The purpose of this research is to find the mathematical and metacognitive communication skills of junior high school students by gender. This research is a quantitative research. The population of this research is all students of class VIII SMP Negeri 1 Banda Aceh, SMP Negeri 9 Banda Aceh, and SMP Negeri 13 Banda Aceh with sample each 1 (one) class from each school. The data collection used is the test of mathematical communication ability and metacognitive questionnaire as well as interview from metacognitive question result. To find the difference of mathematical and metacognitive ability of students used T-test. The results of this study indicate that 1) there is no difference in mathematical communication ability of junior high students on gender-based material circle in the research sample; 2) there is no difference in mathematical communication ability of junior secondary students in gender-based circle material in each school being the research sample; 3) there was no difference in metacognitive ability of junior high school students in gender-based material circles in the study sample; 4) there is no difference in metacognitive ability of junior high school students in gender-based material circles in each school to be a research sample; 5) there is a strong relationship between mathematical communication ability with student's metacognitive ability.
Attendance is an inseparable component from lectures. The current manual attendance process still has its weaknesses, such as the loss or broken attendance sheets, the easiness to conduct fraud on the attendance sheets, and other cases. The attendance system using fingerprint devices are also not available in other locations yet, such as lectures that are done on the field or outdoor, where fingerprint devices are not available. Because of that, an online lecture attendance system that makes use of Android-based smartphones is developed in order to tackle the problem. This online lecture attendance system has the main feature of recording students’ attendances in a radius of 300 metres from the lecturer. This application is named Lecture Attendance System is developed using Rapid Application Development (RAD) model, because it is an effective method to minimise errors in the application. There are two testing performed to the application. The first testing was functional testing of the application. This testing was carried out in order to make sure that all functionalities and features are performing well. The second testing carried out is the distance accuracy testing, to compare between the Google Maps and MapBox API distances. From this testing, it was found that the error percentage using Google Maps is 9.250% and 12.128% for MapBox. From these results, they show that in calculating the distance, using Google Maps API has higher accuracy compared to the MapBox API.
Diabetes mellitus (DM) is one of the major diseases that cause death worldwide and lead to complications of diabetic foot ulcers (DFU). Improper and late handling of a diabetic foot patient can result in an amputation of the patient’s foot. Early detection of DFU symptoms can be observed using thermal imaging with a computer-assisted classifier. Previous study of DFU detection using thermal image only achieved 97% of accuracy, and it has to be improved. This article proposes a novel framework for DFU classification based on thermal imaging using deep neural networks and decision fusion. Here, decision fusion combines the classification result from a parallel classifier. We used the convolutional neural network (CNN) model of ShuffleNet and MobileNetV2 as the baseline classifier. In developing the classifier model, firstly, the MobileNetV2 and ShuffleNet were trained using plantar thermogram datasets. Then, the classification results of those two models were fused using a novel decision fusion method to increase the accuracy rate. The proposed framework achieved 100% accuracy in classifying the DFU thermal images in binary classes of positive and negative cases. The accuracy of the proposed Decision Fusion (DF) was increased by about 3.4% from baseline ShuffleNet and MobileNetV2. Overall, the proposed framework outperformed in classifying the images compared with the state-of-the-art deep learning and the traditional machine-learning-based classifier.
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