The Prediction Model of Human Household Behavior of the Refuse Management System with Artificial Neural Network

Rohana Sham
University of Technology and Innovation, Malaysia
Nor Aziyatul Izni
Universiti Teknologi MARA, Cawangan Selangor, Kampus Dengkil, Malaysia
Nor Asiah Mahmood
Prince Sultan University, Riyadh, Saudi Arabia
Nur Ilyana Ismarau Tajuddin
Universiti Sains Islam Malaysia, Nilai, Negeri Sembilan, Malaysia



Efficient management of household trash is essential to maintaining a sustainable society and a good environment. Low community engagement in environmental cleanup has led to dozens of unused refuse management apps. Today’s refuse management system lacks a secure identification protocol for identifying users, especially those who have signed up for the app. Predicting and understanding human household behavior is needed, and it remains a complex challenge. Therefore, this study aims to predict human household behavior in the refuse management system using artificial neural networks (ANN). The work involved in developing the prediction model included data collection, data pre-processing, neural network model development, and performance validation. There are 505 participants, urban residents in Kuala Lumpur obtained for this study. ANN with one hidden layer is developed in MATLAB. The results show that the accuracy of the developed model is 83%. It indicates that ANN performed well in predicting household behavior in the refuse management system.

Keywords:  artificial neural networks, refuse management system, household behavior, blockchain, prediction