Journal Cover IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)
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  This is an Open Access Journal Open Access journal
   ISSN (Print) 2088-3714 - ISSN (Online) 2460-7681
   Published by Universitas Gadjah Mada Homepage  [27 journals]
  • Sistem Pendeteksi dan Pelacakan Bola dengan Metode Hough Circle Transform,
           Blob Detection, dan Camshift Menggunakan AR.Drone

    • Authors: Elki Muhamad Pamungkas, Bakhtiar Alldino Ardi Sumbodo, Ika Candradewi
      Pages: 1 - 12
      Abstract:  Parrot AR.Drone is one type of quadrotor UAV. Quadrotor is operated manually with remote control and automatically using GPS (Global Positioning System), but using GPS in tracking mission an object has disadvantage that can’t  afford quadrotor position relative to object. Quadrotor require other control methods to perform object tracking. One approach is utilize digital image processing. In this research is designed detection and tracking ball system with digital image processing using OpenCV library and implemented on platform Robot Operating System. The methods which used is hough transform circle, blob detection and camshif.            The results of this research is system on AR.Drone capable of detecting and tracking ball. Based on the test results it was concluded that the maximum distance of system is capable to detecting ball with diameter of 20 cm using hough transform circle method is 500 cm and using blob detection method is 900 cm. Average time detection process to detect the ball using hough transform circle that is 0.0054 second and  for blob detection method is 0.0116 second. The success rate of tracking the ball using camshift method from the results of detection hough circle transfom is 100% while from result of detection blob detection is 96.67%
      PubDate: 2017-04-29
      DOI: 10.22146/ijeis.15405
      Issue No: Vol. 7, No. 1 (2017)
       
  • Deteksi Ketersediaan Slot Parkir Berbasis Pengolahan Citra Digital
           Menggunakan Metode Histogram of Oriented Gradients dan Support Vector
           Machine

    • Authors: Aditya Riska Putra, Ika Candradewi
      Pages: 13 - 24
      Abstract: This research aims to implement method based on digital image processing to inform the status of parking slots at the car parking area by using a feature extraction HOG (Histogram of Oriented Gradients) method in every region of the parking area. Feature extraction results are classified using SVM (Support Vector Machine) by comparing the Linear, RBF (Radial Basis Function), Poly, and Sigmoid kernels. SVM classification results were analyzed using the confusion matrix with accuracy, specificity, sensitivity, and precision parameters. In terms of accuracy, system obtained with Linear kernel in sunny conditions shows 98.0% accuracy; rainy 98.8% accuracy; cloudy 99.2% accuracy. Obtained accuracy using Poly kernel test in sunny conditions shows 99.2%; rainy 98.9%; cloudy 99.4%. Obtained accuracy using RBF kernel in sunny conditions shows 97.9%; rainy 98.7%; cloudy 99.6%. In terms of accuracy using additional data testing obtained with Linear kernel shows accuracy of 97.7%; RBF kernel 97.9% accuracy;  Poly kernel 97.4% accuracy. Sigmoid kernel testing can’t be used because the optimal model did not obtained by using default grid.
      PubDate: 2017-04-30
      DOI: 10.22146/ijeis.15411
      Issue No: Vol. 7, No. 1 (2017)
       
  • Klasifikasi Sel Darah Putih Menggunakan Metode Support Vector Machine
           (SVM) Berbasis Pengolahan Citra Digital

    • Authors: Bhima Caraka, Bakhtiar Alldino Ardi Sumbodo, Ika Candradewi
      Pages: 25 - 36
      Abstract: White blood cells are classified into five types (basophils, eosinophils, neutrophils, lymphocytes and monocytes) with additional classes lymphoblast cells from microscope images are processed. By applying image processing, image its white blood cells extracted using the Histogram Oriented Gradient. Feature extraction results obtained then classified using Support Vector Machine method by comparing the results of two different kernel parameters: kernel Linear and kernel Radial Basis Function (RBF). Classification evaluated with these parameters: Accuracy, specificity, and sensitivity.Obtained an accuracy of 72.26% from the detection of white blood cells in the microscope image. The average value of microscope images of patients and different kernel every white blood cells (monocytes, basophils, neutrophils, eosinophils, lymphocytes and lymphoblast) were evaluated with these parameters. Results of the study show the classification system has an average value of 82.20% accuracy (RBF Patient 1), 81.63% (RBF Patient 2) and 78.73% (Linear Patient 1), 79.55% (Linear Patient 2 ), then the value of specificity of 89.91% (RBF patient 1), 92.18% (RBF patient 2) and 88.06% (Linear patient 1), 91.34% (Linear patient 2), and sensitivity values 15 , 45% (RBF patient 1), 12.97% (RBF patient 2) and 13.33% (Linear patient 1), 12.50% (Linear patient 2).
      PubDate: 2017-04-30
      DOI: 10.22146/ijeis.15420
      Issue No: Vol. 7, No. 1 (2017)
       
  • Implementasi Kalman Filter Pada Kendali Roket EDF

    • Authors: Wisnu Pamungkas, Bakhtiar Alldino Ardi Sumbodo, Catur Atmaji
      Pages: 37 - 48
      Abstract: EDF (electric ducted fan) rocket is a flying object shapes like bullet with electric ducted fan motor as the booster. This rocket fly autonomously by utilizing accelerometer, gyroscop, and magnetometer sensor to determine the attitude of the rocket against the earth’s gravitational and magnetic field of the earth. In controlling the rocket required a control system capable of controlling a rocket with sensor data that has been processed into the value of the attitude that has been filtered.In this study, designed a filter that will be implemented on the microcontroller rocket. The filters are Kalman filter is implemented while the control used is the control proportional integral derivative (PID) with Ziegler-Nichols tuning method.The result of this research is an implementation of kalman filter to EDF rocket control system. Based on the experiment that has been done, control system using a Kalman filter has a standard deviation value against the value of linear regression on a roll attitude of 2.73, a pitch of 3.03, and yaw of 6.96 degrees. While the standard deviation of the ideal value on a roll attitude of 3.43, a pitch of 2.92 and yaw of 5.21 degrees.
      PubDate: 2017-04-30
      DOI: 10.22146/ijeis.15436
      Issue No: Vol. 7, No. 1 (2017)
       
  • Sistem Kendali Penerbangan Quadrotor pada Keadaan Melayang dengan Metode
           LQR dan Kalman Filter

    • Authors: Andi Dharmawan, Ivan Fajar Arismawan
      Pages: 49 - 60
      Abstract: Quadrotor is a type of UAV (Unmanned Aerial Vehicle) with four propellers and four rotor. Quadrotor as flying robots has the advantage to take off and land vertically. In addition quadrotor also has the ability to fly hovered near a stationary state. However quadrotor had some difficulties to operate. One of these difficulties is to make quadrotor be able to fly and maintain the stationary state of the Euler angles (roll, pitch, and yaw). Linear Quadratic Regulator (LQR) as one of the modern control method which has the advantage of maintaining the conditions on the ground. This method can be combined with Kalman filter algorithm. It aims to reduce measurement error from the process sensor fusion and maintain Euler angles (roll, pitch and yaw).Kalman filter aims to reduce the measurement error of the sensor fusion. Then the output of Kalman filter algorithm becomes the input state for control LQR the roll angle and pitch angle. Input state is multiplied with the negative feedback  as process systems. The results are converted into pulses to rotate the brushless motor so quadrotor can fly stably.The test results showed quadrotor while maintaining stability against roll angle has overshoot of 0.35 ° and the pitch angle has overshoot of 2 °.
      PubDate: 2017-04-30
      DOI: 10.22146/ijeis.15262
      Issue No: Vol. 7, No. 1 (2017)
       
  • Pengendalian Kestabilan Ketinggian pada Penerbangan Quadrotor dengan
           Metode PID Fuzzy

    • Authors: Panca Agung Kusuma, Andi Dharmawan
      Pages: 61 - 70
      Abstract:  Quadrotor is a kind of unmanned aerial vehicle that have the ability to take of vertically and maintaining its position while flying mid-air. Flying a quadrotor sometimes needs a stable altitude to perform a specific mission. A stable altitude will make easier for pilot to control the movement of the quadrotor to certain direction.This study designed and implemented a system that can stabilises the altitude of a quadrotor by using Fuzzy-PID method. Altitude control system needed to help pilot controls the altitude stability without adjusting the throttle. Control with PID method is a common control system to be implemented on a quadrotor. This control system has a constant that can be tuned with fuzzy logic with linguistic approach to improve the response time when compensating an error.  The result of this study shows that Fuzzy PID control method generate a better response time compared with the PID-only method. The implementation of PID control generate an altitude stabilisation with a mean value steady state error of ±1,86 cm, whereas the PID Fuzzy generate a mean value of steady state error of ±1,22 cm.
      PubDate: 2017-04-30
      DOI: 10.22146/ijeis.15456
      Issue No: Vol. 7, No. 1 (2017)
       
  • Rancang Bangun Spectrum Analyzer Menggunakan Fast Fouier Transform Pada
           Single Board Computer

    • Authors: Afandi Nur Aziz Thohari, Agfianto Eko Putra
      Pages: 71 - 82
      Abstract: Spectrum analyzer is an instrument device to measure the magnitude of the frequency and the power of signal. It has many benefits, such as used for testing telecommunication devices, determining the allocation of unused frequencies and also for practicum in schools or universities. However, because of these many benefits, the price of this signal measuring equipment soared in the market.As an alternative, a device that can serve as spectrum analyzer yet has an affordable price is invented in the form of the prototype of spectrum analyzer built using a single board computer by applying a fast Fourier transform algorithm. Feedback from the prototype is in the form of radio signal captured using RTL-SDR.The test results showed that the range of frequencies that can be displayed by the prototype is 24 MHz to 1.769 MHz. Then the test results of fast Fourier transform computing on N points showed that the prototype can work smoothly using the N from 512 to 32.768 points. The use of N more than 32.768 points will cause CPU and disk memory overloaded and lead to a slow performance. Finally, comparison of the levels of spectrum was performed using spectrum analyzer Anritsu MS2720T. As a result, it is known that prototype can be used to show the location of the frequency spectrum of the radio signal appropriately.
      PubDate: 2017-04-30
      DOI: 10.22146/ijeis.16417
      Issue No: Vol. 7, No. 1 (2017)
       
  • Purwarupa Sistem Pembuka Pintu Cerdas Menggunakan Perceptron Berdasarkan
           Prediksi Kedatangan Pemilik

    • Authors: Brisma Meihar Arsandi, Triyogatama Wahyu Widodo, Faizah Faizah
      Pages: 83 - 92
      Abstract: Arrival prediction system on smarthome is system that cam estimating time of home owner arrival on smarthome. Prediction system used to reference on smarthome system to preparing electronic devices so at home owner arrive, the devices are already to use. Prediction system made by divide distance of home owner location to home by driving velocity. Prediction also use neural network perceptron to determine travel condition are in traffic or not and correcting to predicting perform. Perceptron use last travel data as reference correction to prediction system. Based on testing on prediction system, accuracy of prediction system reach 74% to 79%. Accuracy reach these values due errors occurred while determining location so predicted route became not match with real condition. Errors occured by GPS usage not on outdoor area and smartphone GPS only detect 6 GPS satellite. Neural network perceptron differ of traffic condition on travel after fourth epoch, with weight value at 11.09 and bias value at 61. And perceptron can correcting prediction system after twelfth epoch with weight values at -0.2778 and 0.2924 also bias value at -0.05.
      PubDate: 2017-04-30
      DOI: 10.22146/ijeis.16840
      Issue No: Vol. 7, No. 1 (2017)
       
  • Rancang Bangun M2M (Machine-to-Machine) Communication Berbasis 6LoWPAN

    • Authors: Doni Pradana, Bakhtiar Alldino Ardi Sumbodo
      Pages: 93 - 104
      Abstract: At the present time the development of technology enabling communication services without the use of cables its called wireless technology. One such wireless communication technology is M2M (Machine to Machine) communication. In this study, using a protocol 6LoWPAN as the basis of M2M communications, becaused in Zigbee protocol encountered to the weakness such as the value of end-to-end delay and packet loss is greater than 6LoWPAN protocol. Work on this study is desaign system of M2M communication based on 6LoWPAN protocol and compare the data of the performance of 6LoWPAN protocol with Zigbee protocol terms of end-to-end delay and packet loss. Variations of testing is to set the baud rate Xbee 1200 bps, 4800 bps, 9600 bps, 19200 bps, 38400 bps, 57600 bps, 115200 bps. Besides the baud rate, the variation also adjusting the distance between  nodes from 10 meter up to 60 meter by 10 meter intervals. Average of end-to-end delay time is 1899 milliseconds on 6LoWPAN, while protocol Zigbee is 422 miliseconds. In the packet loss aspect, in 6LoWPAN protocol not provided because the use of UDP not have an acknowledgement and squence number to track packet loss, while average of packet loss Zigbee protocol is 26%.
      PubDate: 2017-04-30
      DOI: 10.22146/ijeis.18087
      Issue No: Vol. 7, No. 1 (2017)
       
  • Pendeteksian Bola untuk Robot Sepak Bola Humanoid Berbasis Pengenalan Pola

    • Authors: Fauzi Nur Iswahyudi, Bakhtiar Alldino Ardi Sumbodo
      Pages: 105 - 116
      Abstract:  Humanoid soccer robot is one of popular developed robot. RoboCup is a competitive competiton of humanoid robot soccer. The rule of RoboCup changed by the time, the previous orange ball changed by white ball which is same color as the field line and the goal. Accordingly, in this research designed a white ball detection system for humanoid soccer robot based on pattern recognition. Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) method are used in this research for feature extraction and classification. The result of this research is a system that be able to detect ball in humanoid soccer robot environment. The system tested by sliding window parameter testing, the distance of ball to robot testing, different light intensity testing, and other object testing. The conclusions of this research are: optimal detection is obtained by using 8x8 win_stride parameter size and 1,2 scale0 parameter value, maximum distance of detection with 32×32 window detector is 180 cm and with 64×64 window detector is 140 cm, the response of system in different light intensity is good enough, and the success rate of system against other obstacle object with 32×32 window detector is 68% and with 64×64 window detector is 99%.
      PubDate: 2017-04-30
      DOI: 10.22146/ijeis.18108
      Issue No: Vol. 7, No. 1 (2017)
       
 
 
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