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Agus Budi Dharmawan Agus Budi Dharmawan
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EKSTRAKSI CIRI FEATURE POINT UNTUK PENGENALAN TANDA TANGAN DENGAN MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK Adrian Primanta S; Chairisni Lubis; Agus Budi Dharmawan
Jurnal Ilmu Komputer dan Sistem Informasi Vol 2, No 1 (2014): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v2i1.3164

Abstract

In this research, the main goal is to make an application to recognize people signature. To make this, we need the method to do our research. Backpropagation Neural Network that is used to train image sample and Feature Point Extraction for feature extraction. This method work with center point of the pixel image. The pre-processing are image binarization, Median filter, Thinning image with Zhang Suen algorithm, cropping, and  scaling for training image.. The method  for feature extraction is feature point extraction. This method works with counting the centeroid of the image. Number of total point centeroid is decide by depth value. The process will stop if the total point centroid is achived. After feature extraction, it will continue to train the sample image using Backpropagation Neural Network. Key wordsBackpropagation, Featurte Point Extraction, Image Binarization, Median Filter, Thinning, Threshold.
PENGENALAN WAJAH MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK DAN EKSTRAKSI CIRI INDEPENDENT COMPONENT ANALYSIS Filipus Hanung Nugroho; Tony Tony; Agus Budi Dharmawan
Jurnal Ilmu Komputer dan Sistem Informasi Vol 2, No 2 (2014): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v2i2.3206

Abstract

Face recognition is one way that can be used to identify a person. Recognize a person's face is an easy thing to do for humans. But face recognition is a difficult thing to do by intelligent machines, such as computers. The purpose of this application is to see the performance of the ICA method. The results of this experiment showed that the ICA method produces a higher success rate. ICA method produces a good success rate with a 78%-100% success rate. Key words Backpropagation Neural Network, Face Recognition, Independent Component Analysis
APLIKASI PENGENALAN KARAKTER TULISAN TANGAN DENGAN EKSTRAKSI CIRI DWT Yusten Wuntoro; Chairisni Lubis; Agus Budi Dharmawan
Jurnal Ilmu Komputer dan Sistem Informasi Vol 1, No 1 (2013): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v1i1.3100

Abstract

Some research indicates that every person's handwriting is unique and can be recognized by special software. To translate handwritten characters then made an application to the method of DWT (Discrete Wavelet Transform). The process is done in recognition of handwritten characters is done transferring pictures to a computer handwritten, pre-processing by using grayscale, thresholding, normalization size, thinning, edge detection Sobel, slant angle that is used when a character italics, the feature extraction Discrete Wavelet Transform is used and recognition using Euclidean Distance and Manhattan Distance. Test results from all capital letters to 260 data with 208 training data and 52 55% of the successful introduction of data on Haar lv 1, 52% in Haar lv 2, 48% on Daubechies lv 1, and 67% on Daubechies lv 2.
PEMBUATAN APLIKASI DAN DATABASE KERUSAKAN PADA MOTOR UNTUK SISTEM PAKAR Nikolas Patrick Fernando; Chairisni Lubis; Agus Budi Dharmawan
Jurnal Ilmu Komputer dan Sistem Informasi Vol 5, No 1 (2017): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v5i1.784

Abstract

Sistem pakar adalah sistem yang mengambil pengetahuan manusia dan memasukanya ke komputer sehingga komputer dapat menyelesaikan masalah seperti yang biasa dilakukan oleh para ahli Aplikasi komputer ditujukan untuk membantu mengambil keputusan persoalan dalam bidang tertentu. Pengambilan keputusan tersebut dengan cara menggabungkan kesimpulan dengan basis pengetahuan yang diberikan oleh satu atau lebih pakar dalam bidang tertentu.
PENGENALAN BENTUK TANGAN DENGAN METODE PCA DAN NFL SEBAGAI PENGGANTI MOUSE KOMPUTER Willy Wijaya; Chairisni Lubis; Agus Budi Dharmawan
Jurnal Ilmu Komputer dan Sistem Informasi Vol 5, No 1 (2017): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v5i1.795

Abstract

Humans who always do the daily activities with computers, need some ways and means to be able to interact (give inputs and receive outputs) with the computers. This program is made so the people can interact with the computers, using their hand gestures instead of a computer mouse. The program uses the hand image as an input. And then the program uses Principal Component Analysis and Nearest Feature Line methods to recognize user’s hand gestures. The testing of the program is carried out with four scenarios. The first and second scenario with Database of the hand images of one man, show hand gesture recognition success rate of 92% and 76%, respectively. Then the follow-up testing is carried out with two more additional scenarios. The third and fourth scenario is hand gesture recognition testing with Database of the hand images of two people combined (balanced composition).
PENGENALAN TULISAN TANGAN DENGAN PERBAIKAN GORESAN MENGGUNAKAN INTERPOLASI BEZIER DAN SMOOTHING Ronald Ronald; Chairisni Lubis; Agus Budi Dharmawan
Jurnal Ilmu Komputer dan Sistem Informasi Vol 1, No 1 (2013): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v1i1.3087

Abstract

This system can process images in the form of letters handwriting into digital text writing and at the time of processing, the damage from the handwriting image repaired first in order to be a form letter similar to the actual shape of the letter so that it can add recognition accuracy in using Bezier interpolation and smoothing scratches on image which is then carried handwriting feature extraction process with Global Histogram and then calculated the distance of the feature extraction with Manhattan distance or Euclidean Distance.Test results using Manhattan Distance that can achieve 80% recognition rate on number reognition, greater than the Euclidean Distance that can achieve 70% recognition rate on number recognition. The successful recognition of many affected by the similarities in the character of handwriting. In particular the test database and test images are split between uppercase, lowercase and numbers, it appears that the percentage of the recognition  improved quite a lot.
KAJIAN TENTANG APLIKASI AUGMENTED REALITY BERBASIS MARKER Debora Melinda; Lina Lina; Agus Budi Dharmawan
Jurnal Ilmu Komputer dan Sistem Informasi Vol 4, No 1 (2016): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (87.879 KB) | DOI: 10.24912/jiksi.v4i1.142

Abstract

In this paper,  marker detection and pose estimation algorithms are presented for Augmented Reality applications. This marker detection algorithm was designed specially for square-shaped marker. It consists of line detection, corner detection, and square-shaped detection. The pose estimation algorithm is used involving intrinsic and ecstrinsic paramater of camera. The position of a marker can be known from the ectrinsic parameter of camera which is translation and rotation.  Translation and rotation ocurr in three coordinate axes, which is x,y,and z.The translation value can be obtained from the midpoint of the marker, and the rotation value can be calculated with rotation matrix. Several experiments have  been conducted on various images and video sequences. The results of the experiments show that the algorithms can detect marker in various angles and estimate the pose well that the user of the application can interact with the object from digital world.
KAJIAN TENTANG APLIKASI AUGMENTED REALITY DENGAN DETEKSI TELAPAK TANGAN Jun Can; Agus Budi Dharmawan; Lina Lina
Jurnal Ilmu Komputer dan Sistem Informasi Vol 4, No 1 (2016): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v4i1.157

Abstract

In this application, performed in real time by using a camera to detect hand so it can are added a 3-dimensional object. Stages in this application is the first to capture the image of hand on the camera and then converted into YCbCr image to facilitate detection of hand. Then the image of YCbCr classified in the form of hand and not a hand with a change to binary image according to the range of values Cb and Cr. After that, the image thinning using the method thinning zhang zuen. Image thinning, will recognize hand area by looking for the top, left, right and bottom hand detected. After that, will be calculated translational hand detected from a central point and determine the rotation, so that the object can be added above hand. Based on the test results can be detected with a good hand if a bright light on hand and object are added doesn’t match the rotation of hand because it can’t recognize the fingers.
RASPBERRY PI UNTUK PENGENDALIAN MIKROKONTROLER PADA PERGERAKAN ROBOT PEMANTAU BERKAKI ENAM Satria Ramadhan; Lina Lina; Agus Budi Dharmawan
Jurnal Ilmu Komputer dan Sistem Informasi Vol 3, No 2 (2015): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v3i2.3326

Abstract

Designed system is a six-legged monitoring robot that can be controlled through wireless network. The six-legged robot is designed to use a Raspberry Pi processor and a Basic Stamp 2 microcontroller type. The six-legged robot uses 12 motor servo which each leg has 2 degrees of freedom. The six-legged robot can move using the Wave Gait motion, the Tripod Gait motion, and the Ripple Gait motion that were programmed using a programming language PBASIC. The Raspberry Pi was paired with a Raspberry Pi camera to capture images and a wireless adapter to catch the wireless network signal. The programming language for the six-legged robot is Python. User can give order to the robot to run the motion via Raspberry Pi’s command terminal remotely via a wireless network using PuTTY. User also can capture and record images through a Raspberry Pi’s command terminal. Key wordsBasic Stamp 2, Hexapod, Raspberry Pi, Ripple Gait, Tripod Gait, Wave Gait.
PENGENALAN PEMBICARA DENGAN METODE MEL FREQUENCY CEPSTRUM COEFFICIENTS, MANHATTAN DISTANCE DAN EUCLIDEAN DISTANCE Immanuel Chandra; Chairisni Lubis; Agus Budi Dharmawan
Jurnal Ilmu Komputer dan Sistem Informasi Vol 1, No 2 (2013): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v1i2.3135

Abstract

Teknologi  biometrik merupakan suatu teknik pengenalan diri menggunakan bagian tubuh atau perilaku manusia.Sistem biometrik yang akan digunakan berbasis pengenalan pembicara yang dapat memprediksi suara seseorang. Dengan menginput suara yang direkam, akan dilakukan pengekstraksian ciri, pengenalan terhadap input dengan tahap pelatihan. Sehingga dapat mengetahui suara dari individu yang direkam.Karena itu sebagai solusinya dibuatlah perangkat lunak untuk pengenalan biometrik khususnya bagian suara dengan menggunakan metode Mel Frequency Cepstrum Coefficients (MFCC), Manhattan Distance dan Euclidean Distance. Proses pertama pada sistem ini yaitu dilakukan ekstraksi ciri pada suara yang diinput untuk mendapatkan ciri penting dari setiap suara yang kemudian disimpan kedalam basis data. Pengenalan dilakukan dengan Manhattan Distance dan Euclidean Distance dengan menghitung jarak antara nilai bobot yang didapatkan pada proses pembelajaran dengan nilai yang telah diekstraksi oleh MFCC dengan yang berada di dalam basis data. Kata Kunci:Mel Frequency Cepstrum Coefficeints, Euclidean distance, Manhattan Distance, Pengenalan Suara
Co-Authors Adrian Primanta S Alvin Christo Tjiptiadi Asep Darmawan Benny Karnadi Bryce Clement Sutedja Chairisni Lubis Chavia Rossyerin Prabowo Sutjiadi Christ Bastian Waruwu Daniel Antineus Debora Melinda Dedi Trisnawarman Donni Suharyanto Fanjie Hidayat Filipus Hanung Nugroho Immanuel Chandra Jun Can Kelvin Samuel Lina Lina Matthew Patrick Mulyadi Mulyadi Nikolas Patrick Fernando Novario Jaya Perdana Olivia Prima Putri Randy Oetoroputra Renaldy Ronald Ronald Satria Ramadhan Sifra M.B. Pattiasina Susany Soplanit Tony Tony Veronica Santoso Khalim Willy Wijaya Yusten Wuntoro