Creativity is a very important skill that should be possessed by the human resources of a country, especially in this period of the 21st century. Human creativity must be stimulated from various things, including via the field of education to improve the quality of human resources. The goal of this study was to investigate the impact of Lego Mindstorms as learning tools to improve the creativity skills of students, using an experimental methodology. We used a random sampling technique to select 40 students as the sample (N=40) for the study, with age ranging 10-12 years old; the sample was divided 2 groups, 20 students were assigned to the control groups, while the other 20 students formed the experimental groups. The student’s creativity skills were taken from a figural creativity test (TKF). This test was conducted before the intervention (pretest) and after the intervention program (posttest). In the intervention program, the experimental group students were given some education about robotic technology via the use of the Lego Mindstorms tools. To analyze the test results, we utilized the Statistical Product and Service Solutions package. The finding showed that there are significant differences between the creativity scores of students in the experimental group and the creativity scores of the control group. The Lego Mindstorms influences the enhancement of student’s creativity of around 23.6% in the experimental groups.
While researchers have performed numerous studies to understand the human interpretation of visual graphs in reading, comprehending and interpreting displayed data; visually impaired (VI) users still face many challenges that prevent them from fully benefiting from these graphs. Thus, it influences their understanding of data visualization and in turn reduces their role in collaborating with their sighted colleagues in educational and working environments. We intend to develop a mobile application where visually impaired users can work together to build a collaborative graph that supported by data sonification in the mobile environment. The system properties were all tested by the task of identifying line trends in time series, which resulted in an accuracy of more than 80% for notes below 20 points. The usability testing has given result of 6.7 out 10 based on users’ perception on the effectivity of the features.
The impact of the global recession in 1998 that originated from the recession in the US will affect the projected economies in Asia, including Indonesia, both direct and indirect nature. In this study, we predicted Indonesia’s GDP in the event of the economic crisis that hit Indonesia starting in 1998. Instead of using the famous prediction algorithm as a neural network and linear regression. K-Nearest Neighbour is selected because it is easy and fast to use in the small dataset. We use a dataset from 1980-2002, consisting of rice prices, premium prices, GDP of Japanese country, American GDP, currency exchange rates, Indonesian government consumption, and the value of Indonesia’s oil exports. For evaluation, we compare k-NN regression prediction result with prediction result using back propagation neural network and multiple linear regression algorithm. Result show, k-NN regression is able to predict Indonesia’s GDP using small dataset better than the neural network, and multiple linear regression method.
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