Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ramlah P.
Universitas Teknologi Akba Makassar
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Machine learning-based prediction model for high-value waste types to support the circular economy Andi Irmayana; Nurul Aini; Ramlah P.; Muhammad Husnul Yaqin; Zalsabila Sabir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4199-4212

Abstract

The management of high-value waste is a strategic challenge in supporting the implementation of a circular economy at the city level. This study aims to develop and compare machine learning-based prediction models to classify the economic value of waste into low, medium, and high categories based on the characteristics of transaction types and quantities. The dataset was obtained from waste bank transactions in Makassar City from January 2023 to June 2025. The research stages included data preprocessing, category labeling using the k-means and quantile-based classification algorithms, and then creating classification modeling using random forest and gradient boosting. The evaluation results show that both models perform very well with an accuracy rate above 92%, where gradient boosting consistently shows superior performance in terms of F1-score and the ability to reduce classification errors in high-value categories. Confusion matrix analysis shows that the boosting approach is more adaptive in handling class boundary distributions, while random forest tends to be more stable against data variations. These findings confirm that the ensemble learning approach is effective in supporting the classification of waste economic value based on historical transaction data.
Co-Authors Andi Irmayana Muhammad Husnul Yaqin Nurul Aini Zalsabila Sabir