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:
Identification of maturity of tomatoes in general is still mostly done manually by farmers. Manual way is done based on visual observation directly on the fruit to be classified. The development of information technology enables the manufacture of sorting tools by identifying the level of maturity of tomatoes which is very useful for farmers. From 5 times the test with different tomato obtained tomato average data taken on serial communication and digital scales, the system has accuracy 99.84%. The servo motor pusher tomato testing the best movement of Servo Motor is known from the actual angle in coding (90 °) with servo motion angle motion (92.12 °) obtained error of 0.97%. The accuracy on the fall of the tomato from the conveyor is 76,67%, the test scenario is done by measuring the tomato falls on the Switcher at the corners of Grade A, B, and C
Tomato is a fruit that is consumed every day by the Indonesia population Identification of maturity of tomatoes in general is still mostly done manually by farmers. The development of information technology enables the identification of fruit maturity level based on ciitra with the help of computer. This computational way is done by using the camera as an image processor of the recorded image (image processing). The tomato fruit is identified based on the image HSV input obtained from the capture result, after obtaining the HSV for its quality detection. From several samples of grayscale data pattern of tomato fruit with different quality levels obtained by several groups of maturity level, some of the maturity level will be clarified with Learning Vector Quantization algorithm with HSV data is processed into HSV data and classified by this algorithm to get maturity level and accurate quality. This system has accuracy 76.67%