Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
The university need to improve their services for student, especially in this research focus on how to responses the student questions related the academic aspects. This research explained the analysis of chatbot model that suitable for university student. This research used Analytical Hierarchical Process (AHP) to determine the model of chatbot to response the student questions, the alternatives of this model are flow chatbot and artificial intelligence with natural language processing. The criteria that we used to analysis this model is easy maintenance, easy modification, error rate, performance, and cost. We used one expert judgement method to assess the chatbot based on those criteria. The results showed the suitable model for university student is flow chatbot at the initial state based on the current situation and complexity of university in Indonesia
Support vector machine (SVM) is one of the flagship methods in machine learning because SVM have good results in terms of classification and prediction. Classification Analysis is the process of finding the best model of the classifier to predict classes of an object or data whose class label is unknown. The study aims to select accurate classification methods in classification. The study used the Least Support Vector Machine (LS-SVM) method. LS-SVM is better compared to standard SVM in computation processes, rapid convergence, and high precision. On LS-SVM it has several parameters. Optimal parameter delivery on the LS-SVM method can affect increased classification accuracy. As for the various kinds of giving optimal parameter selection methods on LS-SMVS such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Gird Search, and Particle Swarm Optimization and Gravitational Seach Algorithm (PAO-GSA). The study results show that the selection of more optimal parameters can result in significantly increased classification and prediction accuracy and more even computational process times. PSO-GSA is a method of selecting optimization parameters that can be used to improve classification accuracy