JOIV : International Journal on Informatics Visualization
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
Articles
1,172 Documents
<![CDATA[Lookup Table Algorithm for Error Correction in Color Images ]]>
<![CDATA[Ruaa Alaadeen Abdulsattar]]>;
<![CDATA[Nada Hussein M. Ali]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 2, No 2 (2018) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.2.2.113
<![CDATA[Error correction and error detection techniques are often used in wireless transmission systems. A color image of type BMP is considered as an application of developed lookup table algorithms to detect and correct errors in these images. Decimal Matrix Code (DMC) and Hamming code (HC) techniques were integrated to compose Hybrid Matrix Code (HMC) to maximize the error detection and correction. The results obtained from HMC still have some error not corrected because the redundant bits added by Hamming codes to the data are considered inadequate, and it is suitable when the error rate is low for detection and correction processes. Besides, a Hamming code could not detect large burst error period, in addition, the have same values sometimes which lead to not detect the error and consequently increase the error ratio. The proposed algorithm LUT_CORR is presented to detect and correct errors in color images over noisy channels, the proposed algorithm depends on the parallel Cyclic Redundancy Code (CRC) method that's based on two algorithms: Sarwate and slicing By N algorithms. The LUT-CORR and the aforementioned algorithms were merged to correct errors in color images, the output results correct the corrupted images with a 100 % ratio almost. The above high correction ratio due to some unique values that the LUT-CORR algorithm have. The HMC and the proposed algorithm applied to different BMP images, the obtained results from LUT-CORR are compared to HMC for both Mean Square Error (MSE) and correction ratio. The outcome from the proposed algorithm shows a good performance and has a high correction ratio to retrieve the source BMP image.]]>
<![CDATA[Optimization of Digital Image Processing Method to Improve Smoke Opacity Meter Accuracy ]]>
<![CDATA[Dwi Sudarno Putra]]>;
<![CDATA[Donny Fernandez]]>;
<![CDATA[- Wagino]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 2, No 2 (2018) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.2.2.114
<![CDATA[One of the parameters of exhaust emission testing on diesel engines is the level of smoke opacity. If the opacity is high then the emission quality is bad. The instrument for measuring smoke opacity is called Smoke Opacity Meter. The commonly used basic concept to measure smoke density is by utilizing a light sensor (optical sensor). Development of Smoke Opacity Meter applies the concept of Digital Image Processing. Even though it has initially begun, the measurement result is yet as perfect as Optical Sensor Concept. Therefore, this paper describes on how to implement the Digital Image Processing Method in processing the smoke opacity video data.]]>
<![CDATA[An Analytical Approach for Decision-Making ]]>
<![CDATA[Sakshi Aggarwal]]>;
<![CDATA[Shrddha Sagar]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 2, No 3 (2018) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.2.3.123
<![CDATA[In this complex world, coping with daily problems is quite tedious. The more advancement in technology means more difficulties in decision-making process. Hence some analytical tools are needed to deal with improvement in decisions being made. A classic AHP model enables us to make efficient decision by reducing the complex issues. It takes multiple parameters into consideration. One of the area where decision-making is quite a tough job is Politics. Selection of the electoral party in any elections, be it Lok Sabha elections or Rajya Sabha elections, has been a matter of discussion for the voters as well as the media. The decisions are reflected when uncertainties are added in the opinions of the domain experts due to multiple parameters. In this paper we have proposed a model for rectifying the uncertainties using multi criteria decision analysis and analytic hierarchy process (AHP).]]>
<![CDATA[Sybil Node Detection in Mobile Wireless Sensor Networks Using Observer Nodes ]]>
<![CDATA[Mojtaba Jamshidi]]>;
<![CDATA[Milad Ranjbari]]>;
<![CDATA[Mehdi Esnaashari]]>;
<![CDATA[Nooruldeen Nasih Qader]]>;
<![CDATA[Mohammad Reza Meybodi]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 2, No 3 (2018) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.2.3.131
<![CDATA[Sybil attack is one of the well-known dangerous attacks against wireless sensor networks in which a malicious node attempts to propagate several fabricated identities. This attack significantly affects routing protocols and many network operations, including voting and data aggregation. The mobility of nodes in mobile wireless sensor networks makes it problematic to employ proposed Sybil node detection algorithms in static wireless sensor networks, including node positioning, RSSI-based, and neighbour cooperative algorithms. This paper proposes a dynamic, light-weight, and efficient algorithm to detect Sybil nodes in mobile wireless sensor networks. In the proposed algorithm, observer nodes exploit neighbouring information during different time periods to detect Sybil nodes. The proposed algorithm is implemented by J-SIM simulator and its performance is compared with other existing algorithm by conducting a set of experiments. Simulation results indicate that the proposed algorithm outperforms other existing methods regarding detection rate and false detection rate. Moreover, they also showed that the mean detection rate and false detection rate of the proposed algorithm are respectively 99% and less than 2%.]]>
<![CDATA[A Visualization Approach to Analyze Android Smartphone Data ]]>
<![CDATA[Nurul Adhlina Hani Roslee]]>;
<![CDATA[Nurul Hidayah bt Ab Rahman]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 2, No 3-2 (2018): The Diversity in Information Systems ]]>
Publisher : <![CDATA[Politeknik Negeri Padang ]]>
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DOI: 10.30630/joiv.2.3-2.137
<![CDATA[This study aims to design and develop an interactive system that can visualize evidence collected from Android smartphone data. This project is developing to support forensic investigator in investigating the security incidents particularly involving Android smartphone forensic data. The used of smartphone in crime was widely recognized. Several types of personnel information are stored in their smartphones. When the investigator analyses the image data of the smartphone, the investigator can know the behaviour of the smartphone’s owner and his social relationship with other people. The analysis of smartphone forensic data is cover in mobile device forensic. Mobile device forensics is a branch of digital forensics relating to recovery of digital evidence from a mobile device under forensically sound condition. The digital investigation model used in this project is the model proposed by United States National Institute of Justice (NIJ) which consists four phases, which are collection phase, examination phase, analysis phase and presentation phase. This project related with analysis phase and presentation phase only. This paper introduces Visroid, a new tool that provides a suite of visualization for Android smartphone data.]]>
<![CDATA[Exploratory Study of Kohonen Network for Human Health State Classification ]]>
<![CDATA[Hamijah Mohd Rahman]]>;
<![CDATA[Nureize Arbaiy]]>;
<![CDATA[Muhammad Shukeri Che Lah]]>;
<![CDATA[Norlida Hassan]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 2, No 3-2 (2018): The Diversity in Information Systems ]]>
Publisher : <![CDATA[Politeknik Negeri Padang ]]>
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DOI: 10.30630/joiv.2.3-2.143
<![CDATA[Kohonen Network is an unsupervised learning which forms clusters from patterns that share common features and group similar patterns together. This network are commonly uses grids of artificial neurons which connected to all the inputs. This paper presents an exploratory study of Kohonen Neural Network to classify human health state. Neural Connection tool is used to generate the result based on Kohonen learning algorithm. Procedural steps are provided to assist the implementation of the Kohonen Network. The result shows that side 2 is more appropriate for this problem with efficient learning rate 1.0. It gives good distribution for training and test patterns. Study to the variation of dataset’s size will be considered in the near future to evaluate the performance of the network.]]>
<![CDATA[WLAN Security: Threats And Countermeasures ]]>
<![CDATA[Suroto Suroto]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 2, No 4 (2018) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.2.4.133
<![CDATA[A wireless local area Network (WLAN) is being widely recognized as a viable cost effective general purpose solution in providing high speed real time access to information. With a WLAN, users can gain access to shared information without being bound to fixed plug-in-point. WLAN transmit and receive data over the air and thus collectively combine data connectivity with ease of mobility. WLAN provides wireless access to multi location enterprises, small and medium enterprises. It can replace wired LAN or simply be used as extension of wired infrastructure. Besides all these advantages WLAN are also facing major problems of security.So security is the aspect where most of the researchers are working. Following are the major objective of our study : i) To study the various Vulnerabilities and attacks on WLAN and their solutions. ii) To study the some of the exiting security methods used for securing WLAN and explore the possibility of improvements in the same. Our conclusion that WLAN security is not easy, and it is constantly changing. They expose the network to a new group of hackers. All businesses need to determine their security requirements based on the application using the WLAN. Goal so that a WLAN is as protected as Wired LAN.]]>
<![CDATA[A new approach towards Image retrieval using texture statistical methods ]]>
<![CDATA[R Tamilkodi]]>;
<![CDATA[G. Rosline Nesa Kumari]]>;
<![CDATA[S. Maruthu Perumal]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 2, No 1 (2018) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.2.1.60
<![CDATA[Texture is a possession that represents the facade and arrangement of an image. Image textures are intricate ocular patterns serene of entities or regions with sub-patterns with the kind of brightness, color, outline, dimension, and etc.This article proposes a new method for texture characterization by using statistical methods (TCUSM). In this proposed method (TCUSM) the features are obtained from energy, entropy, contrast and homogeneity. In an image, each one pixel is enclosed by 8 nearest pixels. The confined in turn for a pixel can be extracted from a neighbourhood of 3x3 pixels, which represents the fewest absolute unit. We used four vector angles 0, 45, 90,135 to carry out the experimentation with the query image. A total of 16 texture values are calculated per unit. Compute the feature vectors for the query image by calculating texture unit and the resultant value is compared with the image database. The retrieval result shows that the performance using Canberra distance has achieved higher performance. ]]>
<![CDATA[Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia ]]>
<![CDATA[Mohammad Nur Shodiq]]>;
<![CDATA[Dedy Hidayat Kusuma]]>;
<![CDATA[Mirza Ghulam Rifqi]]>;
<![CDATA[Ali Ridho Barakbah]]>;
<![CDATA[Tri Harsono]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 2, No 1 (2018) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.2.1.106
<![CDATA[A model of artiï¬cial neural networks (ANNs) is presented in this paper to predict aftershock during the next five days after an earthquake occurrence in selected cluster of Indonesia with magnitude equal or larger than given threshold. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey’s (USGS). Six clusters was an optimal number of cluster base-on cluster analysis implementing Valley Tracing and Hill Climbing algorithm, while Hierarchical K-means was applied for datasets clustering. A quality evaluation was then conducted to measure the proposed model performance for two different thresholds. The experimental result shows that the model gave better performance for predicting an aftershock occurrence that equal or larger than 6 Richter’s scale magnitude.]]>
<![CDATA[Application of Genetic Algorithm and Personal Informatics in Stock Market ]]>
<![CDATA[Khulood Albeladi]]>;
<![CDATA[Salha Abdullah]]>
<![CDATA[ JOIV : International Journal on Informatics Visualization Vol 2, No 2 (2018) ]]>
Publisher : <![CDATA[Society of Visual Informatics ]]>
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DOI: 10.30630/joiv.2.2.115
<![CDATA[The financial market is extremely attractive since it moves trillion dollars per year. Many investors have been exploring ways to predict future prices by using different types of algorithms that use fundamental analysis and technical analysis. Many professional speculators or amateurs had been analysing the price movement of some financial assets using these algorithms. The use of genetic algorithms, neural networks, genetic programming combined with these tools in an attempt to find a profitable solution is very common. This study presents a prototype that utilizes genetic algorithms (GAs) and personal informatics system (PI) for short-term stock index forecast. The prototype works according to the following steps. Firstly, a collection of input variables is defined through technical data analysis. Secondly, GA is applied to determine an optimal set of input variables for a one-day forecast. Â The data is gathered from the Saudi Stock Exchange as being the target market. Thirdly, PI is utilised to create a smart environment, which enables visualisation of stock prices. The outcome indicates that this approach of forecasting the stock price is positive. The highest accuracy obtained is 64.67% and the lowest one is 48.06%.]]>
