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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Majalah Ilmiah Teknologi Elektro Jurnal INKOM Proceedings of KNASTIK Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications Seminar Nasional Informatika (SEMNASIF) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING KOMPUTIKA - Jurnal Sistem Komputer Jurnal Abdimasa TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol JMECS (Journal of Measurements, Electronics, Communications, and Systems) Jurnal Teknik Informatika (JUTIF) Almufi Jurnal Pengabdian kepada Masyarakat Prosiding Konferensi Nasional PKM-CSR Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) eProceedings of Engineering Majalah Ilmiah Teknologi Elektro
Koredianto Usman
Universitas Telkom
Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Public Health Social Sciences Other

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Deteksi Plat Nomor Kendaraan dengan Hough Transform dan Harris Corner Menggunakan Akuisisi Melalui Raspberry Pi Noor Akhmad Gilar Romadhon; Koredianto Usman; Raditiana Patmasari
TELKA - Jurnal Telekomunikasi, Elektronika, Komputasi dan Kontrol Vol 6, No 2 (2020): TELKA
Publisher : Jurusan Teknik Elektro UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/telka.v6n2.93-103

Abstract

Dalam suatu tempat, seperti di mall, tempat parkir kendaraan dapat terdiri dari beberapa lantai di basement atau di bagian atas bangunan. Di samping itu juga banyak sektor yang berubah fungsi menjadi tempat parkir. Dalam situasi ini, diperlukan sistem monitoring kendaraan yang fleksibel. Pada penelitian ini dibuat sistem yang dapat mengenali jenis kendaraan berdasarkan warna dasar pada plat nomor apakah kendaraan tersebut merupakan kendaraan pribadi, umum, atau pemerintah. Data berupa citra diambil dengan menggunakan webcam melalui akuisisi Raspberry Pi. Sistem dirancang dengan menggunakan deteksi tepi dan morfologi serta menggunakan metode Hough Transform untuk memperbaiki garis tepi dan Harris Corner untuk mendeteksi sudut pada citra. Selanjutnya dilakukan proses cropping plat. Deteksi warna dasar plat nomor dilakukan dengan menggunakan ruang warna YcbC. Selain itu, dilakukan perhitungan waktu proses akuisisi dan waktu proses keseluruhan ketika Raspberry Pi dan laptop melakukan proses deteksi. Berdasarkan hasil pengujian, sistem mendapatkan hasil terbaik sebesar 100% saat deteksi plat nomor kendaaraan pribadi dan hasil terburuk sebesar 70% saat deteksi plat nomor kendaraan umum. Rata-rata akurasi yang didapatkan pada sistem ini sebesar 88,9%. Akurasi tersebut diperoleh dengan 90 data uji. Rata-rata waktu komputasi adalah 0,99 detik, yang artinya proses pendeteksian dilakukan cukup cepat. In some place, such as in a mall, a vehicle parking lot can consist of several floors in the basement or at the top of a building. In addition, many sectors have also been turned into parking lots. In this situation, a flexible vehicle monitoring system is needed to detect the type of vehicle ownership through the base color on the license plate connected to the Raspberry Pi, which is expected to be able to mitigate this problem. This research develop a system that can recognize the type of vehicle based on the color of the plate number whether the vehicle is a private, public, or government vehicle. The data consists of images taken using a webcam through the acquisition of Raspberry Pi. The system was designed by using edge detection, morphology, the Hough Transform method to correct the edge, and Harris Corner to detect angles in the image, after which the plate cropping process is carried out. The license plate base color is detected using the YCbCr color space, and the system will also measure the quality of the network between the Raspberry Pi and the laptop used during the detection process. The system gets the best results at 100% when detecting private vehicle license plates and the worst results at 70% when detecting public vehicle license plates. The average accuracy obtained in this system is 88,9% that used 90 test data. The average computation time is 0,99 seconds, which means that the detection process is quite fast.
Deteksi Bentuk Objek Bawah Tanah Menggunakan Pengolahan Citra B-Scan pada Ground Penetrating Radar (GPR) Rahmayati Alindra; Heroe Wijanto; Koredianto Usman
TELKA - Jurnal Telekomunikasi, Elektronika, Komputasi dan Kontrol Vol 3, No 1 (2017): TELKA
Publisher : Jurusan Teknik Elektro UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/telka.v3n1.73-83

Abstract

Ground Penetrating Radar (GPR) adalah salah satu jenis radar yang digunakan untuk menyelidiki kondisi di bawah permukaan tanah tanpa harus menggali dan merusak tanah. Sistem GPR terdiri atas pengirim (transmitter), yaitu antena yang terhubung ke generator sinyal dan bagian penerima (receiver), yaitu antena yang terhubung ke LNA dan ADC yang kemudian terhubung ke unit pengolahan data hasil survey serta display sebagai tampilan output-nya dan post  processing untuk alat bantu mendapatkan informasi mengenai suatu objek. GPR bekerja dengan cara memancarkan gelombang elektromagnetik ke dalam tanah dan menerima sinyal yang dipantulkan oleh objek-objek di bawah permukaan tanah. Sinyal yang diterima kemudian diolah pada bagian signal processing dengan tujuan untuk menghasilkan gambaran kondisi di bawah permukaan tanah yang dapat dengan mudah dibaca dan diinterpretasikan oleh user. Signal processing sendiri terdiri dari beberapa tahap yaitu A-Scan yang meliputi perbaikan sinyal dan pendektesian objek satu dimensi, B-Scan untuk pemrosesan data dua dimensi  dan C-Scan untuk pemrosesan data tiga dimensi. Metode yang digunakan pada pemrosesan B-Scan salah satunya adalah dengan  teknik pemrosesan citra. Dengan pemrosesan citra, data survey B-scan diolah untuk didapatkan informasi mengenai objek. Pada penelitian ini, diterapkan teori gradien garis pada pemrosesan citra B-scan untuk menentukan bentuk dua dimensi dari objek bawah tanah yaitu persegi, segitiga atau lingkaran. 
PEOPLE COUNTING FOR PUBLIC TRANSPORTATIONS USING YOU ONLY LOOK ONCE METHOD Tsabita Al Asshifa Hadi Kusuma; Koredianto Usman; Sofia Saidah
Jurnal Teknik Informatika (Jutif) Vol. 2 No. 1 (2021): JUTIF Volume 2, Number 1, June 2021
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2021.2.2.77

Abstract

People counting have been widely used in life, including public transportations such as train, airplane, and others. Service operators usually count the amount of passengers manually using a hand counter. Nowadays, in an era that most of human-things are digital, this method is certainly consuming enough time and energy. Therefore, this research is proposed so the service operator doesn't have to count manually with a hand counter, but using an image processing with You Only Look Once (YOLO) method. This project is expected that people counting is no longer done manually, but already based on computer vision. This Final Project uses YOLOv4 that is the latest method in detecting untill 80 classes of object. Then it will use transfer learning as well to change the number of classes to 1 class. This research was done by using Python programming language with various platforms. This research also used three training data scenarios and two testing data scenarios. Parameters measured are accuration, precision, recall, F1 score, Intersection of Union (IoU), and mean Average Precision (mAP). The best configurations used are learning rate 0.001, random value 0, and sub divisions 32. And the best accuration for this system is 69% with the datasets that has been trained before. The pre-trained weights have 72.68% of accuracy, 77% precision, and 62.88% average IoU. This research has resulted a proper performance for detecting and counting people on public transportations.
WEBINAR STUDENT PRESENCE SYSTEM BASED ON REGIONAL CONVOLUTIONAL NEURAL NETWORK USING FACE RECOGNITION Akbar Trisnamulya Putra; Koredianto Usman; Sofia Saidah
Jurnal Teknik Informatika (Jutif) Vol. 2 No. 2 (2021): JUTIF Volume 2, Number 2, December 2021
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2021.2.2.82

Abstract

World health organization announce Covid-19 as a pandemic so On March 15th 2020, the social distancing has been established with working, learning, and praying from home. Webinar is one of the solutions so those activities still can be done face to face and conference-based. With webinar, users can interact each other in an online meeting from home. Student presence is part of a webinar. The purpose of this research is to design an accurate student presence with a face recognition system using R-CNN method. The object of this research is a human face with sufficient light, medium, and the face must be facing the camera. This research proposed for a webinar student presence system is using face recognition with Regional Convolutional Neural Network (R-CNN). With object detection and several scenarios used in this method, the webinar student presence system using R-CNN will be more accurate than the methods that have ever been used before. This research has done four scenarios to obtain the best parameters like 45 of total layers, test data of the whole dataset percentage as 10%, RMSProp as model op- timizer, and 0.0001 learning rate. With those parameters, it have resulted the best system performance including 99.6% accuration, 1 × 10-4 loss, 100% precision, 99% recall, and 99.5% F1 Score.
THE CALCULATION SYSTEM OF NUMBER OF PEOPLE IN A ROOM BASED ON HUMAN DETECTION USING HAAR-CASCADE CLASSIFIER Gusti Ngurah Rama Putra Atmaja; Koredianto Usman; Muhammad Ary Murti
Jurnal Teknik Informatika (Jutif) Vol. 2 No. 2 (2021): JUTIF Volume 2, Number 2, December 2021
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2021.2.2.83

Abstract

Data of number of people in the room, calculations are usually carried out by assigning someone to oversee a room. In this final project, a system for calculating the number of people in the room is designed with image processing based on human detection that can be used in rooms, both for commercial applications and for security. This system uses Raspberry Pi device that already has an image processing method Haar-Cascade Classifier. Input data is in the form of video taken directly via webcam to be captured into a frame so that it can be used as a input the Haar-Cascade Classifier method and perform the counting process will be sent to the Antares platform. The system design has been tested with five scenarios. Scenario 1 the effect of the distance of the object, scenario 2 the effect of the pose of the object, scenario 3 the effect of the amount the object in the frame, scenario 4 affects the scale factor and scenario 5 measurement computation time. Scenarios 1 to 3 will do the best configuration for minimum neighbour. The system gets the best accuracy of 98,5% when the object distance 4 meters, the best accuracy of 96,6% when the object is facing forward and accuracy the best is 97,7% when the object in the frame is more than two objects with the best configuration use the minimum neighbour 5. Scenario 4 gets accuracy the best is 76,2% when using the scale factor 1.1. Scenario 5 gets the average computation time of the system is under one second, meaning the detection process done pretty fast.
Proyeksi Acak dan Teknik Scanning pada Algoritma Sparse Representation based Classification untuk Pengenalan Wajah Ivy Anindhita Hadyningtyas; Denta Rahmadani; Koredianto Usman; Susmini Indriani Lestariningati
Komputika : Jurnal Sistem Komputer Vol 11 No 2 (2022): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v11i2.7201

Abstract

Sparse Representation based Classification (SRC) merupakan metode yang cukup terkenal dalam pengenalan wajah, karena kemampuannya dalam mengatasi beberapa permasalahan yang terjadi pada pengenalan wajah seperti oklusi, variasi pose, dan berbagai macam ekspresi wajah. SRC memiliki kekurangan yaitu beban komputasi yang berat. Untuk mengatasi kekurangan tersebut, dalam makalah ini kami mengusulkan penurunan dimensi citra untuk mengurangi beban komputasi. Penurunan dimensi yang dilakukan dengan cara mengalikan matriks fitur dengan matriks proyeksi acak. Matriks proyeksi acak tersebut dibangkitkan menggunakan distribusi gaussian, uniform binary, dan uniform integer. Faktor reduksi yang digunakan dalam makalah ini yaitu dari 24 hingga 168. Proyeksi acak tersebut akan dibandingkan dengan metode linear klasik yaitu downscale. Hasil simulasi pada dataset AT&T menunjukkan bahwa faktor reduksi dengan sebesar 10.304 : 128 memiliki tingkat akurasi maksimum 87,5% pada proyeksi random uniform integer, dimana nilai maksimum ini dilakukan secara iterasi. Pada pengujian oklusi, teknik SRC masih dapat mendeteksi citra dengan tingkat oklusi sampai dengan 80%. Dari hasil pengujian teknik scanning yang dilakukan tidak mempengaruhi tingkat akurasi, namun dapat mempengaruhi waktu komputasi. Kata Kunci – Representasi Jarang, Proyeksi Acak, Pengenalan Wajah.
Design and Implementation Pulse Compression for S-Band Surveillance Radar Kalfika Yani; Fiky Y Suratman; Koredianto Usman
JMECS (Journal of Measurements, Electronics, Communications, and Systems) Vol 7 No 1 (2020): JMECS
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v7i1.2631

Abstract

The radar air surveillance system consists of 4 main parts, there are antenna, RF front-end, radar signal processing, and radar data processing. Radar signal processing starts from the baseband to IF section. The radar waveform consists of two types of signal, there are continuous wave (CW) radar, and pulse compression radar [1]. Range resolution for a given radar can be significantly improved by using very short pulses. Pulse compression allows us to achieve the average transmitted power of a relatively long pulse, while obtaining the range resolution corresponding to a short pulse. Pulse compression have compression gain. With the same power, pulse compression radar can transmit signal further than CW radar. In the modern radar, waveform is implemented in digital platform. With digital platform, the radar waveform can optimize without develop the new hardware platform. Field Programmable Gate Array (FPGA) is the best platform to implemented radar signal processing, because FPGA have ability to work in high speed data rate and parallel processing. In this research, we design radar signal processing from baseband to IF using Xilinx ML-605 Virtex-6 platform which combined with FMC-150 high speed ADC/DAC.
Robust Modified MVDR Scheme Using Chirp Signal for Direction of Arrival Estimation Kalfika Yani; Koredianto Usman; Fiky Y Suratman
JMECS (Journal of Measurements, Electronics, Communications, and Systems) Vol 6 No 1 (2020): JMECS
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v6i1.2630

Abstract

This research is about an effort to increase the robustness of the Minimum Variance Distortionless Response (MVDR) algorithm to noise by using a chirp signal for direction of arrival estimation (DoA). DoA is a part of radar capability to estimate the angle of arrival on the object under observation. The conventional MVDR as proposed by J. Capon, was designed to work with the monochromatic sinusoidal signal. Even though the conventional MVDR work on low SNR up to 0 dB, however, the conventional method does not work well if chirp signal is used instead of monochromatic sinusoidal signal. The usage of MVDR chirp signal is essential in the case of a very low SNR environment such as in long distance object detection, which is typically more than 10 km. The problem to be solved in this research is how to modify the MVDR algorithm so that it can work well on chirp signal. In this research we offer a modified MVDR algorithm by adding the matched filter and the phase detector components before the MVDR algorithm is applied. Matched filter is responsible for the timing of the chirp signal detection, and the phase detector is to estimate the time delay estimation of each chirp signal from each antenna with a reference signal, which correspond to the phases. Based on the phase estimation, sinusoidal signal is generated and fed to the MVDR algorithm. On the technical aspect, the chirp signal is sent intermittently with a duration of 100 ?s and repeated in time interval of 1 ms. The antenna sensor using an array of Uniform Linear Array (ULA) which consist of N-elements. Computer simulation shows that the modified MVDR using the chirp signal improve the robustness of the algorithm up to -30 dB, while on the other hand the classical MVDR works only up to 0 dB SNR. -30 dB of SNR is the minimum requirement of 3D Radar existing.
SISTEM INSPEKSI PERMUKAAN BAJA BERBASIS DEEP LEARNING MENGGUNAKAN METODE ANCHOR-FREE Singgih Yuliyanto; Nurinda Fadhilah Amani; Fityanul Akhyar; Koredianto Usman
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol 2 No 3 (2022): November Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Publisher : Sekolah Tinggi Ilmu Ekonomi Trianandra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/juritek.v2i3.364

Abstract

Steel is one of the important materials in the industry. Steel may have defects in the production process that can affect the steel products. Therefore, the detection of steel surface defects is an important process to control the quality of steel products. An efficient steel surface detection process is carried out by automating steel images taken using a camera. We use an anchor-free model FoveaBox. FoveaBox is an accurate and flexible model for detecting objects and has a simple architecture. This study uses the NEU-DET dataset consists of six types of steel surface defects, namely crazing, inclusion, patches, pitted surface, rolled-in scale, and scratches, each with a total of 300 data.. The test results on the system show that the method used has a good detection performance with a mean average precision of 0.834 or 83.4% at a learning rate of 0.001, Optimizer SGD, sigma 0.6, and the number of epochs 24. This detection method can detect steel surface defects. This detection method can effectively detect steel surface defects with similar foreground and background characteristics. With an accuracy threshold of 80%, the method used in this study has an adequate precision value.
Klasifikasi Kendaraan Roda Empat Dengan Ekstraksi Ciri Hybrid Berbasis Jaringan Syaraf Tiruan Gryaningrum Widi Pangestuti; Koredianto Usman; Bedy Purnama
eProceedings of Engineering Vol 3, No 2 (2016): Agustus, 2016
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kepadatan volume kendaraan sudah menimbulkan efek yang buruk. Kemacetan dan pencemaran lingkungan adalah dampak yang sulit dihindari dari bertambahnya kendaraan bermotor di berbagai daerah. Untuk mempermudah pengolahan data statistik pertumbuhan kendaraan diperlukan sebuah program yang dapat mengelompokkan kendaraan-kendaraan tersebut secara otomatis. Dalam Tugas Akhir ini kendaraan beroda empat atau lebih akan dikelompokkan ke dalam tiga kelompok yaitu sedan, mini bus, dan mobil besar. Untuk membedakan ketiga jenis tersebut diperlukan ciri yang bisa membedakan ketiga kelompok tersebut dengan baik. Metode ekstraksi ciri hybrid yang digunakan adalah dengan menggabungkan ciri ukuran dan warna dari setiap kendaraan. Selanjutnya akan dilatih dan diuji dengan menggunakan algoritma jaringan syaraf tiruan Radial Basis Function (JST RBF). Klasifikasi kendaraan didapat setelah melalui berbagai tahap preprocesing hingga menghasilkan objek kendaraan saja. Setelah itu dilakukan pencarian nilai parameter JST RBF agar memberikan hasil yang maksimal. Nilai spread 0.4 dan jumlah pusat maksimal dapat memberikan hasil yang cukup baik. Hasil pengujian pun akhirnya dapat mencapai nilai akurasi sebesar 77.52%. Kata kunci : klasifikasi kendaraan, ekstraksi ciri hybrid, JST RBF
Co-Authors Achmad Rizal Achmad Rizal Achmad, Fariz Adam Agus Kurniawan Afif Priyambodo Akbar Trisnamulya Putra Akhmad Yusuf Nasirudin Alfarizi, Muhammad Alfian Andriyan B. Suksmono Andriyan Bayu Suksmono, Andriyan Bayu ARIS HARTAMAN Bambang Hidayat Bedy Purnama Budiyanto, Anggara Chelsya Dwi Marnelius Denny Darlis Denta Rahmadani Eriel Mar Fiky Y. Suratman Fityanul Akhyar Gryaningrum Widi Pangestuti Gusti Ngurah Rama Putra Atmaja Hanung Tyas Saksono Hendra Gunawan Hendra Gunawan Heri Syahrian, Heri Heroe Wijanto Huljannah, Miftah Humayra, Tia Hasna Imanadi, Muhammad Tsabit Imanadi Ivy Anindhita Hadyningtyas Jangkung Raharjo Julyano , Muhammad Billy Kalfika Yani Ledya Novamizanti Maharani , Kartika Dwi Maharani, Kartika Dwi Mochamad Noverian Zhafar Muhammad Ary Murti Noor Akhmad Gilar Romadhon Nor Kumalasari Caecar Pratiwi Nur Ibrahim Nurinda Fadhilah Amani Nurinda Fadhilah Amani Putranto, Devdan Wisesa Putri Marito R. Irawan Satria Putra R. Yunendah Nur Fu’adah Radhibilla, Maulaya Radian Sigit Raditiana Patmasari Rahmayati Alindra Razief Moch Diar Rita Magdalena Runik Machfiroh Rustam Sigit, Radian Singgih Yuliyanto SOFIA SAIDAH Sugondo Hadiyoso Suryo Adhi Wibowo Susmini Indriani Lestariningati Syahanifa , Nancy Olivia Syahanifa, Nancy Olivia Tita Haryanti Tsabita Al Asshifa Hadi Kusuma Victor Aji Admaja Pellokila Vitria Puspitasari Rahadi Wardani , Shania Yuliyanto, Singgih