Knowledge Engineering and Data Science
Vol 5, No 2 (2022)

Adaptive Neuro-Fuzzy Inference System for Waste Prediction

Haviluddin Haviluddin ((SCOPUS ID: 56596793000, Universitas Mulawarman))
Herman Santoso Pakpahan (Mulawarman University)
Novianti Puspitasari (Mulawarman University)
Gubtha Mahendra Putra (Mulawarman University)
Rima Yustika Hasnida (Mulawarman University)
Rayner Alfred (Knowledge Technology Research Unit, Universiti Malaysia Sabah)



Article Info

Publish Date
30 Dec 2022

Abstract

The volume of landfills that are increasingly piled up and not handled properly will have a negative impact, such as a decrease in public health. Therefore, predicting the volume of landfills with a high degree of accuracy is needed as a reference for government agencies and the community in making future policies. This study aims to analyze the accuracy of the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The prediction results' accuracy level is measured by the value of the Mean Absolute Percentage Error (MAPE). The final results of this study were obtained from the best MAPE test results. The best predictive results for the ANFIS method were obtained by MAPE of 3.36% with a data ratio of 6:1 in the North Samarinda District. The study results show that the ANFIS algorithm can be used as an alternative forecasting method.

Copyrights © 2022






Journal Info

Abbrev

keds

Publisher

Subject

Computer Science & IT Engineering

Description

Knowledge Engineering and Data Science (2597-4637), KEDS, brings together researchers, industry practitioners, and potential users, to promote collaborations, exchange ideas and practices, discuss new opportunities, and investigate analytics frameworks on data-driven and knowledge base systems. ...