Neural Network Prediction for Efficient Waste Management in Malaysia

Siti Hajar Yusoff (International Islamic University Malaysia)
Ummi Nur Kamilah Abdullah Din (International Islamic University Malaysia)
Hasmah Mansor (International Islamic University Malaysia)
Nur Shahida Midi (International Islamic University Malaysia)
Syasya Azra Zaini (International Islamic University Malaysia)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 12, No 2: November 2018

Maintaining current municipal solid waste management (MSWM) for the next ten years would not be efficient anymore as it has brought many environmental issues such as air pollution. This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Generation (SWG) based on population growth factor. This study uses Malaysian population as sample size and the data for weight is acquired via authorized Malaysia statistics’ websites. All data will be normalized in the pre-processing stage before proceeding to the prediction using Visual Gene Developer. This project evaluated the performance using R2 value. Two hidden layers with ten and five nodes were used respectively. The result portrayed that there will be an increase of 29.03 percent of SWG in year 2031 compared to 2012. The limitation to this study is that the data was not based on real time as it was restricted by the government.