A comparison of machine learning methods for knowledge extraction model in A LoRa-Based waste bin monitoring system

Zaenal Abidin, Aa Zezen and Othman, Mohd Fairuz Iskandar and Hassan, Aslinda and Murdianingsih, Yuli and Suryadi, Usep Tatang and Siallagan, Timbo Faritchan (2024) A comparison of machine learning methods for knowledge extraction model in A LoRa-Based waste bin monitoring system. International Journal of Advances in Intelligent Informatics, 10 (1). pp. 79-93. ISSN 2442-6571

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Abstract

Knowledge Extraction Model (KEM) is a system that extracts knowledge through an IoT-based smart waste bin emptying scheduling classification. Classification is a difficult problem and requires an efficient classification method. This research contributes in the form of the KEM system in the classification of scheduling for emptying waste bins with the best performance of the Machine Learning method. The research aims to compare the performance of Machine Learning methods in the form of Decision Tree, Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, and Multi-Layer Perceptron, which will be recommended in the KEM system. Performance testing was performed on accuracy, recall, precision, F-Measure, and ROCS curves using the cross-validation method with ten observations. The experimental results show that the Decision Tree performs best for accuracy, recall, precision, and ROCS curve. In contrast, the K-NN method obtains the highest F-measure performance. KEM can be implemented to extract knowledge from data sets created in various other IoT-based systems.

Item Type:	Article
Uncontrolled Keywords:	IoT, Knowledge extraction model, LoRa, Machine Learning, Waste management
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	23 Feb 2026 04:04
Last Modified:	23 Feb 2026 04:04
URI:	http://eprints.utem.edu.my/id/eprint/29547
Statistic Details:	View Download Statistic

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