## 3 Proposed System Model
### Dataset Overview and Data Preprocessing  
The dataset consists of a range of components related to environmental factor measurements, user feedback, crowd density data in the library, and the timestamp for each record. The sensor data encompass environmental parameters such as light, CO\({}_{2}\), temperature, humidity, sound, TVOCs, and eCO\({}_{2}\), while user feedback provides insights into the perception of environmental conditions and overall user experience. The crowd density data were collected using Raspberry Pi to detect the number of individuals in the library, and the timestamp enables the accurate temporal association of each data point.  
Categorical data were transformed into numerical values using the Label Encoding method, which is commonly employed in machine learning applications to facilitate data modeling [47]. For instance, qualitative user feedback ratings--such as \"very sufficient\", \"sufficient\", \"indifferent\", \"insufficient\", and \"not sufficient at all\"--were mapped to numerical values of 5, 4, 3, 2, and 1, respectively. This conversion enabled the integration of categorical data into the analysis, thereby improving the overall effectiveness of the modeling process. Following this transformation, sensor data were correlated with the numerically encoded user feedback categories to facilitate comprehensive analysis.  
During the data preprocessing phase, the dependent variable, representing the environmental quality level (e.g., environmental condition classification based on user feedback), was identified, while independent variables, including the sensor data and crowd density, were selected as key predictive factors [48]. A thorough examination of class distribution revealed an imbalance, necessitating the application of the Synthetic Minority Over-sampling Technique (SMOTE) to generate additional samples for underrepresented classes. This approach has been demonstrated to improve model performance by ensuring that minority class instances are adequately learned while also mitigating overfitting risks [49].  
To further explore the characteristics of the dataset, various visualization techniques were utilized. Histograms were employed to examine the distribution of variables, allowing for the identification of patterns and potential anomalies within the data [50]. Additionally, correlation analysis was conducted to assess the relationships between different environmental parameters and user feedback, providing deeper insights into the dependencies among variables [51].  
Following the numerical encoding process, sensor data and user feedback were synchronized based on their respective timestamps. This alignment ensured that each data point was accurately associated with its corresponding environmental conditions and user perception, thereby creating a structured and temporally coherent dataset.  
To enable a more comprehensive environmental assessment, CO\({}_{2}\), TVOC, and eCO\({}_{2}\) measurements were integrated to represent overall ventilation conditions. Similarly, the temperature and humidity parameters were grouped to facilitate a more holistic analysis of indoor environmental quality. Finally, feature selection techniques were applied to refine the dataset, enhancing model accuracy by prioritizing the most relevant attributes for predictive analysis.  
Table 2 presents the distribution of sensor data based on the classes, providing a detailed insight into how the sensor data are distributed across each class. It allows for a  
systematic examination of the data collection density of specific sensor readings within each class.