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:
Corn is one of the leading food that produces carbohydrates in Indonesia. It can grow well in hot and cold areas with sufficient rainfall and irrigation. However, each part of the corn is sensitive to several diseases, and it can reduce the quantity and quality of the corn result production. Damage of corn plant that is caused by the disease can be conducted by the disturbing process into the plant and make the plant died. The diseases can undermine corn plants by disrupting the processes inside the plant and make the plant died. Therefore, this study aims to design a system for detecting diseases and pests in corn plants using Certainty Factor and Fuzzy Sugeno methods. The Fuzzy Sugeno method is employed to identify diseases and pests in corn plants based on the degree of trust in the diseases of the corn plants. The degree of confidence in the disease can be obtained from the certainty level of the base system built by the Certainty Factor method. The experiments have been carried out to determine the accuracy of the Certainty Factor and Fuzzy Sugeno methods. Therefore, the detection system can work effectively and efficiently as well as minimize the amount of damaged corn production. We collected 15 diseases or pests and 48 symptoms, and the experiment results have obtained an accuracy of 85.16%
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
