Fountain of Informatics Journal

4

Authors: Aidil Primasetya Armin, Fridy Mandita

The BVH data is composed of two parts, the first part detailing the hierarchy and the initial pose of the framework while the second part describes the data for each frame. This data contains the translation and rotation of each joint in the form of coordinates based on X, Y, and Z axes. This study demonstrates methods for modifying BVH data to obtain new movements. First choose the joint on the BVH data, then determine the coordinate point of the initial movement and the coordinate point of the joint end motion. Both select the coordinate point to be modified. The three selected points are rotated using a rotation matrix based on the X, Y, and Z axes. The results of the test will be displayed in the form of animated characters.

6

Authors: Muhammad Nugraha1, Yogi Nugraha

Lectures schedule management in campus is an important aspect that must be implemented in every period. The schedule will be managed well if avoided from collision of the room, time, and lecturer. To overcome collision problem it's necessary to make mitigate system to avoid schedule changes frequently caused by collision of room, time, and lecturer. Thus, the author design and build SIM-DRaW (Sistem Informasi Manajemen Dosen, Ruang, dan Waktu) web-based to fulfill needs and overcome schedule management system. The final result of the design and build SIM-DRaW is implemented information system that can help schedule manager to avoid collision earlier.

7

Authors: Hendra Saputra, Setio Basuki, Mahar Faiqurahman

Android Malware hasgrowth significantly along with the advance of the times and the increasing variety of technique in the development of Android. Machine Learning technique is a method that now we can use in the modeling the pattern of a static and dynamic feature of Android Malware. In the level of accuracy of the Malware type classification, the researcher connect between the application feature with the feature required by each types of Malware category. The category of malware used is a type of Malware that many circulating today, to classify the type of Malware in this study used Support Vector Machine (SVM). The SVM type wiil be used is class SVM one against one using the RBF Kernel. The feature will be used in this classification are the Permission and Broadcast Receiver. To improve the accuracy of the classification result in this study used Feature Selection method. Selection of feature used are Correlation-based Feature Selection (CFS), Gain Ratio (GR) and Chi-Square (CHI). Result from Feature Selection will be evaluated together with result that not use Feature Selection. Accuracy Classification Feature Selection CFS result accuracy of 90.83%, GR and CHI of 91.25% and data that not use Feature Selection of 91.67%. The result of testing indicate that permission and broadcast receiver can be used in classyfing type of Malware, but the Feature Selection method that used have accuracy is a little below the data that are not using Feature Selection.