Abstract
Separation of organic and inorganic waste for daily need is one of efforts to yield sanitation. However, most people have difficulties to distinguish these kind of waste. Therefore this paper propose a system that can recognize organic and inorganic waste automatically. These system is developed using hybrid PSO-BPNN algorithm to recognize type of waste. Input data is organic and inorganic image which is captured around campus. This paper also presents comparison of BPNN, PSO and PSO-BPNN in recognizing type of waste. The results show that each algorithm achieves 77%, 69% and 95% for BPNN, PSO and hybrid PSO-BPNN respectively.
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The authors thank to School of Computer Science (SoCS) and Research Technological Transfer Office (RTTO) of Bina Nusantara University, Indonesia for supporting this research.
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Djaya, C.R.A., Sucianti, N., Randy, Wulandhari, L.A. (2017). Hybrid Particle Swarm Optimization and Backpropagation Neural Network for Organic and Inorganic Waste Recognition. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-57261-1_17
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DOI: https://doi.org/10.1007/978-3-319-57261-1_17
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