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Waste has become a serious environmental problem in Indonesia, which continues to increase along with population growth. The issue of waste management poses serious challenges for the environment, especially in the process of separating organic and inorganic waste. In the field of computer vision, recognising the type and shape of waste through camera images remains a challenge due to variations in shape, colour, and complex lighting conditions. Therefore, this problem utilises Deep Learning technology, which is expected to be widely applied in Indonesia, especially in large cities with high waste volumes. This study aims to distinguish between organic and inorganic waste using the Convolutional Neural Network (CNN) method based on digital images. The developed CNN model was trained to recognise the visual patterns of each type of waste and tested to measure its accuracy. The test results show that the CNN-based classification system is capable of achieving an accuracy rate of 95%, thus proving the effectiveness of this method in supporting artificial intelligence-based automatic waste sorting systems.

Indonesia has extraordinary resources and potential in developing renewable energy sources (RES), but various obstacles must be overcome in implementing RES. The purpose of this study is to analyze the gap in the application of RES. This gap includes energy knowledge, community participation, battery waste management, service quality, regulation, and legal policy. This study uses a mixed-methods approach, by conducting a structured questionnaire in quantitative data collection, while qualitative data collection through special interviews, focused group discussions, and conducting policy regulation analysis. The results show that 62% of people do not understand RES, 28% are involved in project planning, and 74% are unaware of SOP (standard operating procedures) regarding battery waste recycling. The results of the correlation analysis reveal a positive relationship between the level of knowledge and interest in RES (R = 0.56). Also, the developed community-based participation model includes initial involvement, transparency of information, and local incentives. These findings further strengthen the compatibility of the innovation diffusion theory, planned behavior theory, SERVQUAL, and the theory of public interest. This study will make a practical contribution through evidence-based strategies in increasing resilience, especially for policymakers and energy service providers. The impact of the policy aspects includes the need for large reforms, education, public campaigns, and the realization of battery waste management systems. This study also provides an opportunity for further study by expanding the geographical scope and related industrial sectors.