Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

All

ComEngApp : Computer Engineering and Applications Journal

comengapp
Website

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 318 Documents

9 10 11 12 13

Coronary Heart Disease Interpretation Based on Deep Neural Network Darmawahyuni, Annisa
Computer Engineering and Applications Journal Vol 8 No 1 (2019)
Publisher : Universitas Sriwijaya

Coronary heart disease (CHD) population increases every year with a significant number of deaths. Moreover, the mortality from coronary heart disease gets the highest prevalence in Indonesia at 1.5 percent. The misdiagnosis of coronary heart disease is a crucial fundamental that is the major factor that caused death. To prevent misdiagnosis of CHD, an intelligent system has been designed. This paper proposed a simulation which can be used to diagnose the coronary heart disease in better performance than the traditional diagnostic methods. Some researches have developed a system using conventional neural network or other machine learning algorithm, but the results are not a good performance. Based on a conventional neural network, deeper neural network (DNN) is proposed to our model in this work. As known as, the neural network is a supervised learning algorithm that good in the classification task. In DNN model, the implementation of binary classification was implemented to diagnose CHD present (representative “1”) or CHD absent (representative “0”). To help performance analysis using the UCI machine learning repository heart disease dataset, ROC Curve and its confusion matrix were implemented in this work. The overall predictive accuracy, sensitivity, and specificity acquired was 96%, 99%, 92%, respectively.

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

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.

Skin Lesion Classification Based on Convolutional Neural Networks Renny Amalia Pratiwi; Siti Nurmaini; Dian Palupi Rini
Computer Engineering and Applications Journal Vol 8 No 3 (2019)
Publisher : Universitas Sriwijaya

Melanoma causes the majority of skin cancer deaths. The population level of melanoma has increased over the past 30 years. It kills around 9.320 people in the US every year. Melanoma can often be found early, when it is most likely to be cured. Medical diagnoses using digital imaging with machine learning methods have become popular because of their ability to recognize patterns in digital images. Image diagnosis accuracy allows disease cured at an early stage. This paper proposes a simulation that can be used for early detection of skin cancer that can help dermatologists to distinguish melanomas from other pigmented lesions on the skin. Some researchers have developed a system using machine learning algorithms used to classify skin lesions from dermoscopy images of human skin. In this study, we proposed Convolutional Neural Network (CNN) to our model. CNN is very efficient for image processing because feature extractors can be optimized, applied to each feature image position. The results of skin lesion classification of benign nevi and melanoma based on CNN models produces high accuracy (area under the receiver operator characteristics (ROC) curve (AUC) is 92.59 %, sensitivity is 89.47%, specificity is 100.0%, precision is 100 % and F1 score is 94.44 %).

The Implementation of Deep Neural Networks Algorithm for Malware Classification Nurul Afifah; Deris Stiawan
Computer Engineering and Applications Journal Vol 8 No 3 (2019)
Publisher : Universitas Sriwijaya

Malware is very dangerous while attacked a device system. The device that can be attacked by malware is a Mobile Phone such an Android. Antivirus in the Android device is able to detect malware that has existed but antivirus has not been able to detect new malware that attacks an Android device. In this issue, malware detection techniques are needed that can grouping the files between malware or non-malware (benign) to improve the security system of Android devices. Deep Learning is the proposed method for solving problems in malware detection techniques. Deep Learning algorithm such as Deep Neural Network has succeeded in resolving the malware problem by producing an accuracy rate of 99.42%, precision level 99% and recall 99.4%.

Peat Land Fire Monitoring System Using Fuzzy Logic Algorithm Nyayu Husni Latifah; Masayu Annisah; Tresna Dewi
Computer Engineering and Applications Journal Vol 8 No 3 (2019)
Publisher : Universitas Sriwijaya

In this research, a fuzzy logic algorithm is implemented in a monitoring system for detecting the potential fires in peat land. The monitoring system in this research employs two sensors as the fuzzy inputs, i.e. TGS 2600 gas sensor and DHT11 temperature sensor. The outputs of the fuzzy logic are the specified conditions of motor activation (PWM) with 3600 rotations. The system is monitored through camera, which sends the monitoring result to android via web server. The result is sent when TGS 2600 and DHT11 sensors detect the determined gas concentration and surrounding temperature. Before sending the result, the rotating motor stops every five minutes to take the photograph of peat land location. The result shows that the algorithm used in this research has been successful in determining the condition of the peat lands correctly and therefore can be used as the early prevention of fires.

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

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.

Analyzing Co-Authorship Networks in Indonesian PTN-BH Institution Through Social Network Analysis Firdaus, Firdaus; Nurmaini, Siti; Kurniawan, Anggy Tyas; Darmawahyuni, Annisa; Naufal, Muhammad; Raflesia, Sarifah Putri; Lestarini, Dinda
Computer Engineering and Applications Journal Vol 14 No 1 (2025)
Publisher : Universitas Sriwijaya

This study involved an examination of bibliographic information from Indonesia. Our approach centered on utilizing social network analysis to explore the co-authorship relationships among Indonesian authors, focused on the co-authorship network within the context of authors affiliated with Indonesian state universities known as "PTN-BH," which specialize in higher education and legal studies. To conduct our analysis, we gathered publication data from the Scopus database, spanning a time frame from 1948 to 2020. The primary methodology entailed constructing a graph composed of nodes and edges, representing the co-authorship connections among these authors. By employing the Louvain method, we were able to identify prominent communities within this graph. We carried out a comprehensive analysis at both macro and micro levels, involving measurement techniques tailored to these perspectives. Through this approach, we revealed and examined the collaboration patterns among authors associated with PTN-BH institutions, as illuminated by the co-authorship network analysis.

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

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

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.

Accuracy Improvement of Incidence Level Detection Based on Electroencephalogram Using Fuzzy C-Means and Support Vector Machine Marwan Ramdhany Edy; S. Wahjuni; S. N. Neyman
Computer Engineering and Applications Journal Vol 8 No 3 (2019)
Publisher : Universitas Sriwijaya

Some jobs require that the concentration level be maintained for a long time during work time. Lack of sleep will in disruption of someone concentration level. To find out a human concentration level can be done by recording his/her brain waves. This research uses Electroencephalography (EEG) technology which functions to capture human brain waves. The focus of this study is to build a model of the detection system of a human concentration level. The research datasets are data from brain wave recording using Neurosky Mindwave Mobile which has extracted in 19 features. Data will then be labeled using cluster techniques namely Fuzzy C-Means to become data to be input into the classification process using Support Vector Machine (SVM). The classification results show an accuracy of 98.34%. That results show FCM can be used to automatically label EEG data properly.

Page 11 of 32 | Total Record : 318

9 10 11 12 13

Filter by Year

2012 2025

Filter By Issues

All Issue Vol. 14 No. 2 (2025) Vol. 14 No. 1 (2025) Vol 14 No 1 (2025) Vol 13 No 03 (2024) Vol. 13 No. 3 (2024) Vol. 13 No. 2 (2024) Vol 13 No 2 (2024) Vol 13 No 1 (2024) Vol. 13 No. 1 (2024) Vol. 12 No. 3 (2023) Vol 12 No 3 (2023) Vol 12 No 2 (2023) Vol. 12 No. 2 (2023) Vol. 12 No. 1 (2023) Vol 12 No 1 (2023) Vol 11 No 3 (2022) Vol. 11 No. 3 (2022) Vol 11 No 2 (2022) Vol. 11 No. 2 (2022) Vol 11 No 1 (2022) Vol. 11 No. 1 (2022) Vol 10 No 3 (2021) Vol 10 No 2 (2021) Vol 10 No 1 (2021) Vol 9 No 3 (2020) Vol 9 No 2 (2020) Vol 9 No 1 (2020) Vol 8 No 3 (2019) Vol 8 No 2 (2019) Vol 8 No 1 (2019) Vol 7 No 3 (2018) Vol 7 No 2 (2018) Vol 7 No 1 (2018) Vol 6 No 3 (2017) Vol. 6 No. 2 (2017) Vol 6 No 2 (2017) Vol 6 No 1 (2017) Vol. 6 No. 1 (2017) Vol. 5 No. 3 (2016) Vol 5 No 3 (2016) Vol 5 No 2 (2016) Vol 5 No 1 (2016) Vol 4 No 3 (2015) Vol 4 No 2 (2015) Vol 4 No 1 (2015) Vol 3 No 3 (2014) Vol 3 No 2 (2014) Vol 3 No 1 (2014) Vol. 2 No. 3 (2013) Vol 2 No 3 (2013) Vol 2 No 2 (2013) Vol. 2 No. 2 (2013) Vol. 2 No. 1 (2013) Vol 2 No 1 (2013) Vol 1 No 2 (2012) Vol. 1 No. 2 (2012) Vol 1 No 1 (2012) More Issue

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Siti Nurmaini

Contact Email
comengappjournal@unsri.ac.id

Phone
+6285268048092

Journal Mail Official
comengappjournal@unsri.ac.id

Editorial Address
Jurusan Sistem Komputer, Fakultas Ilmu Komputer, Universtas Sriwijaya, KampusUnsri Bukit Besar, Palembang

Location
Kab. ogan ilir,

Sumatera selatan

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Siti Nurmaini

Contact Email comengappjournal@unsri.ac.id

Phone +6285268048092

Journal Mail Official comengappjournal@unsri.ac.id

Editorial Address Jurusan Sistem Komputer, Fakultas Ilmu Komputer, Universtas Sriwijaya, KampusUnsri Bukit Besar, Palembang

Location Kab. ogan ilir, Sumatera selatan INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Siti Nurmaini

Contact Email
comengappjournal@unsri.ac.id

Phone
+6285268048092

Journal Mail Official
comengappjournal@unsri.ac.id

Editorial Address
Jurusan Sistem Komputer, Fakultas Ilmu Komputer, Universtas Sriwijaya, KampusUnsri Bukit Besar, Palembang

Location
Kab. ogan ilir,

Sumatera selatan

INDONESIA