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PERFORMANCE COMPARISON OF USART COMMUNICATION BETWEEN REAL TIME OPERATING SYSTEM (RTOS) AND NATIVE INTERRUPT Novian Habibie; Machmud Roby Alhamidi; Dwi Marhaendro Jati Purnomo; Muhammad Febrian Rachmadi
Jurnal Ilmu Komputer dan Informasi Vol 9, No 1 (2016): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (376.107 KB) | DOI: 10.21609/jiki.v9i1.365

Abstract

Comunication between microcontrollers is one of the crucial point in embedded sytems. On the other hand, embedded system must be able to run many parallel task simultaneously. To handle this, we need a reliabe system that can do a multitasking without decreasing every task’s performance. The most widely used methods for multitasking in embedded systems are using Interrupt Service Routine (ISR) or using Real Time Operating System (RTOS). This research compared perfomance of USART communication on system with RTOS to a system that use interrupt. Experiments run on two identical development board XMega A3BU-Xplained which used intenal sensor (light and temperature) and used servo as external component. Perfomance comparison done by counting ping time (elapsing time to transmit data and get a reply as a mark that data has been received) and compare it. This experiments divided into two scenarios: (1) system loaded with many tasks, (2) system loaded with few tasks. Result of the experiments show that communication will be faster if system only loaded with few tasks. System with RTOS has won from interrupt in case (1), but lose to interrupt in case (2).
PARTICLE SWARM OPTIMIZATION (PSO) FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN) Arie Rachmad Syulistyo; Dwi Marhaendro Jati Purnomo; Muhammad Febrian Rachmadi; Adi Wibowo
Jurnal Ilmu Komputer dan Informasi Vol 9, No 1 (2016): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (313.927 KB) | DOI: 10.21609/jiki.v9i1.366

Abstract

Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO) in Convolutional Neural Networks (CNNs), which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN.  The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.  
BEAGLEBOARD EMBEDDED SYSTEM FOR ADAPTIVE TRAFFIC LIGHT CONTROL SYSTEM WITH CAMERA SENSOR Muhammad Febrian Rachmadi; F Al Afif; M Anwar Ma'sum; M Fajar; A Wibowo
Jurnal Ilmu Komputer dan Informasi Vol 5, No 2 (2012): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1268.713 KB) | DOI: 10.21609/jiki.v5i2.190

Abstract

Traffic is one of the most important aspects in human daily life because traffic affects smoothness of capital flows, logistics, and other community activities. Without appropriate traffic light control system, possibility of traffic congestion will be very high and hinder people’s life in urban areas. Adaptive traffic light control system can be used to solve traffic congestions in an intersection because it can adaptively change the durations of green light each lane in an intersection depend on traffic density. The proposed adaptive traffic light control system prototype uses Beagleboard-xM, CCTV camera, and AVR microcontrollers. We use computer vision technique to obtain information on traffic density combining Viola-Jones method with Kalman Filter method. To calculate traffic light time of each traffic light in intersection, we use Distributed Constraint Satisfaction Problem (DCSP). From implementations and experiments results, we conclude that BeagleBoard-xM can be used as main engine of adaptive traffic light control system with 91.735% average counting rate. Lalu intas adalah salah satu aspek yang paling penting dalam kehidupan sehari-hari manusia karena lalu lintas memengaruhi kelancaran arus modal, logistik, dan kegiatan masyarakat lainnya. Tanpa sistem kontrol lampu lalu lintas yang memadai, kemungkinan kemacetan lalu lintas akan sangat tinggi dan menghambat kehidupan masyarakat di perkotaan. Sistem kontrol lampu lalu lintas adaptif dapat digunakan untuk memecahkan kemacetan lalu lintas di persimpangan karena dapat mengubah durasi lampu hijau di setiap persimpangan jalan tergantung pada kepadatan lalu lintas. Prototipe sistem kontrol lampu lalu lintas menggunakan BeagleBoard-XM, kamera CCTV, dan mikrokontroler AVR. Peneliti menggunakan teknik computer vision untuk mendapatkan informasi tentang kepadatan lalu lintas dengan menggabungkan metode Viola-Jones dan metode Filter Kalman. Untuk menghitung waktu setiap lampu lalu lintas di persimpangan, peneliti menggunakan Distributed Constraint Satisfaction Problem (DCSP). Dari hasil implementasi dan percobaan dapat disimpulkan bahwa BeagleBoard-XM dapat digunakan sebagai mesin utama sistem kontrol lampu lalu lintas adaptif dengan tingkat akurasi penghitungan rata-rata sebesar 91.735%.
SIMULATION OF LANDMARK APPROACH FOR WALL FOLLOWING ALGORITHM ON FIRE-FIGHTING ROBOT USING V-REP Sumarsih Condroayu Purbarani; Qurrotin A’yunina; Muhammad Anwar Ma’sum; Muhammad Febrian Rachmadi
Jurnal Ilmu Komputer dan Informasi Vol 8, No 2 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (737.768 KB) | DOI: 10.21609/jiki.v8i2.308

Abstract

Autonomous mobile robot has been implemented to assist humans in their daily activity. Autonomous robots have also contributed significantly in human safety. Autonomous mobile robot have been implemented to assist humans in their daily activity. Autonomous robots Have also contributed significantly in human safety. An example of the autonomous robot in the human safety sector is the fire fighting robot, which is the main topic of this paper. As an autonomous robot, the fire fighting robot needs a robust navigation ability to execute a given task in the shortest time interval. Wall-following algorithm is one of several navigating algorithm that simplifies this autonomous navigation problem. As a contribution, we propose two methods that could be combined to make the existing wall-following algorithm more robust. The combined wall-flowing algorithm will be compared to the original wall-following algorithm. By doing so, we could determine which method has more impact on the robot’s navigation robustness. Our goal is to see which method is more effective when combined with the wall-following algorithm.
COMPARISON OF IMAGE ENHANCEMENT METHODS FOR CHROMOSOME KARYOTYPE IMAGE ENHANCEMENT Dewa Made Sri Arsa; Grafika Jati; Agung Santoso; Rafli Filano; Nurul Hanifah; Muhammad Febrian Rachmadi
Jurnal Ilmu Komputer dan Informasi Vol 10, No 1 (2017): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (612.028 KB) | DOI: 10.21609/jiki.v10i1.445

Abstract

The chromosome is a set of DNA structure that carry information about our life. The information can be obtained through Karyotyping. The process requires a clear image so the chromosome can be evaluate well. Preprocessing have to be done on chromosome images that is image enhancement. The process starts with image background removing. The image will be cleaned background color. The next step is image enhancement. This paper compares several methods for image enhancement. We evaluate some method in image enhancement like Histogram Equalization (HE), Contrast-limiting Adaptive Histogram Equalization (CLAHE), Histogram Equalization with 3D Block Matching (HE+BM3D), and basic image enhancement, unsharp masking. We examine and discuss the best method for enhancing chromosome image. Therefore, to evaluate the methods, the original image was manipulated by the addition of some noise and blur. Peak Signal-to-noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to examine method performance. The output of enhancement method will be compared with result of Professional software for karyotyping analysis named Ikaros MetasystemT M . Based on experimental results, HE+BM3D method gets a stable result on both scenario noised and blur image. 
PEER ASSESSMENT RATING (PAR) INDEX CALCULATION ON 2D DENTAL MODEL IMAGE FOR OVER JET, OPEN BITE, AND TEETH SEGMENTATION ON OCCLUSION SURFACE Muhammad Febrian Rachmadi; Ratna Rustamadji; Miesje Karmiati Purwanegara; sani Muhammad Isa; Benny Hardjono
Jurnal Ilmu Komputer dan Informasi Vol 7, No 1 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1186.131 KB) | DOI: 10.21609/jiki.v7i1.256

Abstract

Abstract Malocclusion is a clinical symptom, in which the teeth of maxilla and mandible are not located at the proper location. If malocclusion left untreated, it can lead to complications in the digestive system, headache, and periodontal disease disorders. Malocclusion problems involving abnormalities of teeth, bones, and muscles around the jaw are obligation of orthodontic specialists to treat them. The treatments can be varying based on the type of malocclusion, including tooth extraction and tooth braces. To know certain degree of malocclusion experienced by the patient, an assessment method called Peer Assessment Rating (PAR) Index is usually used by the specialist. To help the works of orthodontic specialists in Indonesia, a new automated calculation system based on 2D image of tooth model for PAR Index is being developed. In this paper, the calculation system for over-jet, open-bite, and teeth segmentation is developed. The result of the developed system is then compared with manual assessment done by orthodontic specialist, in order to verify the accuracy of the system.
Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation Jatmiko, Wisnu; Setiawan, I Md. Agus; Akbar, Muhammad Ali; Suryana, Muhammad Eka; Wardhana, Yulistiyan; Rachmadi, Muhammad Febrian
Makara Journal of Technology Vol. 20, No. 2
Publisher : UI Scholars Hub

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

Abstract

Cardiac disease is one of the major causes of death in the world. Early diagnose of the symptoms depends on abnormality on heart beat pattern, known as Arrhythmia. A novel fuzzy neuro generalized learning vector quantization for automatic Arrhythmia heart beat classification is proposed. The algorithm is an extension from the GLVQ algorithm that employs a fuzzy logic concept as the discriminant function in order to develop a robust algorithm and improve the classification performance. The algorithm is tested against MIT-BIH arrhythmia database to measure the performance. Based on the experiment result, FN-GLVQ is able to increase the accuracy of GLVQ by a soft margin. As we intend to build a device with automated Arrhythmia detection, FN-GLVQ is then implemented into Field Gate Programmable Array to prototype the system into a real device.
Astronomical Image Denoising Using AttentionGAN Naufal, Faishal Zaka; Rachmadi, Muhammad Febrian; Krisnadhi, Adila Alfa
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v10i1.13154

Abstract

Denoising astronomical images is a significant challenge in the field of astronomical data processing. Image data acquired from astronomical sources typically contains noise from various sources. The study aims to investigate the denoising of astronomical images using an image-to-image translation approach with AttentionGAN method. This method combines attention-guided techniques with a Generative Adversarial Network (GAN) model to improve the quality of noisy astronomical images. Attention-guided technique allows the model to learn the most important features of the image and guide the image generation process. This approach has been tested on several images in different domains, each with varying levels of noise. The results shows that AttentionGAN method produces denoised images with better and sharper quality than several other denoising methods. Two databases, The Panoramic Survey Telescope and Rapid Response System (PAN-STARRS) and the Sloan Digital Sky Survey (SDSS), were used in this research. Images acquired from PAN-STARRS contain noise, while images acquired from SDSS are clean. Overall, this research contributes to improving the quality of astronomical images by demonstrating the effectiveness of the AttentionGAN method in denoising noisy astronomical images. We employed denoising techniques using CycleGAN and AttentionGAN and evaluated them using metrics such as PSNR, SSIM, and FID. The analysis showed that the AttentionGAN model outperformed CycleGAN. We also conducted ablation studies to further investigate the components of the AttentionGAN model. This study provides a foundation for future research in the field of astronomical data processing, which has the potential to enhance image quality.