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.
The current practice of drug inspection is usually carried out at school or university. This procedure, however, is not effective and efficient, as the urine samples are taken randomly. In many cases, the drug-taking student is not present or evades the urine or hair inspection. A predictive drug user tool is needed, where only suspected student drug users are selected for a urine test. In general, drug abuse constantly causes terrible damage to the skin lesions Since they damage the skin during hallucinations due to the effects of drugs. The Grey Level of Occurrence Matrix (GLCM) is used in this study to discover the scratch pattern. Our proposed GLCM is evaluated with 104 images collected from the Internet. Training data is generated from 88 images of people before and after the drug was collected from the Internet, and we set 16 image faces to test the prediction. The experiment shows that the prediction based on GLCM has better accuracy (81%) compared with the local binary pattern (LBP) which only reach up to 75%.
The management of zakat, infaq and shodaqoh funds is a very crucial activity for each zakat institution, in which two processes are carried out, namely the receipt and distribution of zakat infaq and shodaqoh. This article is the result of further research on the zakat receipt system that was previously published. This fund distribution system is the responsibility of all receipts of funds received so it is very crucial even compared to the ZIS acceptance system itself. This is because there is accountability for the receipt of funds received by the institution and must be submitted to certain parties in accordance with the provisions of Islamic Sharia. Problems that often occur in the process of fund distributing zakat, infaq shodaqoh is a entry data that accountability reports can not be made, the accountability report also can be made, besides that there is also an inaccurate target of the intended recipient distribution object. So that it is necessary to regulate how the right procedures in funds distributing of zakat infaq shodaqoh. The analytical method used by PIECES (Performances, Information, Economics, Control, Efficiency, Services). The standard operating procedure in the ZIS fund distribution system is made with internal control parameters (COSO).
While researchers have performed numerous studies to understand the human interpretation of visual graphs in reading, comprehending and interpreting displayed data; visually impaired (VI) students still face many challenges that prevent them from fully benefiting from these graphs in class. In this study, we conducted a test with 20 students to track the work described in studies in an expanded scenario. As we have tried to answer the question as to whether adding multi-reference mapping of sonification to auditory graphs could improve the of point estimation accuracy in non-visual condition. We also emphasize the efficiency of the performance of multi-reference graphs to make them as efficient as mapping using single pitch. Our proposed study improved performance of multi-reference task completion time by having fewer reference note. The results help provide empirical evidence that the multi-reference mode provides more accurate results than the single-pitch mode and confirms that adding contexts to auditory graphs could be used for better comprehension.
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