The businesses in various fields use the online communication application to gather their data and information with local and global sources. The gathered data may sensitive such as the financial and businesses development information. The hackers or online thief try to stole the valuable data i.e. credit card numbers. The organizations looking for secure online channels in order to transfer their data efficiently and avoid the data thieving. One of the most applicable methods that developed to secure the online transferred data is the cryptography which transfers the original data or information to encrypted formulation. Cryptography still has many drawbacks such as stole and decrypts the original texts using automatic decryption counter. The main aim of this research is to improve the cryptography securing level using supportive method which is Steganography. The Steganography is the processes of hide the data or information in media files such as video, images and audio files. There are four stages represent the methodology of this paper; (1) encrypt the original texts using RSA algorithm, (2) hide the encrypted texts in Image files, (3) extract the encrypted texts from Image files, and (4) decrypt the original texts using decryption key of RSA algorithm. It is expected to improve the security level of the online transferred textual data. The performance of the final results will be evaluated through compare the Image files quality before and after hide the data in these files. The quality of the original and stego Image files need to be same or near in order to maximize the difficulty of detect that there data hide in these files.

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.

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 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).

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%.