Abstract-The inference engine is one of main components of expert system that influences the performance of expert system. The task of inference engine is to give answers and reasons to users by inference the knowledge of expert system. Since the idea of ternary grid issued in 2004, there is only several developed method, technique or engine working on ternary grid knowledge model. The in 2010 developed inference engine is less efficient because it works based on iterative process. The in 2011 developed inference engine works statically and quite expensive to compute. In order to improve the previous inference methods, a new inference engine has been developed. It works based on backward chaining process in ternary grid expert system. This paper describes the development of inference engine of expert system that can work in ternary grid knowledge model. The strategy to inference knowledge uses backward chaining with recursive process. The design result is implemented in the form of software. The result of experiment shows that the inference process works properly, dynamically and more efficient to compute in comparison to the previous developed methods.

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Currently the development of robots has reached a high level of complexity. However, it is also accompanied by increasing complexity problems in its control. Some control with the image processing method also requires competent operators, at least memorize each command used. Therefore, to reduce the complexity in its control, in this research robot control, especially humanoid robot is made using motion capture method. Motion capture is a control technique using a camera transducer in the form of Microsoft Kinect, to obtain the coordinate skeleton of the joint user. Furthermore the data is processed in Visual Studio software to obtain the angular information that will be formed by the robot. So that the robot can perform the same movement with the user. The information is transmitted wirelessly to the microcontroller contained on the robot in real-time. The results of research showed that the system can translate the user's movement into information movement of humanoid robot. With an average skeleton vector detection error of 1.69 cm, an average response time of 1.3 seconds and an absolute error average position of the end effector on the x axis of 3.2 cm and on the y-axis of 1.28 cm.

Artikel ini membahas desain antarmuka pada vehicle routing problem (VRP) 3-dimensi untuk armada multi-drone. Armada ini melakukan perjalanan untuk mengunjungi serangkaian titik dengan memperhatikan batasan tertentu. Karena VRP diklasifikasikan sebagai masalah optimasi NP-hard, algoritma aproksimasi seperti Algoritma Genetika diterapkan untuk menemukan solusi terbaik untuk masalah optimisasi kombinatorial ini. Dalam merancang GUI ini, kami menggunakan Netlogo sebagai alat untuk membangun antarmuka, dan juga untuk eksperimen dan simulasi. Hasil penelitian ini menunjukkan bahwa dengan menggunakan Netlogo, kita dapat mendesain antarmuka untuk mensimulasikan algoritma aproksimasi dalam penyelesaian permasalahan optimasi kombinatorial, yang mudah dioperasikan oleh pengguna