| Autor: |
Ihsan A; Department of Informatics, Faculty of Engineering, Universitas Samudra, Langsa 24416, Aceh, Indonesia., Muttaqin K; Department of Informatics, Faculty of Engineering, Universitas Samudra, Langsa 24416, Aceh, Indonesia., Fajri R; Department of Chemistry, Faculty of Engineering, Universitas Samudra, Langsa 24416, Aceh, Indonesia., Mursyidah M; Department of Multimedia Engineering Technology, Politeknik Negeri Lhokseumawe, Kota Lhokseumawe 24301, Aceh, Indonesia., Fattah IMR; Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia. |
| Abstrakt: |
In this paper, we introduce a new and advanced multi-feature selection method for bacterial classification that uses the salp swarm algorithm (SSA). We improve the SSA's performance by using opposition-based learning (OBL) and a local search algorithm (LSA). The proposed method has three main stages, which automate the categorization of bacteria based on their unique characteristics. The method uses a multi-feature selection approach augmented by an enhanced version of the SSA. The enhancements include using OBL to increase population diversity during the search process and LSA to address local optimization problems. The improved salp swarm algorithm (ISSA) is designed to optimize multi-feature selection by increasing the number of selected features and improving classification accuracy. We compare the ISSA's performance to that of several other algorithms on ten different test datasets. The results show that the ISSA outperforms the other algorithms in terms of classification accuracy on three datasets with 19 features, achieving an accuracy of 73.75%. Additionally, the ISSA excels at determining the optimal number of features and producing a better fit value, with a classification error rate of 0.249. Therefore, the ISSA method is expected to make a significant contribution to solving feature selection problems in bacterial analysis. |
