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The researcher of Asian Composites Manufacturing Sdn. Bhd. (ACM) are currently doing a research on how to reduce waste within all processes in an activity duration of the production, in order to increase profit for the company. Currently, the duration of the activity processes was recorded by manual procedure in paper-based form which are prone to many problems. At the same time, it lacks in real-time feedback between the researcher and the staff of ACM. To overcome these problems, the Online Activity Duration Management System (OADMS) is proposed. This system will computerize the manual procedure. It is provided in a web-based environment. The OADMS system is developed by using the waterfall system development methodology, while the programming languages are using Hypertext Markup Language (HTML), Hypertext Preprocessor (PHP), Structured Query Language (SQL) and JavaScript. The OADMS provided a systematic activity duration management for staff of ACM and eased the works of researcher in analyzing the processes and providing feedbacks for improvements of the activity duration. By, providing a computerized solution, it is concluded that the activity duration management is more systematic and precise.

The most important experiences we discovered from several disasters are that cellular networks were vulnerable, and the loss of the communication system may have a catastrophic consequence. Mobile ad-hoc networks (MANETs) play a significant role in the construction of campus, resident, battlefield and search/rescue region. MANET is an appropriate network for supporting a communication where is no permanent infrastructure. MANET is an effective network that uses to establishing urgent communication between rescue members in critical situations like, disaster or natural calamities. The sending and receiving data in MANET is depending on the routing protocols to adapt the dynamic topology and maintain the routing information. Consequently, This paper evaluates the performance of three routing protocols in MANET: ad-hoc on-demand distance vector (AODV), destination sequenced distance vector (DSDV), and ad-hoc on-demand multipath distance vector (AOMDV). These protocols are inherent from different types of routing protocols: single-path, multi-path, reactive and proactive mechanisms. The NS2 simulator is utilized to evaluate the quality of these protocols. Several metrics are used to assess the performance of these protocols such: packet delivery ratio (PDR), packet loss ratios (PLR), throughput (TP), and end-to-end delay (E2E delay). The outcomes reveal the AOMDV is the most suitable protocol for time-critical events of search and rescue missions.

Students are one of the substances that need to be considered in relation to the world of education, because students are translators of the dynamics of science, and carry out the task of exploring that knowledge. As a subject with potential and, at the same time, objects in their activities and creativity, students are expected to be able to develop their qualities. The quality can be seen from the academic achievements achieved, which are evidence of the effort earned by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria to be declared eligible to continue their studies, the student is declared to be not graduating on time or even dropout (DO). The high number of students not graduating on time or dropouts at higher institutions can be minimized by the policies of higher institutions by directing and detecting at-risk students in the early stages of education. Therefore, in this paper, we present the use of Fuzzy Soft Set Classification (FSSC), which is based on the Fuzzy Soft set theory to predict student graduation. The 2068 dataset was taken from the Directorate of Information Systems, Ahmad Dahlan University. The results showed that the FSSC reached up to 0.893292 in terms of accuracy. So, it is expected to be able to detect students at risk in the early stages of education so that higher education can minimize students not graduating on time or dropout by providing appropriate treatment and designing strategic programs.

Taxi reservation system is developed for the Association of Taxi Companies Batu Pahat. The system is developed based website aims to facilitate users taxi company to make a reservation. Application of this book will be approved by the administrator. The system administrator can update the taxi booking application and issue a report on consumer booking system. This system was designed and developed using the prototype model approach has four phases: Planning, Analysis, Design and Implementation. Interfaces are used to develop this system is Adobe Dreamweaver CS6. The programming language used is PHP (Hypertext Pre-processor) and XAMMP used as a web server. With the taxi booking system, consumers will be able to make reservations online as well as make it easier and save time. Factors colour selection, concept presentations, text and colours were chosen to suit the theme of the show and the ease of use and feel the comfort in using this system.

Lensalyza Photography Studio is an organization that provides photography reservation service. This company located at strategic area that is students and publics gathered area that they visit to reserve photography like wedding event, engagement, convocation, family portrait and so on. Besides, this company also provides photography reservation service through telephone, social page like Facebook and direct reservation with company. However, Lensalyza studio’s reservation is done manually whereby operators record customer’s information by manual. There are many problems occurred in which overturn of reservation data and manual report generation through file system. The main purpose of this project is to automatize the manual system to web system. In conclusion, this system able to help publics reserve photography easily, faster and accurate and some more facilitate operator’s works in terms of file management system.

Businesses that are promoted through social media Facebook are one of the smartest ways to increase sales of a product. Sweet8Bakery is a business operated by a private cake seller who runs a small cake business order. Sweet8Bakery seller also took this initiative as one of the ways to attract buyers. However, there are still some problems encountered such as booking information from customers still recorded through the manual method of bookkeeping. Therefore, the purpose of this project is to design a reservation system application to support the online system so that customer booking information can be managed properly. The development methodology for this system is the waterfall methodology in which it will be developed in stages and parallel to the phases found in the waterfall model. Adobe Dreamweaver CS6 is a web developer used with PHP programming language and is supported by Xampp’s web server and MySQL database. The development of this system is to automate the manual system to a web-based system. In conclusion, at the end of this project a Sweet8Bakery reservation system will be built. With this system, it can help Sweet8Bakery customers to make cake reservations easier.

Most Stationery Inventory Management is a system that is built to facilitate the outgoing management of the stationery stock. The main objective of developing this system is to help inventory operator of Most Stationery shop to manage their inventory in a systematic and efficient way. Through the interview, the problem found is stationery stock inventory management still using the manual method to record stationery stock from the stored record file. All the stationeries are divided into file categories in which the suppliers and tools are based on invoice or bills provided. Besides, daily sales report cannot be generated due to the manual sales record produced. Therefore, profit report cannot be generated at the end of month. In a whole, the developed inventory management system can speed up the process of recoding the inventory information to generate the required report by the operators.

This Information Technology Club Management System is built to assist the IT staff of the Information Technology team to manage the club systematically and more effectively. The objective of this system is to develop and develop an online information technology club management system. In addition, this system is developed to generate more systematic and effective activities and programs. This system also helps not only members of the staff but also ordinary members, as well as ordinary members can interact and communicate with members of the post. The system is also able to provide systematic and efficient working advantages. Use of e-mail, as well as short messaging services, are also used in this system to facilitate the affairs of the power of affiliation. To achieve that objective, developers use a prototype method to develop and improve as a key to this system's revolt. In conclusion, this system reduces data filing on paper to be recorded using a computerized system. System developers use Adobe Dreamweaver and MYSQL applications and other applications like Adobe Photoshop to develop this system.

The recent progress in the digital multimedia technologies has offered many facilities in the transmission, reproduction and manipulation of data. However, this advance has brought the problem such as copyright protection for content providers. Watermarking is one of the proposed solutions for copyright protection of multimedia. Most of the content provider use Photoshop to create a watermark photo, but Photoshop involve many procedures to complete a watermarked image. In addition, watermark image using Photoshop easily destroy by attacker where attacker can modifies the image to remove the watermark from the image. Hence, it has become a tough task to protect copyright of an individual’s creation. Thus, i-Mark is developed to solve the problem to protect the watermarked image. An invisible watermark embeds an imperceptible signal into data such as image, which indicates whether or not the content is copyrighted. By using i-Mark, invisible watermarking was provided where steganography method was implemented for users to encrypt message into an image. Hence, the image is protected from been easily modifies or copy by others. Meanwhile, i-Mark allow user to store their invisible watermarked image in database and can be view later. In addition, i-Mark also can made a visible watermark where user can adding text on the cover of image more convenient than Photoshop. In conclusion, i-Mark provides users to watermark their image either visible watermark or invisible watermark. Hence, throughout the project, user may use the system user friendly compare to Photoshop.

Teaching performance evaluation process in higher learning institutions (HLI) is of the most importance in building a higher education policy which purposes for teaching improvement and administrative decision making. Several issues have been raised such as 1) instrument characteristic used to detect values is not sufficient to handle subjective evaluation criteria’s and, 2) the most techniques used in handling survey data is statistical method. In conventional practice, ordinal Likert scales used for marking schema and statistical techniques are implemented for analyzing the data. In order to manage the issues, this research presents a multi-criteria decision making methodology, called analytic hierarchical process (AHP) to evaluate HLI instructors teaching performance. As a well known decision making approach, AHP can systematically cope with highly subjective problems in social system. An AHP implementation framework for teaching performance evaluation will use the possible internal factors; preparation, organization, delivery and effectiveness that are evaluated as teaching evaluation weight. The future work is also suggested, this evaluation system should be implemented in real time situations. It becomes the best platform to evaluate teaching performance and categorize instructors into appropriate bands.

Web Based Management System of Enactus Malaysia National Cup (E-EMNC) is event management system that specially designed for the uses of Enactus Malaysia National Cup. Without a proper event management system, Enactus Malaysia National Cup cannot be perfect enough due to several outburst issues such as miss update of payment, long queue at registration counter, misunderstand of information and others. Thus, this event management system was developed to overcome most of the problem that occurs before, during and after the events. Related objectives are stated to solve the problem. System Development Life Cycle Waterfall model is used as the methodology to develop this system. This system is developed by using PHP as server-side scripting and Bootstrap as front end framework. The prototype of the system was considered success because it could navigate student leader to complete submit the report by shown the flow of system. This system is expected to lighten the burden of event committees and educated team leader on filing document through internet.

This research presents a Performance Visualized Assessment (PVA) archetype via which the performance of autonomous Unmanned Aerial Vehicles (UAV) or drone framework can be scrutinized and evaluated. The PVA is a performance-based assessment method that appropriately fits in assessing the flight point of the UAV in an indoor situation and outside the requirement of GPS aid. The PVA prototype came up with is comprised of Visualized Mission Grid (VMG) and Chi-square (CSI) modules. The VMG maps viewable field information of the surrounding including the situation of the route mission. In addition, the utilization of restriction and optical stream indoor direction monitoring and trailing is used in the CSI. The PVA follows and evaluates the execution of the UAV autonomous operations by checking and envisioning the direction and the UAV action amid route missions. The implementation outcomes demonstrate that the PVA is able to precisely assess and envision the execution quality of three aerial missions.

Measuring semantic similarity between terms is an important step in information retrieval and information integration which requires semantic content matching. Semantic similarity has attracted great concern for a long time in artificial intelligence, psychology and cognitive science. This paper contains a review of the state of art approaches including structure based approach, information content based approach, and feature based approach and hybrid approach. We also discussed similarity according to their advantages, disadvantages and issues related to multiple ontology especially on method in features based approach.

Recommendation systems or recommender system (RSs) is one of the hottest topics nowadays, which is widely utilized to predict an item to the end-user based on his/her preferences primary. Recommendation systems applied in many areas mainly in commercial applications. This work aims to collect evidence of utilizing social network information between users to enhance the quality of traditional recommendation system. It provides an overview of traditional and modern approaches used by RSs such as collaborative filter (CF) approach, content-based (CB) approach, and hybrid filter approach. CF is one of the most famous traditional approaches in RSs, which is facing many limitations due to the lack of information available during a performance such as Cold start, Sparsity and Shilling attack. Additionally, this content focused on the role of incorporating a trust relationship from the social network to enhance the weaknesses of CF and achieve better quality in the recommendation process. Trust-aware Recommendation Systems (TaRSs) is a modern approach proposed to overcome the limitations of CF recommendation system in a social network. The trust relationship between users can boost and enhance CF limitations. Many researchers are focusing on trust in the recommendation system but fewer works are highlighting the role of trust in the recommendation system. In the end, limitations, and open issues of the current picture of the recommendation system come across.

Online learning is a common tool among university students that depends solely on the Internet medium. However, the lack of monitoring during examination sessions has resulted in the rising cases of online cheating among the students. As such, better mechanisms to replace the use of password authentication are needed to enhance the level of security for the online learning assessment. Keystroke dynamics is one of the most popular methods used, because it does not require any extra devices other than the keyboard. In addition, it can be used as a tool to secure online learning system, although the username and password are known to the user. This paper aimed to comprehensively investigate authentication systems, specifically the keystroke dynamic authentication system for current applications. Besides, the applications and the keystroke benchmarking dataset were also reviewed to have a better insight about their security and usability and to improve the level of security in the current online learning envi...

Back Propagation (BP) algorithm is one of the oldest learning techniques used by artificial neural networks (ANN). It has successfully been implemented in various practical problems. However, the algorithm still faces some drawbacks such as getting easily stuck at local minima and needs longer time to converge on an acceptable solution. Recently, the introduction of Second Order Methods has shown a significant improvement on the learning in BP but it still has some drawbacks such as slow convergence and complexity. To overcome these limitations, this research proposed a modified approach for BP by introducing the Conjugate Gradient and Quasi-Newton which were Second Order methods together with ‘gain’ parameter. The performances of the proposed approach is evaluated in terms of lowest number of epochs, lowest CPU time and highest accuracy on five benchmark classification datasets such as Glass, Horse, 7Bit Parity, Indian Liver Patient and Lung Cancer. The results show that the proposed Second Order methods with ‘gain’ performed better than the BP algorithm.

Radio Frequency Identification (RFID) is widely used to track and trace objects in supply chain management. However, massive uncertain data produced by RFID readers are not suitable for directly use in RFID applications. This is due to repetitive readings which are unnecessary because it contains only the same information. Thus, an approach to remove repetitive readings in supply chain is paramount to minimize massive data storage that could affect query performances. We propose a simplified approach, which is suitable for a wide range of application scenarios. Experimental evaluations show that our approach is effective and efficient in terms of the removing duplicate readings and compressing the massive data significantly.

This paper discusses the research framework related to the scenario of dragon fruit planting prediction based on moisture rainfall and temperature. The modeling that is used is season forecasting index, evolution algorithm called, Fuzzy MCDM and Fuzzy Inference System. The purpose of this paper is to design a research framework that produces a new hybrid algorithm and forecasting prediction. In this framework, this research combines one part of the evolutionary algorithm Grammatical Evolution (using individual representations that can be used to evolve the program) with the Multiple Criteria Decision Making (MCDM) — Fuzzy method resulted from a combination of algorithms and methods formed a new hybrid algorithm. The result of accuracy for forecasting has not implemented. The focus of this research is the scenario of the research framework.

Osteoarthritis is a chronic joint disease that occurs when the protective cartilage that cushions the ends of bones wears down over time and fails to be repaired. The common form of the disease is knee osteoarthritis while it can affect all body parts with joints, such as hands, ankles, hips, and spine. The major cause of knee osteoarthritis is the continuous depletion of its cartilage. During the diagnosis, machine learning is used because early prevention is necessary for proper treatment. This study, therefore, considers classification methods of Support Vector Machine (SVM) and clustering methods using fuzzy clusterings such as Fuzzy C-Means (FCM), Fuzzy Possibilistic C-Means (FPCM), and Fuzzy Possibilistic C-Means based on kernel (FPCMK) to analyze of knee osteoarthritis. SVM is a machine learning technique that works based on the principle of structural risk minimization (SRM) to obtain the best hyperplane to separate two or more classes in input space. Otherwise, the fuzzy clustering is to determine the value of a distance and to know and measure the similarity of each object to be observed. FPCMK uses the kernel Radial Base Function (RBF) in the fuzzy clustering method. The kernel function is applicable for handling non-separable data problems. This method will be compared to the level of the measured parameter; their accuracy, recall, precision, and f1 score. The greatest level of accuracy is generated from SVM with an accuracy value of 86.7%, then followed by FPCMK with an accuracy value of 85.5%.

A Web server log files contain an entire record of the user’s browsing history such as referrer, date and time access, path, operating system (OS), browser and IP address. User navigation pattern discovery involves learning of user’s browsing behaviour to gain the pattern from web server log file. This paper emphasizes on identifying user navigation pattern from web server log file data of iLearn portal. The study implements the framework for user navigation including phases of acquisition of weblog, log query parser, preprocessor, navigational pattern modelling, clustering, and classification. This study is conducted in the context of the actual data logs of the iLearn portal of Universiti Teknologi MARA (UiTM). This study revealed the navigational patterns of online learners which relatively related to their intake or group along the semester of 14 weeks. Besides, access patterns for students along the semester are different and can be classified into three (3) quarter, namely Q1, Q2 and Q3 based on the total of week per semester. Future work will focus on the development of prototype to improve the security of online learning especially during the assessment progress such as online quiz, test and examination.

The warm and humid condition is the characteristic of Malaysia tropical climate. Prediction of rain occurrences is important for the daily operations and decisions for the country that rely on agriculture needs. However predicting rainfall is a complex problem because it is effected by the dynamic nature of the tropical weather parameters of atmospheric pressure, temperature, humidity, dew point and wind speed. Those parameters have been used in this study. The rainfall prediction are compared and analyzed. Fuzzy Logic and Fuzzy Inference System can deal with ambiguity that often occurred in meteorological prediction; it can easily incorporate with expert knowledge and empirical study into standard mathematical. This paper will determine the dependability of Fuzzy Logic approach in rainfall prediction within the given approximation of rainfall rate, exploring the use of Fuzzy Logic and to develop the fuzzified model for rainfall prediction. The accuracy of the proposed Fuzzy Inference System model yields 72%

Processing an increasing volume of data, especially in industrial and manufacturing domains, calls for advanced tools of data analysis. Knowledge discovery is a process of analyzing data from different perspectives and summarizing the results into some useful and transparent findings. To address such challenges, a thorough extension and generalization of well-known techniques such as regression analysis becomes essential and highly advantageous. In this paper, we extend the concept of regression models so that they can handle hybrid data coming from various sources which quite often exhibit diverse levels of data quality. The major objective of this study is to develop a sound vehicle of a hybrid data analysis, which helps in reducing the computing time, especially in cases of real-time data processing. We propose an efficient real-time fuzzy switching regression analysis based on a genetic algorithm-based fuzzy C-means associated with a convex hull-based fuzzy regression approach. The method enables us to deal with situations when one has to deal with heterogeneous data which were derived from various database sources (distributed databases). In the proposed design, we emphasize a pivotal role of the convex hull approach, which is essential to alleviate the limitations of linear programming when being used in modeling of real-time systems. O 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

It is paramount to provide seamless and ubiquitous access to rich contents available online to interested users via a wide range of devices with varied characteristics. Recently, a service-oriented content adaptation scheme has emerged to address this content-device mismatch problem. In this scheme, content adaptation functions are provided as services by multiple providers. This elevates service discovery as an important problem. As most of the clients are using battery-powered devices, discovering closer providers are essential to reduce the accumulated time of receiving adapted content version. This study proposed a proximity-aware service discovery to address closer service discovery issue. The proposed mechanism uses estimation method to find closer service providers. The performance analysis verifies that our approach guarantees closer providers with a high probability, while at the same time significantly reduced the overhead up to one magnitude compared with the baseline brute force method.

MBJB Claim Management System is developed to provide an online platform for MBJB staff to manage the claim activities. By changing the current manual claim method to computerised system, the effectiveness and efficiency of claim process have been improved, for instances this system can quicken the approval process as well as allow applicants to check the status of claim application, minimizes human errors in calculation and provides several type of claim report. By putting the system online, there is no more problems of time and distance. Iterative Model which each iterative are mini-waterfall is applied to develop this project. Furthermore, Model-View-Controller (MVC) architecture is also used for developing this project. Hence, laravel framework is used. Data dictionary is produced for better understanding on system database. Several user interfaces are created to visualise the actual system environment. Last but not least, Hypertext pre-processor (PHP), Javascript, jQuery and MySQL are used during system development. As a conclusion, MBJB Claim Management system can replace the existing manual claim management as well as bring benefits to staff MBJB.

The code of occupational health and safety (OHS) is an influential regulation to improve the on-the-job safety of employees. A number of factors influence the planning and implementation of OHS management systems (OHSMS). The evaluation of OHSMS practice is the most important component when forming a health and safety environmental policy for employees. The objective of this research is to develop an intelligent data analysis (IDA) in which possibilistic regression being endowed with a convex hull approach is used to support the analysis of essential factors that influence OHSMS. Given such subjective terms, the obtained samples can be conveniently regarded as fuzzy input/output data represented by membership functions. The study offers this vehicle of intelligent data analysis as an alternative to evaluate the influential factors in a successful implementation of OHS policies and in this way decrease an overall computational effort. The obtained results show that several related OHSMS influential factors need to be carefully considered to facilitate a successful implementation of the OHSMS procedure.

in order to realize stable electricity generation, nuclear power plant (NPP) generators are evaluated in their performance of generated output power in term of quality and quantity. Therefore, the evaluation is realized on the basis of several influence factor, which have to be analyzed via the exploitation of heterogeneous data sets obtained from scattered locations and different types of sources. In this paper, we stress the pivotal role of extended fuzzy switching regression analysis in handling this type of data, which come from real world of the NPPs industry. The key objective of this study is to implement the enhancement of a convex hull approach in the fuzzy switching regression analysis process which can be viewed as an intelligent data analysis (IDA) approach. This approach is concerned with the effective combination of fuzzy sets theory with the analysis of large amounts of online data. For deploying the multisource data problem, the fuzzy regression analysis based on convex-hull, specifically Beneath-Beyond algorithm. The selected IDA becomes a potential analysis vehicle to successfully reduce the computing time as well as minimize the computational complexity. It is shown that the proposed approach becomes an efficient vehicle for the evaluation of produced output flow by NPPs. The study offers an interesting and practically appealing alterative platform to evaluate the quality and quantity of produced output flow of NPPs.

The limitations of the existing Knowledge Hyper-surface method in learning cause and effect relationships in the manufacturing process is explored. A new approach to enhance the performance of the current Knowledge Hyper-surface method has been proposed by constructing midpoints between each primary weight along each dimension by using a quadratic Lagrange interpolation polynomial. The new secondary-weight values, generated due to the addition of midpoints, were also represented as a linear combination of the corresponding primary/axial weight values. An improved neural networks in learning from examples have also been proposed where both of the proposed algorithms able to constrain the shape of the surface in two-dimensional and multi-dimensional cases and produced more realistic and acceptable results as compared to the previous version. The ability of the proposed approach to models the exponential increase/decrease in the belief values by using high-ordered polynomials without introducing ‘over-fitting’ effects was investigated. The performance of the proposed method in modelling the exponential increase/decrease in belief values was carried out on real cases taken from real casting data. The computed graphical results of the proposed methods were compared with the current Knowledge Hyper-surface and neural-network methods. As a result, the proposed methods correctly predict the sensitivity of process-parameter variations with the occurrence of a defect and very important area of research in a robust design methodology.

How to get the proper combination of feature extraction and classification is still crucial in facial expression recognition, and it has been addressed conducted over two decades. Hence, if inadequate features are used, even the best classifier could fail to achieve the accurate recognition. Therefore, Local Binary Pattern (LBP) is used as a feature extraction technique for facial expressions recognition where it is evaluated based on statistical local features. LBP is proven successful technique by the recent study due to its speed and discrimination performance aside of robust to low-resolution images. For the classification, Support Vector Machine is chosen, and the algorithm is implemented in MATLAB and tested on JAFFE (Japanese Female Facial Expressions) database in order to achieve the objectives and the goal of this research which is to obtain high accuracy in facial expressions and identify the seven basic facial expressions. The performance of feature extraction and classification is evaluated based on the recognition accuracy. The observation on results obtained in facial expressions recognition rate indicated the effectiveness of the proposed algorithm based on SVM-LBP features.

The back propagation (BP) algorithm is the most popular supervised learning method for multi-layered feed forward Neural Network. It has been successfully deployed in numerous practical problems and disciplines. Regardless of its popularity, BP is still known for some major drawbacks such as easily getting stuck in local minima and slow convergence; since, it uses Gradient Descent (GD) method to learn the network. Over the years, many improved modifications of the BP learning algorithm have been made by researchers but the local minima problem remains unresolved. Therefore, to resolve the inherent problems of BP algorithm, this paper proposed BPGD-A3T algorithm where the approach introduces three adaptive parameters which are gain, momentum and learning rate in BP. The performance of the proposed BPGD-A3T algorithm is then compared with BPGD two term parameters (BPGD-2T), BP with adaptive gain (BPGD-AG) and conventional BP algorithm (BPGD) by means of simulations on classification datasets. The simulation results show that the proposed BPGD-A3T shows better performance and performed highest accuracy for all dataset as compared to other.

Information on Internet and especially on Web sites increasing rapidly day by day, Web sites play an important role in this manner where a lot of Web users are always upload, download and brows a lot of contents based on their needs. Web server provide a way to browse these contents that scattered in many Web sites that reside in it by assigning an Internet Protocol (IP) address or Domain Name System (DNS) to be accessed around the world. Server log the requests that made by users to access their interesting content then store this information in the form of log file. Log file size can be growth from some kilobytes to several megabytes in few days depending on data traffic and the popularity of Web sites. With the fast growth of the data and information in Web environment made a necessity to use sophisticated techniques that have never used in other domains to extract knowledge and significant Web patterns. Web Mining is an extension of Data Mining that integrated various technology in research fields including Artificial Intelligence (AI), statistics, informatics, knowledge discovery and computational linguistics. The aim of Web Mining is to provide a algorithm or technique to make data accesses more efficient and convenient. Web Mining techniques are categorized into three classes depend on which part to be mined which are: Web Content Mining (WCM), Web Structure Mining (WSM) and Web Usage Mining (WSM), in this paper a survey of Web Mining techniques and application are discussed briefly.

Many governments around the world increasingly use internet technologies such as electronic government to provide public services. These services range from providing the most basic informational website to deploying sophisticated tools for managing interactions between government agencies and beyond government. Electronic government (e-government) aims to provide a more accurate, easily accessible, cost-effective and time saving for the community. In this study, we develop a new model of e-government adoption service by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) through the incorporation of some variables such as System Quality, Information Quality and Trust. The model is then tested using a large-scale, multi-site survey research of 237 Indonesian citizens. This model will be validated by using Structural Equation Modeling (SEM). The result indicates that System Quality, Information Quality and Trust variables proven to effect user behavior. This study extends the current understanding on the influence of System Quality, Information Quality and Trust factors to researchers, practitioners, and policy makers.

The area of halal industry is the fastest growing global business across the world. The halal food industry is thus crucial for Muslims all over the world as it serves to ensure them that the food items they consume daily are syariah compliant. Currently, ontology has been widely used in computer sciences area such as web on the heterogeneous information processing, semantic web, and information retrieval. However, ontology has still not been used widely in the halal industry. Today, Muslim community still have problem to verify halal status for products in the market especially foods consisting of E number. This research tried to solve problem in validating the halal status from various halal sources. There are various chemical ontology from multilple databases found to help this ontology development. The E numbers in this chemical ontology are codes for chemicals that can be used as food additives. With this E numbers ontology, Muslim community could identify and verify the halal status effectively for halal products in the market

Currently, Big Data is one of the common scenario which cannot be avoided. The presence of the voluminous amount of unstructured and semi-structured data would take too much time and cost too much money to load into a relational database for analysis purpose. Beside that, regression models are well known and widely used as one of the important categories of models in system modeling. This chapter shows an extended version of fuzzy regression concept in order to handle real-time data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-based Fuzzy C-Means (GAFCM) and a convex hull-based regression approach, which is regarded as a potential solution to the formation of information granules. It is shown that a setting of Granular Computing with the proposed approach, helps to reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. Additionally, the proposed approach shows an efficient real-time processing of information granules regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design sub-convex hulls as well as a main convex hull structure. In the proposed design setting, it was emphasized a pivotal role of the convex hull approach or more specifically the Beneath-Beyond algorithm, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.

In this study, we present a new idea dealing with the analysis of fuzzy random variables (FRVs) being treated as samples of data. The proposed concept can be used to model various real-life situations where uncertainty is not only present in the form of randomness but also comes in the form of imprecision described in terms of fuzzy sets. We propose a hybrid approach, which combines a convex hull approach (called Beneath-Beyond algorithm) with a fuzzy random regression analysis. Falling under the umbrella of intelligent data analysis (IDA) tool, this approach is suitable for real-time implementation of data analysis. For a fuzzy random data set, we include simulation results and highlight two main advantages, namely a decrease of required analysis time and a reduction of computational complexity. This emphasizes that the proposed IDA approach becomes an efficient way for real-time data analysis.