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ComEngApp : Computer Engineering and Applications Journal
Published by Universitas Sriwijaya
ISSN : 22524274     EISSN : 22525459     DOI : 10.18495
Core Subject : Science, Engineering,
Computer Science & IT Engineering
ComEngApp-Journal (Collaboration between University of Sriwijaya, Kirklareli University and IAES) is an international forum for scientists and engineers involved in all aspects of computer engineering and technology to publish high quality and refereed papers. This Journal is an open access journal that provides online publication (three times a year) of articles in all areas of the subject in computer engineering and application. ComEngApp-Journal wishes to provide good chances for academic and industry professionals to discuss recent progress in various areas of computer science and computer engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 8 No 2 (2019)" : 5 Documents clear
PCA-Based on Feature Extraction and Compressed Sensing for Dimensionality Reduction Desiani, Anita; Maiyanti, Sri Indra; Miraswan, Kandak Januar; Arhami, muhammad
Computer Engineering and Applications Journal Vol 8 No 2 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (334.541 KB) | DOI: 10.18495/comengapp.v8i2.281

Abstract

Compressive sensing reduces the number of samples required to achieve acceptable reconstruction for medical diagnostics, therefore this research will implement dimensional reduction algorithms through compressed sensing for electrocardiogram signals (EKG). dimensional reduction is performed based on the fact that ECG signals can be reconstructed with linear combination coefficients with a bumpy base of small measurements with high accuracy. This study will use PCA for feature extraction on ECG signals. The data used are the ECG patient records on the website page www.physionet.org as many as 1200 with each attribute as many as 256 attributes. The total data dimension used is 1200x256, which means the data has 1200 rows and has as many as 256 columns. To show the accuracy of the dimensional reduction result, so it is performed classification on data using KNN and Naive Bayes. The classification results show that KKN can classify well with 84,02% accuracy rate and the Naive Bayes accuracy is 65,78%. for 100 dimensions The conclusion is those dimensional reductions for ECG data that have large dimensions, it still able to provide valid information like it uses the original data. Principle Component Analysis is a good method for reducing data dimensions by selecting certain features, so the dimensions of the data become smaller but still able to provide good accuracy to the reader.
Trough OpenMP Platform for Reducing Computational Time Cost in Underwater Landslide Simulation on Inclined Bottom Putu Harry Gunawan; Fadhil Lobma
Computer Engineering and Applications Journal Vol 8 No 2 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (472.589 KB) | DOI: 10.18495/comengapp.v8i2.289

Abstract

Simulation of underwater landslide becomes important, since underwater landslide phenomena is very dangerous in real life. One of the enormous disasters caused by this phenomena can be a Tsunami. Computer simulation of underwater landslide can reduce cost of time and money from conventional simulation (using laboratory). However, to obtain high resolution of computer simulation, large discrete points should be computed. In this paper, the numerical simulation of underwater landslide using two-layers shallow water equations (SWE) and OpenMP platform is elaborated. Here, the finite volume method framework using upwinding dispersive correction hydrostatic reconstruction (UDCHR) scheme is used. The results of numerical simulation is in a good agreement with the numerical simulation using Nasa-Vof2d numerical scheme. In parallel performance, speedup and efficiency of this numerical simulation are observed 2.8 times and 76% respectively at t=0.8 s final time simulation.
Design and Implementation of Temperature Control for a Mini-chamber using Self-Tuning PID Controller Muhammad Aziz Muslim; M. Rony Hidayatullah
Computer Engineering and Applications Journal Vol 8 No 2 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (566.348 KB) | DOI: 10.18495/comengapp.v8i2.298

Abstract

Despite its popularity in industrial application, PID controller suffers parameters setting difficulty due to set point change, disturbance, and ageing. This paper proposed Self-tuning PID controller using Dahlin method for temperature control of a laboratory scale mini-chamber. Experimental results show that the proposed controller has better performance compared to the conventional PID controller in term of rise time and settling time. It also shows that the algorithm can compensate the changing environment and robust toward the existence of disturbance.
An Approach to Improve the Live Migration Using Asynchronized Cache and Prioritized IP Packets Keyvan Mohebbi; Mohammad Reza Moslehi Takantapeh
Computer Engineering and Applications Journal Vol 8 No 2 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (422.61 KB) | DOI: 10.18495/comengapp.v8i2.302

Abstract

The live migration of a virtual machine is a method of moving virtual machines across hosts within a virtualized data center. Two main parameters should be considered for evaluation of live migration; total duration, and downtime of migration. This paper focuses on optimization of live migration in Xen environment where memory pages are dirtied rapidly. An approach is proposed to manage dirty pages during migration in the cache and prioritize the packets at the network level. According to the evaluations, when the system is under heavy workload or it is running within a stress tool, the virtual machines are intensively writing. The proposed approach outperforms the default method in terms of number of transferred pages, total migration time, and downtime. Experimental results showed that by increasing workload, the proposed approach reduced the number of sent pages by 47.4%, total migration time by 10%, and the downtime by 27.7% in live migration.
Book Recommender System Using Genetic Algorithm and Association Rule Mining Hani Febri Mustika; Aina Musdholifah
Computer Engineering and Applications Journal Vol 8 No 2 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (336.683 KB) | DOI: 10.18495/comengapp.v8i2.305

Abstract

Recommender system aims to provide on something that likely most suitable and attractive for users. Many researches on the book recommender system for library have already been done. One of them used association rule mining. However, the system was not optimal in providing recommendations that appropriate to the user's preferences and achieving the goal of recommender system. This research proposed a book recommender system for the library that optimizes association rule mining using genetic algorithm. Data used in this research has taken from Yogyakarta City Library during 2015 until 2016. The experimental results of the association rule mining study show that 0.01 for the greatest value of minimum support and 0.4359 for the average confidence value due to a lot of data and uneven distribution of data. Furthermore, other results are 0.499471 for the average of Laplace value, 30.7527 for the average of lift value and 1.91534252 for the average of conviction value, which those values indicate that rules have good enough level of confidence, quite interesting and dependent which indicates existing relation between antecedent and consequent. Optimization using genetic algorithm requires longer execution time, but it was able to produce book recommendations better than only using association rule mining. In Addition, the system got 77.5% for achieving the goal of recommender system, namely relevance, novelty, serendipity and increasing recommendation diversity.

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