Garuda - Garba Rujukan Digital

Sistem Pendeteksi dan Pelacakan Bola dengan Metode Hough Circle Transform, Blob Detection, dan Camshift Menggunakan AR.Drone Elki Muhamad Pamungkas; Bakhtiar Alldino Ardi Sumbodo; Ika Candradewi
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 7, No 1 (2017): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

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%

Deteksi Ketersediaan Slot Parkir Berbasis Pengolahan Citra Digital Menggunakan Metode Histogram of Oriented Gradients dan Support Vector Machine Aditya Riska Putra; Ika Candradewi
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 7, No 1 (2017): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

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.

Klasifikasi Sel Darah Putih Menggunakan Metode Support Vector Machine (SVM) Berbasis Pengolahan Citra Digital Bhima Caraka; Bakhtiar Alldino Ardi Sumbodo; Ika Candradewi
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 7, No 1 (2017): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

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).

Sistem Klasifikasi Kendaraan Berbasis Pengolahan Citra Digital dengan Metode Multilayer Perceptron Muhammad Irfan; Bakhtiar Alldino Ardi Sumbodo; Ika Candradewi
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 7, No 2 (2017): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

The evolution of video sensors and hardware can be used for developing traffic monitoring system vision based. It can provide information about vehicle passing by utilizing the camera, so that monitoring can be done automatically. It is needed for the processing systems to provide some information regarding traffic conditions. One such approach is to utilize digital image processing.This research consisted of two phases image processing, namely the process of detection and classification. The process of detection using Haar Cascade Classifier with the training data image form the vehicle and data test form the image state of toll road drawn at random. While, Multilayer Perceptron classification process uses by utilizing the result of the detection process. Vehicle classification is divided into three types, namely car, bus and truck. Then the classification parameters were evaluated by accuracy. The test results vehicle detection indicate the value of accuracy is 92.67. Meanwhile, the classification process is done with phase trial and error to evaluate the parameters that have been determined. Results of the study show the classification system has an average value of the accuracy is 87.60%.

Model Tracking Pembicara Dalam Perekaman Video Otomatis Pada Kelas Cendekia Elga Ridlo Sinatriya; Muhammad Idham Ananta Timur; Ika Candradewi
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 9, No 1 (2019): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

The requisite of intelligent classroom’s saving the information from speakers inside the class using ubiquitous computing concept. It said the most profound technologies are those that disappear, and they weave themselves into fabric of everday life until they are indistinguishable from it. It requires a few capability such as tracking the speaker and record it. Therefore it will be require the system that can tracking the speaker in real time, ignore the other speaker, and recording speaker’s activity. The system consumes 168.02 ms in one move, like detection using statis camera, send the centroid to microcontroller, second detection using dinamis camera, and record it. The system had an accuracy of 93.37 % to fits the speaker at the middle of frame record. The system is also had an accuracy of 98% to detecting the correct speaker.

Hand-Raise Detection Pada Kelas Cendekia Menggunakan Kamera RGB Dan Depth Muhammad Fajar Khairul Auni; Muhammad Idham Ananta Timur; Ika Candradewi
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 8, No 1 (2018): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

The requisite of intelligent classroom’s to perform the quickest speaker lift determination of speakers in the classroom using the concept of ubiquitous computing where the technology exists but does not feel around. The classroom concept requires several capabilities such as knowing the ideal distance from the camera, performing real-time hand-lifted movements from the speaker using the AdaBoost method, and determining the fastest hand lift from the speaker in real-time. The camera's ideal distance to speakers is about 250 cm. the system has a detection accuracy of 97.485497% and accuracy using coordinates joint point of 98%. The system is also capable of determining the fastest time using AdaBoost with 93.5% accuracy and the accuracy of the fastest hands lifting using coordinates joint point of 95%.

Penggunaan Deteksi Gerak untuk Pengurangan Ukuran Data Rekaman Video Kamera CCTV Jockie P Sagala; Ika Candradewi; Agus Harjoko
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 10, No 1 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Some cases the recording data of Closed Circuit Television (CCTV) is stored for future use. In the long term usage, the files size will grow larger and requiring large storage devices. In some cases, the recorded data not only image with the desired object but also the background images that may be recorded for long periods of time. This cases make data storage device usage to be less effective. So this research will design a system of CCTV devices that capable to select images to reduce the size of stored images data by image processing.The images selection of this system is based on based on adaptive median algorithm. When any object get detected, the images data to be saved is current input frame. Otherwise, the data to be saved is background model image. Background model on this system is adapted with the change visual data of background image.The results obtained from this research in the form of a CCTV system that are able to select recording data to be stored with image processing. The background model will be kept adapting with background visual data changes.

Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation Apri Nur Liyantoko; Ika Candradewi; Agus Harjoko
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 9, No 2 (2019): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

Sistem Pengenal Isyarat Tangan Untuk Mengendalikan Gerakan Robot Beroda menggunakan Convolutional Neural Network Habib Astari Adi; Ika Candradewi
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 9, No 2 (2019): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Currently, Human and computer interaction is generally done using a remote control. This approach tends to be impractical for wheeled robot operation because it must always carry an intermediary tool during the operation. The application of hand gesture recognition using digital image processing techniques and machine learning in the control process of wheeled robots will facilitate the control of wheeled robots because control no longer requires an intermediary tool.In this study, hand image taken using a camera then will be processed using a single board computer to be recognized. The results of recognized are passed on to arduino leonardo and DC motor to control twelve wheeled robot movement. The method used in this study is contrast stretching for preprocessing and Convolutional Neural Network (CNN) for hand recognition. This method is tested with a variation of bright 26-140 lux, the distance from the face to the camera is 120-200cm. Hand recognition systems using this method resulting accuracy 97,5%, precision 97,57%, sensitivity 97.5%, spesificity 99,77 and f1 score 97.45%.

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