International Journal of Artificial Intelligence Research
International Journal Of Artificial Intelligence Research (IJAIR) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics of Artificial intelligent Research which covers four (4) majors areas of research that includes 1) Machine Learning and Soft Computing, 2) Data Mining & Big Data Analytics, 3) Computer Vision and Pattern Recognition, and 4) Automated reasoning. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Articles
621 Documents
<![CDATA[An Embedded Fuzzy Logic Based Application for Density Traffic Control System ]]>
<![CDATA[Adewale, Ajao Lukman]]>;
<![CDATA[Jumoke, Ajao Falilat]]>;
<![CDATA[Adegboye, Mutiu]]>;
<![CDATA[Ismail, Abideen]]>
<![CDATA[ International Journal of Artificial Intelligence Research Vol 2, No 1 (2018): June 2018 ]]>
Publisher : <![CDATA[Universitas Dharma Wacana ]]>
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DOI: 10.29099/ijair.v2i1.44
<![CDATA[The control of density traffic at cross junction road usually manned by human efforts or implementation of automatic traffic light system. This system seem and proves to be inefficient with some challenges. The major constraints of this traffic control are as a result of the inability of most traffic control systems to assign appropriate waiting time for vehicles based on the lane density. Also with little or no consideration for pedestrians, emergency and security agents priorities. In view of this, an intelligent density traffic control system using (fuzzy logic) which is capable of providing priority to the road users based on the density and emergency situations was developed and presented in this paper. This system will obtain the approximate amount of vehicle and presence of pedestrians respectfully on each lane with help of Infrared Sensors (IR) and siren detection system for emergency and security road users. The working principle of this system depending on the logic inputs rules given into the processing unit by the (sensors, S1 and S2) which helps the system to generates a timing sequence that best suit the number of vehicles and pedestrians available on the lane at point in time.]]>
<![CDATA[The Comparison of Signature Verification Result Using 2DPCA Method and SSE Method ]]>
<![CDATA[Anita Sindar R M Sinaga]]>
<![CDATA[ International Journal of Artificial Intelligence Research Vol 2, No 1 (2018): June 2018 ]]>
Publisher : <![CDATA[STMIK Dharma Wacana ]]>
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DOI: 10.29099/ijair.v2i1.38
<![CDATA[The rate of speed and validation verify to be a reference of quality information and reliable results. Everyone has signature characteristics but it will be difficult to match original signatures with a clone. Two Dimensional Principal Component Analysis (2DPCA) method, Sum Equal Error (SSE) method includes a method that can provide accurate data verification value of 90% - 98%. Results of scanned signatures, converted from RGB image - grayscale - black white (binary color). The extraction process of each method requires experimental data as a data source in pixel size. Digital image consists of a collection of pixels then each image is converted in a matrix. Preprocessing Method 2 DPCA each data is divided into data planning and data testing. Extraction on SSE method, each data sought histogram value and total black value. This study yields a comparison of the suitability of the extraction results of each method. Both of these methods have a data accuracy rate of 97% - 98%. When compared to the results of the accuracy of image verification with 2DPCA method: SSE is 97%: 96%. With the same data source will be tested result of 2DPCA method with SSE method.]]>
<![CDATA[Comparative Analysis Of Dempster Shafer Method With Certainty Factor Method For Diagnose Stroke Diseases ]]>
<![CDATA[Erwin Kuit Panggabean]]>
<![CDATA[ International Journal of Artificial Intelligence Research Vol 2, No 1 (2018): June 2018 ]]>
Publisher : <![CDATA[STMIK Dharma Wacana ]]>
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DOI: 10.29099/ijair.v2i1.53
<![CDATA[The development of artificial intelligence technology that has occurred has allowed expert systems to be applied in detecting disease using programming languages. One in terms of providing information about a variety of disease problems that have recently been feared by Indonesian society, namely stroke. Expert system method used is dempster shafer and certainty factor method is used to analyze the comparison of both methods in stroke.Based on the analysis result, it is found that certainty factor is better than demster shafer and more accurate in handling the knowledge representation of stoke disease according to the symptoms of disease obtained from one hospital in medan city, uniqueness of algorithm that exist in both methods.]]>
<![CDATA[Computer Vision and Image Processing: A Paper Review ]]>
<![CDATA[Wiley, Victor]]>;
<![CDATA[Lucas, Thomas]]>
<![CDATA[ International Journal of Artificial Intelligence Research Vol 2, No 1 (2018): June 2018 ]]>
Publisher : <![CDATA[Universitas Dharma Wacana ]]>
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DOI: 10.29099/ijair.v2i1.42
<![CDATA[Computer vision has been studied from many persective. It expands from raw data recording into techniques and ideas combining digital image processing, pattern recognition, machine learning and computer graphics. The wide usage has attracted many scholars to integrate with many disciplines and fields. This paper provide a survey of the recent technologies and theoretical concept explaining the development of computer vision especially related to image processing using different areas of their field application. Computer vision helps scholars to analyze images and video to obtain necessary information,   understand information on events or descriptions, and scenic pattern. It used method of multi-range application domain with massive data analysis. This paper provides contribution of recent development on reviews related to computer vision, image processing, and their related studies. We categorized the computer vision mainstream into four group e.g., image processing, object recognition, and machine learning. We also provide brief explanation on the up-to-date information about the techniques and their performance.]]>
<![CDATA[The Design of Optimal PID Control Method for Quadcopter Movement Control ]]>
<![CDATA[Arrosida, Hanum]]>;
<![CDATA[Echsony, Mohammad Erik]]>
<![CDATA[ International Journal of Artificial Intelligence Research Vol 2, No 1 (2018): June 2018 ]]>
Publisher : <![CDATA[Universitas Dharma Wacana ]]>
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DOI: 10.29099/ijair.v2i1.32
<![CDATA[Nowadays, quadcopter motion control has become a popular research topic because of its versatile ability as an unmanned aircraft can be used to alleviate human labor and also be able to reach dangerous areas or areas which is unreachable to humans. On the other hand, the Optimal PID control method, which incorporates PID and Linear Quadratic Regulator (LQR) control methods, has also been widely used in industry and research field because it has advantages that are easy to operate, easy design, and a good level of precision. In the PID control method, the main problem to be solved is the accuracy of the gain value Kp, Ki, and Kd because the inappropriateness of those value will result in an imprecise control action. Based on these problems and referring to the previous study, the optimal PID control method was developed by using PID controller structure with tuning gain parameter of PID through Linear Quadratic Regulator (LQR) method. Through the integration of these two control methods, the optimum solutions can be obtained: easier controller design process for quadcopter control when crossing the determined trajectories, steady state error values less than 5% and a stable quadcopter movement with roll and pitch angle stabilization at position 0 radians with minimum energy function.]]>
<![CDATA[Bio-inspired Expert System based on Genetic Algorithm for Printer Identification in Forensic Science ]]>
<![CDATA[Darwish, Saad Mohamed]]>;
<![CDATA[ELgohary, Hany M]]>
<![CDATA[ International Journal of Artificial Intelligence Research Vol 2, No 2 (2018): December 2018 ]]>
Publisher : <![CDATA[Universitas Dharma Wacana ]]>
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DOI: 10.29099/ijair.v2i2.67
<![CDATA[Printer identification models are provided for the goal of distinguishing the printer that produced a suspicious imprinted document. Source identification of a published document can easily be a significant procedure intended for the forensic science. The arising problem is that the extraction of many features of the printed document for printer identification sometimes increases time and reduces the classification accuracy since a lot of the document features may come to be repetitive and non-beneficial. Distinct combinatorial collection of features will need to be acquired in order to preserve the most effective fusion to accomplish the maximum accuracy. This paper presents an intelligent machine learning algorithm for printer identification that adopts both of texture features formulated from gray level co-occurrence matrix of the printed letter ''WOO'' and genetic heuristic search to select the optimal reduced feature set. This integration aims to achieve high classification accuracy based on small group of discriminative features. For classification, the system utilizes k-nearest neighbors (KNN) to recognize the source model of the printer for its simplicity. Experimental results validate that the suggested system has high taxonomy accuracy and requires less computation time.]]>
<![CDATA[Distance and Fuzzy Classifiers Alliance: The Solution to Off-line Arabic Signature Verification System for Forensic Science ]]>
<![CDATA[Darwish, Saad Mohamed]]>;
<![CDATA[Noori, Zainab H]]>
<![CDATA[ International Journal of Artificial Intelligence Research Vol 2, No 2 (2018): December 2018 ]]>
Publisher : <![CDATA[Universitas Dharma Wacana ]]>
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DOI: 10.29099/ijair.v2i2.66
<![CDATA[Signature of a person is one of the most popular and legally accepted behavioral biometrics that provides secure means for verification and personal identification in many applications such as financial, commercial and legal transactions. The objective of the signature verification system is to classify between genuine and forgery that is often associated with intrapersonal and interpersonal variability. Unlike other languages, Arabic has unique features; it contains diacritics, ligatures, and overlapping. Â Because of lacking any form of dynamic information during the Arabic signature writing process, it will be more difficult to obtain higher verification accuracy. This paper addresses the above difficulty by introducing a novel Off-Line Arabic signature verification algorithm. Different from state-of-the-art works that adopt one-level of verification or multiple classifiers based on statistical learning theory; this work employs two-level of fuzzy set related verification. The level one verification depends on finding the total difference between the features extracted from the test signature and the mean values of each corresponding features in the training signatures (owning the same signature). Whereas, the level two verification relies on the output of the fuzzy logic module depending on the membership functions that has been created from the signature features in the training dataset for a specific signer. It is concluded from the experimental results that the verification system performs well and has the ability to reduce both False Acceptance Rate (FAR) and False Rejection Rate (FRR).]]>
<![CDATA[Application Of Backpropagation Neural Networks In Predicting Rainfall Data In Ambon City ]]>
<![CDATA[Yopi Andry Lesnussa]]>;
<![CDATA[C. G. Mustamu]]>;
<![CDATA[F. Kondo Lembang]]>;
<![CDATA[M. W. Talakua]]>
<![CDATA[ International Journal of Artificial Intelligence Research Vol 2, No 2 (2018): December 2018 ]]>
Publisher : <![CDATA[STMIK Dharma Wacana ]]>
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DOI: 10.29099/ijair.v2i2.59
<![CDATA[The Artificial Neural Networks is a process of information system on certain traits which as representatives of the human neural networks. The Artificial Neural Networks can be applied in every area of human life, one of them is environment especially about prediction of climate or weather. In this research, the artificial neural network is used to predict the rainfall with Backpropagation method and using MATLAB software. The other meteorology parameters used to predict the rainfall are air temperature, air velocity and air pressure. The result showed less accuracy level is 80% by using alpha 0,7, iteration number (epoch) 10000 and MSE value = 0,0218. Therefore, the result of rainfall prediction system is accurate.]]>
<![CDATA[A Modified Meta-Heuristic Approach for Vehicle Routing Problem with Simultaneous Pickup and Delivery ]]>
<![CDATA[Faiz, Alfian]]>;
<![CDATA[Subiyanto, Subiyanto]]>;
<![CDATA[Arief, Ulfah Mediaty]]>
<![CDATA[ International Journal of Artificial Intelligence Research Vol 2, No 2 (2018): December 2018 ]]>
Publisher : <![CDATA[Universitas Dharma Wacana ]]>
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DOI: 10.29099/ijair.v2i2.71
<![CDATA[The aim of this work is to develop an intelligent optimization software based on enhanced VNS meta-heuristic to tackle Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). An optimization system developed based on enhanced Variable Neighborhood Search with Perturbation Mechanism and Adaptive Selection Mechanism as the simple but effective optimization approach presented in this work. The solution method composed by combining Perturbation based Variable Neighborhood Search (PVNS) with Adaptive Selection Mechanism (ASM) to control perturbation scheme. Instead of stochastic approach, selection of perturbation scheme used in the algorithm employed an empirical selection based on each perturbation scheme success along the search. The ASM help algorithm to get more diversification degree and jumping from local optimum condition using most successful perturbation scheme empirically in the search process. A comparative analysis with a well-known exact approach is presented to test the solution method in a generated VRPSPD benchmark instance in limited computation time. Then a test to VRPSPD scenario provided by a liquefied petroleum gas distribution company is performed. The test result confirms that solution method present superior performance against exact approach solution in giving best solution for larger sized instance and successfully obtain substantial improvements when compared to the basic VNS and original route planning technique used by a distributor company.]]>
<![CDATA[Solution Search Simulation The Shortest Step On Chess Horse Using Breadth-First Search Algorithm ]]>
<![CDATA[Bastian, Ade]]>;
<![CDATA[Nugraha, Rezha]]>
<![CDATA[ International Journal of Artificial Intelligence Research Vol 2, No 2 (2018): December 2018 ]]>
Publisher : <![CDATA[Universitas Dharma Wacana ]]>
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DOI: 10.29099/ijair.v2i2.58
<![CDATA[Horse seed in the chess board movement resembles the letter L. The chess pieces are one of a very hard-driven beans and seeds are often also the most dangerous if not carefully considered every movement. Simulation of this problem provides a chess board size n x n. Target (goal) of this problem is to move a horse beans of a certain position on a chess board position to the desired destination with the shortest movement simulates all possible solutions to get to the goal position. This problem is also one of the classic problems in artificial intelligence (AI). Settlement of this problem can use the help system and tree production tracking.Therefore, designed a simulation applications by utilizing several techniques of simulation programming and Breadth-First Search method. With this method, all nodes will be traced and the nodes at level n will be visited first before visiting the nodes at level n + 1. The purpose of this study is to design a software that is able to find all the solutions for the shortest movement toward the goal position by using the system of production and tracking tree.Results from this paper is that the software is able to find all solutions shortest movement a horse beans from the initial position to the goal position and displays the simulation of the movement of the horse in the chess board.]]>
