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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 6 Documents
 1 
Search results for , issue "Vol 2 No 2 (2013)" : 6 Documents clear
An Analysis of Adaptive Approach for Document Binarization Reza Firsandaya Malik; Saparudin -; Intan Septyliana
Computer Engineering and Applications Journal Vol 2 No 2 (2013)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (807.61 KB) | DOI: 10.18495/comengapp.v2i2.21

Abstract

Abstract Binarization is an initial step in document image analysis for differentiate text area from background. Determination of binarization technique is really important to retrieve all the text information especially from degraded document image. This paper explains about adaptive binarization using Gatos’s method. Gatos’s method is doing preprocessing, foreground estimation using Sauvola’s method, background estimation, upsampling, final thresholding and postprocessing. In this paper, Sauvola’s method is final thresholding from Wiener filter image result and source image, and count F-Measure from both of these binary image results. By using optimum constant value on k value, n local window, Ksw and Ksw1, Gatos’s method can produced binary image better than Sauvola’s method based on F-Measure value. Sauvola’s method produces average value F=84,62%, Sauvola’s method with Wiener filter produces average value F=99.06% and Gatos’s method produces average value F=99,43%. Keyword : Degraded Document Image, Adaptive Approcah for Binarization, Gatos’s  Method, Sauvola’s MethodDOI: 10.18495/comengapp.22.185194
An Immune Based Patient Anomaly Detection using RFID Technology Sri Listia Rosa; Siti Mariyam Shamsuddin; Evizal Evizal
Computer Engineering and Applications Journal Vol 2 No 2 (2013)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (596.823 KB) | DOI: 10.18495/comengapp.v2i2.22

Abstract

Online product reviews is considered as a major informative resource which is useful for both customers and manufacturers. The online reviews are unstructured-free-texts in natural language form. The task of manually scanning through huge volume of review is very tedious and time consuming. Therefore it is needed to automatically process the online reviews and provide the necessary information in a suitable form. In this paper, we dedicate our work to the task of classifying the reviews based on the opinion, i.e. positive or negative opinion. This paper mainly addresses using ensemble approach of Support Vector Machine (SVM) for opinion mining. Ensemble classifier was examined for feature based product review dataset for three different products. We showed that proposed ensemble of Support Vector Machine is superior to individual baseline approach for opinion mining in terms of error rate and Receiver operating characteristics Curve. Â Key words: Opinion, Classification, Machine Learning.
Codes Correcting and Simultaneously Detecting Solid Burst Errors P. K. Das
Computer Engineering and Applications Journal Vol 2 No 2 (2013)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (310.953 KB) | DOI: 10.18495/comengapp.v2i2.23

Abstract

Digital images are foremost source for information transfer. Due to advancement of the technology, images are now not treated as reliable source of information. Digital images can be edited according to the need. Adding and deleting content from an image is most easiest and popular way of creating image forgery, which is known as copy-move forgery. Digital Image Forensics is the field that deals with the authenticity of the images. Digital image forensics checks the integrity of the images by detecting various forgeries. In order to hide the traces of copy-move forgery there are editing operations like rotation, scaling, JPEG compression, Gaussian noise called as attacks, which are performed on the copied part of the image before pasting. Till now these attacks are not detected by the single method. The novel approach is proposed to detect image forgery by copy-move under above attacks by combining block-based and keypoint-based method. Keywords: Digital image forensics, copy-move forgery, passive blind approach, keypoint-based, block-based methods
Proposed Developements of Blind Signature Scheme Based on ECC F. Amounas; E. H. El Kinani
Computer Engineering and Applications Journal Vol 2 No 2 (2013)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (328.965 KB) | DOI: 10.18495/comengapp.v2i2.24

Abstract

Detecting and gaining clues from the crime scene plays a major role in the process of investigation. Now a days with increase of cybercrime and fraud on the internet, digital forensics gaining importance. It needs to collect information the events happened at crime place. This paper deals with event reconstruction process which is useful for digital crime scene. The process of reconstruction is based on crime event and event characteristics. It also furnishes crime rate guess using some semi-formal techniques. Keyword: Digital Investigation, Digital Forensics, Events, Attack Trees, Reconstruction
Automatic Iranian Vehicle License Plate Recognition System Based on Support Vector Machine (SVM) Algorithms Mahdi Aghaie; Fatemeh Shokri; Meisam Yadolah Zade Tabari
Computer Engineering and Applications Journal Vol 2 No 2 (2013)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (900.924 KB) | DOI: 10.18495/comengapp.v2i2.25

Abstract

In the emerging trends the pervasive nature across the computing environment shows that the system is platform independent and device independent. The system development is designed with the help of Structured Query Language and middleware infrastructure that are used to collect the information from various nodes. An essential feature of this proposed middleware architecture suites the device independent as the major supporting capability to the system. This facilitates to add new device types in the system feels easy through the use of device self-description. It mainly focuses on the issues related to the heterogeneity of the different devices composing a pervasive system: This aspect is investigated both at data management and at physical integration levels. Using the nontrivial approach aims at handling the related issues are resolved with the corresponding solution. Keyword: Perla, Cloud Monitoring, Middleware, Declarative Language
On Constructing Static Evaluation Function using Temporal Difference Learning Samuel Choi Ping Man
Computer Engineering and Applications Journal Vol 2 No 2 (2013)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (874.34 KB) | DOI: 10.18495/comengapp.v2i2.26

Abstract

An Outlier is a data point which is significantly different from the remaining data points. Outlier is also referred as discordant, deviants and abnormalities. Outliers may have a particular interest, such as credit card fraud detection, where outliers indicate fraudulent activity. Thus, outlier detection analysis is an interesting data mining task, referred to as outlier analysis. Detecting outliers efficiently from dataset is an important task in many fields like Credit card Fraud, Medicine, Law enforcement, Earth Sciences etc. Many methods are available to identify outliers in numerical dataset. But there exist limited number of methods are available for categorical and mixed attribute datasets. In the proposed work, a novel outlier detection method is proposed. This proposed method finds anomalies based on each record’s “multi attribute outlier factor through correlation” score and it has great intuitive appeal. This algorithm utilizes the frequency of each value in categorical part of the dataset and correlation factor of each record with mean record of the entire dataset. This proposed method used Attribute Value Frequency score (AVF score) concept for categorical part. Results of the proposed method are compared with existing methods. The Bank data (Mixed) is used for experiments in this paper which is taken from UCI machine learning repository. Keyword: Outlier, Mixed Attribute Datasets, Attribute Value Frequency Score

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