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:
New Normal is a New order that can adapt to COVID-19. Indonesian people must maintain productivity in the midst of the COVID-19 corona virus pandemic. To realize the new normal scenario, the Indonesian Government and experts have formulated a protocol or Standard Operational Procedure (SOP) to ensure that people can return to their activities, but remain safe from COVID-19. One of the health protocols is to require people to wear masks. Several previous studies have designed a facial pattern recognition system, but to detect facial patterns using masks has not been the optimal solution. Therefore, this study has tried to design and analyze a facial pattern recognition system using masks using the Viola Jones method with the Haar Cascade Classifier. The Haar Cascade Classifier method is used to recognize facial patterns based on haar wavelets. The haar-like feature method is made easier with the cascade classifier adaboost. Meanwhile, the Viola Jones method is used for object detection methods by combining Haar Like Feature and has high accuracy and better performance than other eigen faces algorithms. The purpose of this study is to help the performance of the level of security in selecting facial patterns into a room that has complied with health protocols in the new normal era or not. From the test results with 50 face samples, the accuracy percentage is 92.3% and it takes a relatively short time to recognize faces using masks, which is an average of 15 seconds per sample tested
This study aims to analyze the forecasting the GHG emission, population, GDP growth of energy consumption in the Industries sectors. The scope of the study covers the analysis of energy consumption, and the forecasting of GHG emission population, GDP growth of energy consumption for the next 10 years (2016-2025), and 20 years (2016-2035) by using Vector Autoregressive Model: VAR Model from the Input-output table of Thailand. The result show that petroleum has the highest level of energy consumption, followed by Other Petroleum Products, Cement, Ceramic and Earthen Wares, Cosmetic, fertilizer and pesticides, soap and cleaning preparations, rubber sheet and block rubber, and petroleum refineries, respectively. The prediction results show that these models are effective in forecasting measured by using RMSE, MAE, and MAPE. The results forecast of each model are as follows: 1) Model 1(2,1,1) shows that GHG emission will, increasing steadily to 25.71% by the year 2025 in comparison to 2016.2) Model 2 (2,1,2) shows that GHG emission will rise steadily, increasing to 45.20% by the year 2035 in comparison to 2016
