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All Journal International Journal of Advances in Intelligent Informatics IJIES (International Journal of Innovation in Enterprise System)
Hassan, Aslinda
Unknown Affiliation
Computer Science & IT Control & Systems Engineering

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A comparison of machine learning methods for knowledge extraction model in A LoRa-Based waste bin monitoring system Abidin, Aa Zezen Zaenal; Othman, Mohd Fairuz Iskandar; Hassan, Aslinda; Murdianingsih, Yuli; Suryadi, Usep Tatang; Siallagan, Timbo Faritchan
International Journal of Advances in Intelligent Informatics Vol 10, No 1 (2024): February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i1.1026

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.
An Insider Threat Categorization Framework for Automated Manufacturing Execution System Putri, Nilda Tri; Elsera, Azira Fitria; Mohammad, Nur Ameera Natasha; Yassin, Warusia Mohamed; Ahmad, Rabiah; Hassan, Aslinda; Al Mhiqani, Mohammed Nasser Ahmed
International Journal of Innovation in Enterprise System Vol. 3 No. 2 (2019): International Journal of Innovation in Enterprise System
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Insider threats become one of the most dangerous threats in the cyber world as compared to outsider as the insiders have knowledge of assets. In addition, the threats itself considered in-visible and no one can predict what, when and how exactly the threat launched. Based on conducting literature, threat in Automated Manufacturing Execution Systems (AMESs) can be divided into three principle factors. Moreover, there is no standard framework to be referring which exist nowadays to categorize such factors in order to identify insider threats possible features. Therefore, from the conducted literature a standard theoretical categorization of insider threats framework for AMESs has been proposed. Hence, three principle factors, i.e. Human, Systems and Machine have considered as major categorization of insider threats. Consequently, the possible features for each factor identified based on previous researcher recommendations. Therefore, via identifying possible features and categorize it into principle factors or groups, a standard framework could be derived. These frameworks will contribute more benefit specifically in the manufacturing field as a reference to mitigate an insider threat. Keywords—automated manufacturing execution systems insider threats, factors and features, insider threat categorization framework.
Co-Authors Aa Zezen Zaenal Abidin, Aa Zezen Zaenal Ahmad, Rabiah Al Mhiqani, Mohammed Nasser Ahmed Elsera, Azira Fitria Mohammad, Nur Ameera Natasha Nilda Tri Putri Othman, Mohd Fairuz Iskandar Siallagan, Timbo Faritchan Usep Tatang Suryadi, Usep Tatang Yassin, Warusia Mohamed Yuli Murdianingsih, Yuli