Wina Rachmawan, Irene Erlyn
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An Embedded System for applying High Performance Computing in Educational Learning Activity Wina Rachmawan, Irene Erlyn; Fahmi, Nurul; Widodo, Edi Wahyu; Huda, Samsul; Pamenang, M. Unggul; Roziqin, M. Choirur; W., Andri Permana; Sukaridhoto, Stritusta; Pramadihanto, Dadet
EMITTER International Journal of Engineering Technology Vol 4 No 1 (2016)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1553.613 KB) | DOI: 10.24003/emitter.v4i1.113

Abstract

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.
Spatio-Temporal Deforestation Measurement Using Automatic Clustering Wina Rachmawan, Irene Erlyn; Barakbah, Ali Ridho; Harsono, Tri
EMITTER International Journal of Engineering Technology Vol 4 No 1 (2016)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (613.557 KB) | DOI: 10.24003/emitter.v4i1.120

Abstract

Deforestation is one of the crucial issues in Indonesia. In 2012, deforestation rate in Indonesia reached 0.84 million hectares, exceeding Brazil. According to the 2009 Guinness World Records, Indonesia's deforestation rate was 1.8 million hectares per year between 2000 and 2005. An interesting view is the fact that Indonesia government denied the deforestation rate in those years and said that the rate was only 1.08 million hectares per year in 2000 and 2005. The different problem is on the technique how to deal with the deforestation rate. In this paper, we proposed a new approach for automatically identifying the deforestation area and measuring the deforestation rate. This approach involves differential image processing for detecting Spatio-temporal nature changes of deforestation. It consists series of important features extracted from multiband satellite images which are considered as the dataset of the research. These data are proceeded through the following stages: (1) Automatic clustering for multiband satellite images, (2) Reinforcement Programming to optimize K-Means clustering, (3) Automatic interpretation for deforestation areas, and (4) Deforestation measurement adjusting with elevation of the satellite. For experimental study, we applied our proposed approach to analyze and measure the deforestation in Mendawai, South Borneo. We utilized Landsat 7 to obtain the multiband images for that area from the year 2001 to 2013. Our proposed approach is able to identify the deforestation area and measure the rate. The experiment with our proposed approach made a temporal measurement for the area and showed the increasing deforestation size of the area 1.80 hectares during those years.