(Informatics Departement, Universitas Mandiri & Universiti Teknikal Malaysia Melaka, Malaysia)(2) Mohd Fairuz Iskandar Othman
(Universiti Teknikal Malaysia Melaka, Malaysia)(3) Aslinda Hassan
(Universiti Teknikal Malaysia Melaka, Malaysia)(4) Yuli Murdianingsih
(Informatics Departement, Universitas Mandiri, Indonesia)(5) Usep Tatang Suryadi
(Informatics Departement, Universitas Mandiri, Indonesia)(6) Timbo Faritchan Siallagan
(Informatics Departement, Universitas Mandiri, Indonesia)*corresponding author
AbstractKnowledge 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.
KeywordsIoT; Knowledge extraction model; LoRa; Machine Learning; Waste management
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DOIhttps://doi.org/10.26555/ijain.v10i1.1026 |
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