## 3 Results
### Classification of Solid Waste with MNT Sensor Using Machine Learning Algorithms
Finally, for both reusable and organic waste, the capacitance thresholds are in an intermediate tolerance zone, which significantly reduces the risk of incorrect classification when the new data deviate slightly from the values established during the training process. The MT and MNT sensors, made from recycled materials such as aluminum, represent an economical and sustainable solution compared to commercial sensors. This approach not only significantly reduces production costs but also promotes sustainable practices by incorporating recycled components into their design. Unlike commercial capacitive sensors, which only provide binary detection (presence or absence), the MT and MNT sensors offer continuous capacitance values, enabling more precise and effective material differentiation. This outstanding capability makes them suitable for applications requiring high classification accuracy, such as the separation of organic and recyclable waste. Additionally, they surpass commercial alternatives based on optical or spectrometry technologies in terms of simplicity and cost. Another distinguishing feature of the MT and MNT sensors is their sensitivity to dielectric materials, which offers a significant advantage in environments where environ  
Figure 11: Validation of the Random Forest. mental conditions vary considerably. Unlike commercial optical solutions, which can be affected by external factors such as lighting or dust, the MT and MNT sensors maintain consistent and reliable performance. These sensors are an innovative and efficient alternative that combine precision, low cost, and sustainability, making them ideal for real-world applications. Their accessible design enhances their feasibility in communities with limited resources compared to advanced technologies that, although precise, require expensive equipment and specialized maintenance. This accessibility also makes them a valuable tool for waste classification at the source, particularly in residential settings, marking a significant difference in waste management and contributing to environmental protection. Among the limitations identified in the present study is the possibility that the sensitivity of the sensors may be influenced by environmental factors, such as humidity and temperature, which could alter the recorded capacitance values. However, it is reasonable to assume that reusable waste, such as plastics, metals and glass, present significantly lower humidity levels than organic waste, where the humidity concentration could have a more representative impact on the measurements. Additionally, the long-term durability and scalability of sensors made from recycled materials constitute challenges inherent to any emerging technology. Although these aspects and hypotheses were not analyzed in detail in this manuscript, they do not compromise the validity of the results obtained. This is because the main objective of the study was to demonstrate the technical feasibility of low-cost capacitive sensors and their integration with machine learning algorithms for waste classification. Although the capacitance values recorded by the sensors are sufficiently differentiable to classify organic and reusable waste without the need to use machine learning algorithms, the inclusion of a classification algorithm such as Random Forest provides significant benefits to the design of the proposed system. Firstly, it offers robustness against possible fluctuations in capacitance values derived from external factors, such as humidity, variations in temperature, interference from electromagnetic fields generated by nearby devices and other environmental elements. This allows for more accurate classification in real scenarios where operating conditions are often less controlled and data may be affected by noise.