THE EFFECT OF GOOGLE SKETCHUP AND NEED FOR ACHIEVEMENT ON THE STUDENTS’ LEARNING ACHIEVEMENT OF BUILDING INTERIOR DESIGN

This study aims to find out the effect of the Google SketchUp application and the need for achievement on students’ learning achievements of building interior design. Quasi-experimental research was conducted at the Vocational High School (VHS) with a 2 x 2 factorial design. The Google SketchUp application was used in the experimental group, while the PowerPoint Slides were used in the control group. The sample consisted of 56 VHS students, study program of modeling design and building information. The instruments used are need for achievement tests and learning achievement tests with reliability coefficients of 0.916 and 0.671. To test the hypothesis using multiple variance analysis techniques and the Tukey test. The results show that the Google SketchUp application is more effective than the PowerPoint in the learning of building interior design. Students who classified with a high need for achievement earn higher learning achievement compared to the lower one. There is an interaction between the Google SketchUp application and the students’ need for achievement. For students who have a high need for motivation, using the Google SketchUp application is more effective than using PowerPoint Slides. On the other side, the students who have a low need for motivation, the use of the Google SketchUp application does not differ significantly compared to the use of PowerPoint Slides. This finding is very useful for vocational teachers as an effort to improve the learning process of building interior design. However, it is also possible that these findings can apply more broadly to student learning in other skills competencies in VHS. These findings contribute to the management of vocational education as an effort to implement VHS revitalization. Furthermore, it also can be used as a consideration by the Head of VHS and decision-makers in the Ministry of Education and Culture

TCP FIN flood attack pattern recognition on internet of things with rule based signature analysis

Focus of this research is Transmission Control Protocol (TCP) FIN flood attack pattern recognition in Internet of Things network using rule based signature analysis method. Dataset is created using three traffic scenarios: normal, attack and normal-attack. The process of identification and recognition of TCP FIN flood attack pattern is done by observing and analyzing packet's attributes from raw data (pcap format) through a feature extraction and feature selection processes. Further experiments were conducted using Snort as intrusion detection system (IDS). The evaluation results of the rate of confusion matrix detection against the Snort as IDS show the average percentage of the precision level