity data attributes \(H\)1_ and \(H\)2_ and H_3 and \(H\)4_ have correlations exceeding 0.79. Additionally, the \(H\)4 attribute exhibits a significant correlation with the target attribute Joule, with a value of 0.91. Figure 12: Web application: (**a**) dashboard page; (**b**) data record page. Figure 14 depicts the temperature distribution pattern in the incubator. From the image, it is evident that the section of the incubator occupied by Sensor 1 and Sensor 2 has a higher temperature than the section occupied by Sensor 3 and Sensor 4. This temperature difference can be attributed to two primary factors.