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:
One of the milestones any company or industry should consistently achieve is the long-term steady growth. That nonetheless is precisely the position of natural gas distribution sector-without exception. Year-over-year statistical data should portray the broader picture of city gas sector in Indonesia that can possibly be explained by considering a number of key elements such as the workforces involved in the industry, wages that have to be spent on the labours and finally yet importantly volume of natural gas distributed for the city consumers. Throughout this particular study, a set of variables taken into account and then subsequently perform the analysis by using multivariate regression analysis as the selected method. The purpose of this particular study is to explain a better understanding of Indonesia’s city gas distribution sector. The methodology used in this study is the multivariate linear regression. In general, this paper is directed toward defining the notion of the growth of natural gas distribution sector in Indonesia. At the end of this study, the linear model effectively explains the strong correlation between the revenue (dependent variable) of the city gas distribution sector with other independent variables predictors) and fits to represent the macro perspective of city gas sector in Indonesia. In that respect, by incorporating a number of variables as the input of regression analysis tools, the expected result is a model that can be applied to predict the long-term growth
Rhizome is a kind of herbal plant that has many benefits for health. There are many kinds of rhizomes in Indonesia, such as temuireng, temulawak and temumangga, which have similarities in color, shape and odor. Therefore, for ordinary people, they are difficult to be identified. In this study, some rhizomes were classified into three classes, namely the class of temuireng, temulawak and temumangga based on their odor. The odor of a rhizome was captured by a TGS2600 sensor which was connected to the raspberry phi 3B in the voltage value. The voltage data ware taken in 20 minutes for each class before classification process using a multiclass support vector machine with the one-against-one method. Results showed that the support vector machine could classify the type of a rhizome based on its odor with 98.8% accuracy
