HPC (High Performance Computing) has become more popular in the last few years. With the benefits on high computational power, HPC has impact on industry, scientific research and educational activities. Implementing HPC as a curriculum in universities could be consuming a lot of resources because well-known HPC system are using Personal Computer or Server. By using PC as the practical moduls it is need great resources and spaces. This paper presents an innovative high performance computing cluster system to support education learning activities in HPC course with small size, low cost, and yet powerful enough. In recent years, High Performance computing usually implanted in cluster computing and require high specification computer and expensive cost. It is not efficient applying High Performance Computing in Educational research activiry such as learning in Class. Therefore, our proposed system is created with inexpensive component by using Embedded System to make High Performance Computing applicable for leaning in the class. Students involved in the construction of embedded system, built clusters from basic embedded and network components, do benchmark performance, and implement simple parallel case using the cluster. In this research we performed evaluation of embedded systems comparing with i5 PC, the results of our embedded system performance of NAS benchmark are similar with i5 PCs. We also conducted surveys about student learning satisfaction that with embedded system students are able to learn about HPC from building the system until making an application that use HPC system.

Deforestation is still a major nature phenomenon in our society. For assessing deforestation effect, satellites remote sensing provides a fundamental data for observation. While new remote-sensing technologies are able to represent high-resolution forest mapping, the application is still limited only for detecting and mapping the deforestation area. In this paper, we proposed a new method for automatically extract features of Satellite Multispectral images for interpreting deforestation effect in the context of soil degradation. We proposed an idea to interpret reflected "substances (material)" of bare soil in deforested area in spectrum domain into human language. The objectives of this paper are to (1) recognize the deforestation activity automatically. (2) Identify deforestation causes and examines the deforestation effect based on deforestation causes. (3) Scrutinize deforestation effects on soil degradation. (4) Representing nature knowledge of deforestation effect in human language using semantic computing, to bring the clear, comprehensible knowledge even for people who are not familiar with forestry. As for the experimental study, Riau Tropical Forest has been selected as the study area, where the multispectral data was acquired by using Landsat 8 Satellite between 2013 and 2014; Where forest fire and logging activities are reported and detected.

No abstract

No abstract