Improving the accuracy of waste classification using other CNN architectures

Oct 13, 2025 by recoveryshake.com 142 views

The Growing Concern of Waste Management in Indonesia

Waste has become one of the increasingly urgent environmental issues, along with the increase in population and increasing human activity. Data from the Integrated Environmental and Forestry Information System (SIPSN) of the Ministry of Environment and Forestry shows that Indonesia is facing a waste of waste, with the volume of waste production reaching 35.16 million tons in 2022. Inappropriate waste management can have a negative impact on the environment and human health. The consequences of poor waste management can be severe, including the contamination of soil, water, and air, as well as the spread of diseases.

The Importance of Accurate Waste Classification

Accurate waste classification is crucial in ensuring that hazardous waste is properly managed and disposed of. The use of artificial intelligence (AI) and machine learning (ML) techniques has shown promise in improving the accuracy of waste classification. In this study, we utilize the Conversional Nerve Network (CNN) with two popular CNN architectures, namely VGG-16 and Densenet-121, to sort dangerous and toxic solid waste.

The Dataset Used in This Study

This study utilizes a dataset from the Kaggle source, which consists of seven data classes. The dataset shows an imbalance between classes, where four classes each have 500 samples and three other classes have 300 samples, so the total reaches 2,900 data points. Although data is not balanced, promising results are obtained through ensemble modeling that combines the VGG-16 and Densenet-121 architecture.

The Experimental Results

In the experimental results between VGG architecture and denset, the accuracy value was obtained, in which VGG reached an accuracy score of 76%, while the Densenet recorded a better accuracy score, which was 79%. The prediction error rate (loss) for each model is below 70%, with VGG at 0.27% and denset at 0.68%. To increase the accuracy of each architecture, the ensemble technique is used in this study, which results in an accuracy of 83% and a prediction error rate (loss) of 0.57%.

The Benefits of Ensemble Learning Techniques

The use of ensemble learning techniques in dangerous waste sorting is very relevant given the challenges faced by traditional classification methods. Data imbalances, as contained in the dataset of this study, often become an obstacle in achieving high accuracy. The ensemble approach, which combines predictions of several models, is able to utilize the strength of each model while minimizing the impact of weaknesses of a weaker model.

The Advantages of VGG-16 and Densenet-121 Architectures

VGG-16 and DENSENET-121 are CNN architectures that have been proven effective in various image classification tasks. VGG-16, although simpler in the structure, has shown good ability in learning the visual features of data. On the other hand, the denset-121 offers advantages by connecting layers in the network more complex, allowing information to flow better and support deeper learning.

The Results of This Study

The results of this study indicate that the integration of the two architectures through the ensemble technique produces a more reliable and accurate model. With 83% accuracy achieved, this research not only contributes to the management of hazardous waste, but also opens opportunities to apply similar approaches in other fields, such as the introduction of objects, resource management, and others.

Conclusion

Overall, the use of ensemble learning techniques with VGG-16 and Densenet-121 architecture in this study provides a clear picture of the potential for artificial intelligence in solving urgent environmental problems, especially in terms of waste management. It is hoped that the results of this study can be a reference for further research and the development of a more effective and efficient waste management system in the future.

Future Directions

This study opens up opportunities for further research in the field of waste management using ensemble learning techniques. Some potential directions for future research include:

Improving the accuracy of waste classification using other CNN architectures
Developing a more efficient and effective waste management system using AI and ML techniques
Applying ensemble learning techniques to other fields, such as resource management and object introduction

By exploring these directions, we can further develop the potential of AI and ML in solving urgent environmental problems, and contribute to a more sustainable and environmentally-friendly future.

Q: What is the main objective of this study?

A: The main objective of this study is to utilize ensemble learning techniques with VGG-16 and Densenet-121 architecture to sort dangerous and toxic solid waste, and to evaluate the effectiveness of this approach in improving the accuracy of waste classification.

Q: What is the significance of using ensemble learning techniques in waste management?

A: Ensemble learning techniques are significant in waste management because they can combine the strengths of multiple models and minimize the impact of weaknesses, leading to more accurate and reliable results.

Q: What are the advantages of using VGG-16 and Densenet-121 architectures in image classification tasks?

A: VGG-16 and Densenet-121 are CNN architectures that have been proven effective in various image classification tasks. VGG-16 has shown good ability in learning the visual features of data, while Densenet-121 offers advantages by connecting layers in the network more complex, allowing information to flow better and support deeper learning.

Q: What is the dataset used in this study, and what are its characteristics?

A: The dataset used in this study consists of seven data classes, with four classes having 500 samples and three other classes having 300 samples, resulting in a total of 2,900 data points. The dataset shows an imbalance between classes, which can be a challenge in achieving high accuracy.

Q: What are the experimental results of this study, and what do they indicate?

A: The experimental results of this study show that the ensemble technique combining VGG-16 and Densenet-121 architectures achieves an accuracy of 83% and a prediction error rate (loss) of 0.57%. This indicates that the ensemble approach is effective in improving the accuracy of waste classification.

Q: What are the potential applications of this study in other fields?

A: The results of this study can be applied to other fields, such as resource management and object introduction, where accurate classification is crucial.

Q: What are the future directions for this research?

A: Future directions for this research include improving the accuracy of waste classification using other CNN architectures, developing a more efficient and effective waste management system using AI and ML techniques, and applying ensemble learning techniques to other fields.

Q: What are the implications of this study for waste management?

A: The results of this study have significant implications for waste management, as they demonstrate the potential of AI and ML techniques in improving the accuracy of waste classification and developing more efficient and effective waste management systems.

Q: What are the limitations of this study?

A: The limitations of this study include the use of a specific dataset and the need for further research to generalize the results to other datasets and applications.

Q: What are the potential challenges in implementing this study in real-world settings?

A: The potential challenges in implementing this study in real-world settings include the need for high-quality data, the complexity of the waste classification task, and the potential for errors in the classification process.

Q: What are the potential benefits of implementing this study in real-world settings?

A: The potential benefits of implementing this study in real-world settings include improved accuracy of waste classification, reduced errors in the classification process, and more efficient and effective waste management systems.