Articles
<![CDATA[Signature PSO: A novel inertia weight adjustment using fuzzy signature for LQR tuning ]]>
<![CDATA[Achmad Komarudin]]>;
<![CDATA[Novendra Setyawan]]>;
<![CDATA[Leonardo Kamajaya]]>;
<![CDATA[Mas Nurul Achmadiah]]>;
<![CDATA[Zulfatman Zulfatman]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v10i1.2667
<![CDATA[Particle swarm optimization (PSO) is an optimization algorithm that is simple and reliable to complete optimization. The balance between exploration and exploitation of PSO searching characteristics is maintained by inertia weight. Since this parameter has been introduced, there have been several different strategies to determine the inertia weight during a train of the run. This paper describes the method of adjusting the inertia weights using fuzzy signatures called signature PSO. Some parameters were used as a fuzzy signature variable to represent the particle situation in a run. The implementation to solve the tuning problem of linear quadratic regulator (LQR) control parameters is also presented in this paper. Another weight adjustment strategy is also used as a comparison in performance evaluation using an integral time absolute error (ITAE). Experimental results show that signature PSO was able to give a good approximation to the optimum control parameters of LQR in this case.]]>
<![CDATA[Kontrol Tegangan Self-Excited Induction Generator dengan Electronic Load Controller Terkontrol PID-GA ]]>
<![CDATA[Ermanu Azizul Hakim]]>;
<![CDATA[Rahayu Pandunengsih]]>;
<![CDATA[Diding Suhardi]]>;
<![CDATA[Novendra Setyawan]]>
<![CDATA[ IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 10, No 1 (2020): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijeis.54197
<![CDATA[Induction generator operation requires reactive power with external contactor. One of induction generator types, SEIG reactive power supplied by capacitor bank connected to generator terminal. SEIG is alternative energy conversion in small area or rural, SEIG has the main disadvantage of poor voltage regulation under various load conditions. ELC combine PID control which is optimized using Genetic Algorithm in order to maintain the stability of the voltage when the load varies. The result shows the SEIG system using ELC with PID-GA control worked to stable voltage in accordance with the standard with voltage tolerance of 10% when load change. The addition of GA to determine the value of the PID parameter where response system better with difference overshoot value start is 70.48%, when decrease load in 5 second by 44.3% and in the 10 second when increase load of 2 kW is 5.96% compared system with PID control without GA optimization.]]>
<![CDATA[Klasifikasi Golongan Darah Menggunakan Artificial Neural Networks Berdasarkan Histogram Citra ]]>
<![CDATA[Lailis Syafaah]]>;
<![CDATA[Yudawan Hidayat]]>;
<![CDATA[Novendra Setyawan]]>
<![CDATA[ IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 11, No 2 (2021): Oktober ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijeis.64049
<![CDATA[ Blood type in the medical world can be divided into 4 groups, namely A, B, AB and O. To be able to find out the blood type, a blood type test must be done. So far, human blood type detection is still done manually to observe the agglutination process. This research applies a blood type identification process using image processing. This system works by reading the blood type card image that has been filled with blood samples, then it will be processed through a histogram process to get the minimum and maximum RGB values and pixel locations which are then classified by Artificial Neural Networks (ANN) to determine the blood type from the training results and data matching. From the test results using 12 samples, it was found that the average error in blood type identification was 16.67%.]]>
<![CDATA[Active Fault Tolerance Control For Sensor Fault Problem in Wind Turbine Using SMO with LMI Approach ]]>
<![CDATA[Nuralif Mardiyah]]>;
<![CDATA[Novendra Setyawan]]>;
<![CDATA[Bella Retno]]>;
<![CDATA[Zulfatman Has]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v5.1673
<![CDATA[In this paper, we start to investigate the sensor fault problem in a Wind Turbine model with Fault Tolerant Control (FTC). FTC is used to allow the parameters of the controller to be reconfigured in accordance error information obtained online from sensors to improve the stability and overall performance of the system when an error occurs. The design is divided into two parts. The first part is designed Sliding Mode Observer (SMO) based Fault Detection Filter (FDF) to generate a residual signal to estimate fault. FDF is designed to maximize sensitivity fault. The second is a design output feedback control and Fault Compensation to guarantee the stability and performance system from disturbance by ignoring faults. Moreover, the function of fault compensation is to minimize effect fault of the system. The main contribution of this research is FTC proved to solve the sensor fault problem in a Wind Turbine model. The simulation showed the effectiveness of this method to estimate the fault and stabilized the system faster to a steady condition.]]>
<![CDATA[Object Detection of Omnidirectional Vision Using PSO-Neural Network for Soccer Robot ]]>
<![CDATA[Novendra Setyawan]]>;
<![CDATA[Nuralif Mardiyah]]>;
<![CDATA[Khusnul Hidayat]]>;
<![CDATA[Nurhadi Nurhadi]]>;
<![CDATA[Zulfatman Has]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v5.1696
<![CDATA[The vision system in soccer robot is needed to recognize the object around the robot environment. Omnidirectional vision system has been widely developed to find the object such as a ball, goalpost, and the white line in a field and recognized the distance and an angle between the object and robot. The most challenging in develop Omni-vision system is image distortion resulting from spherical mirror or lenses. This paper presents an efficient Omni-vision system using spherical lenses for real-time object detection. Aiming to overcome the image distortion and computation complexity, the distance calculation between object and robot from the spherical image is modeled using the neural network with optimized by particle swarm optimization. The experimental result shows the effectiveness of our development in the term of accuracy and processing time.]]>
<![CDATA[Pemantauan Physical Distance Pada Area Umum Menggunakan YOLO Tiny V3 ]]>
<![CDATA[Mohammad Chasrun Hasani]]>;
<![CDATA[Fadhila Milenasari]]>;
<![CDATA[Novendra Setyawan]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v6i1.3808
<![CDATA[Coronavirus disease in 2019 (Covid-19) is a phenomenon that become to the world concern because almost all countries experience the outbreak. One of attention to preventing the spread of Covid-19 is the physical distance in public areas. This study proposes human detection in public spaces by using image processing. The application of physical distance is intended to monitor the distance between people in public places. In this study, a human detection system is done by using the YOLO Tiny V3 method and the Euclidean algorithm to be developed to detect distances between humans. There are several stages in the research process: data collection, data preprocessing, data training, and physical distance detection. The system that has been designed can detect by getting an accuracy result of 78.43% for detecting human objects and an accuracy result of 87.82% for detecting distances between humans.]]>
<![CDATA[Navigasi Robot Sepak Bola Beroda Menggunakan Particle Filter Localization ]]>
<![CDATA[Novendra Setyawan]]>;
<![CDATA[Nur Alif Mardiyah]]>;
<![CDATA[Zulfatman Zulfatman]]>;
<![CDATA[Dwi Nur Fajar]]>
<![CDATA[ CYCLOTRON Vol 5, No 1 (2022): CYCLOTRON ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surabaya ]]>
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DOI: 10.30651/cl.v5i1.9419
<![CDATA["Dimana saya?" adalah pertanyaan utama, yang merupakan representasi lokalisasi atau penentuan psisi, dimana hal tersebut adalah permasalahan yang harus dijawab oleh robot sepak bola beroda. Deadreconing adalah metode paling populer yang digunakan dalam pergerakan robot beroda. Namun, kesalahan posisi yang meningkat adalah topik utama dari metode deadreconing. Selanjutnya dalam makalah ini diusulkan lokalisasi sepak bola beroda menggunakan filter partikel melalui Omnivision. Model sensor dan model gerak dari filter partikel juga dibahas, dimana model sensor diperoleh dari segmentasi dan ekstraksi ciri landmark lapangan sepak bola. Hasil eksperimen menunjukkan bahwa metode yang diusulkan memperkirakan posisi robot secara akurat dengan kesalahan 15%. ]]>
<![CDATA[Optimization of Technical and Economical Objective Functions of Hybrid Renewable Energy Generation Based Genetic Algorithm ]]>
<![CDATA[Novendra Setyawan]]>;
<![CDATA[Zulfatman Zulfatman]]>;
<![CDATA[Haris Rahmana Putra]]>;
<![CDATA[Muhammad Ikhwanul Khair]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v4i1.11690
<![CDATA[This study is aimed to optimize the technical and economic objective functions of a renewable energy hybrid generator system by using genetic algorithms (GA) in order to create a balanced and optimal power generation system configuration. The technical and economic aspects used were the Loss of Power Supply Probability (LPSP) and Annualized Cost of System (ACS), respectively. The objective functions of GA method were LPSP and ACS. The types of power plants used in this hybrid system were photovoltaic (PV), Wind Turbine (WT), battery, and Micro Hydro Power Plant (MHPP). Validation on the GA method was done by simulation in Matlab. Results of the simulation show that the use of the GA offers the most balanced system configuration with less expensive costs and a very good level of system reliability against hybrid systems. The use of the objective function with penalty factor scenario in GA is not as effective as the conventional GA, following the weakness of its evaluation results.]]>
<![CDATA[MPPT Menggunakan Algoritme Particle Swarm Optimization dan Artificial Bee Colony ]]>
<![CDATA[Ermanu Azizul Hakim]]>;
<![CDATA[Tamadar Al Ghufran]]>;
<![CDATA[Machmud Effendy]]>;
<![CDATA[Novendra Setyawan]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
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DOI: 10.22146/jnteti.v9i2.81
<![CDATA[Solar power plant is a renewable electricity generator that utilizes heat from sunlight. However, because the intensity of light received by the solar cell and the temperature in the solar cell is always changing, the power generated is not optimal. To optimize the output power of the solar cell, a Maxi-mum Power Point Tracking (MPPT) system is needed. Solar cells can be optimized by looking for MPPT and also by using a DC-DC converter. In this study, boost converter is optimized using Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. The results show that the highest efficiency obtained from boost converter is 78.25%,using duty cycle of 20%. For the overall system testing conducted at 09:00 WIB until 11:10 WIB, the average power obtained without using MPPT is 12.55 W, the average power of MPPT system using boost converter with PSO algorithm is 16.79 W, and average power of MPPT system using boost converter with ABC algorithm is 14.52 W. From the results, it was concluded that the output power of MPPT system using boost converter with PSO algorithm is more optimal than the MPPT system using boost converter with ABC algorithm.]]>
<![CDATA[Hybrid Frequency and Period Based for Angular Speed Measurement of DC Motor Using Kalman Filter ]]>
<![CDATA[Novendra Setyawan]]>;
<![CDATA[Basri Noor Cahyadi]]>;
<![CDATA[Ermanu Azizul Hakim]]>;
<![CDATA[Mas Nurul Achmadiah]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 2, May 2022 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v7i2.1420
<![CDATA[The Incremental Rotary Encoder have been widely used to measure the angular speed of electrical drive such as Permanent Magnet Direct Current Motor (PMDCM). Nevertheless, speed measurement of PMDCM from the encoder signals can be subject to errors in some special condition such as in low resolution encoder. There are two main methods to measure the angular speed of PMDCM through encoder signal such as frequency-based and period-based wich has its own properties. Hence in this reseach aimed to improve the angular speed measurement with hybridization of frequency and period-based measurement. The Hybrid method is defined as paralleling the period and frequency then estimated the angular speed using sensor fusion with Kalman Filter. The experiment is doing by comparing of all method to get the best way in measuring. From the experimental showed that the Kalman filter parameter was fine tuned that resulting the sensor fusion or the mixed measurement between the frequency-based and the period based measure the angular speed accurately.]]>
