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Journal of ICT Research and Applications
Published by Institut Teknologi Bandung
ISSN : 23375787     EISSN : 23385499     DOI : https://doi.org/10.5614/itbj.ict.res.appl.
Core Subject : Science,
Computer Science & IT
Journal of ICT Research and Applications welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless & Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 302 Documents
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Optimization of Spaced K-mer Frequency Feature Extraction using Genetic Algorithms for Metagenome Fragment Classification Arini Pekuwali; Wisnu Ananta Kusuma; Agus Buono
Journal of ICT Research and Applications Vol. 12 No. 2 (2018)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2018.12.2.2

Abstract

K-mer frequencies are commonly used in extracting features from metagenome fragments. In spite of this, researchers have found that their use is still inefficient. In this research, a genetic algorithm was employed to find optimally spaced k-mers. These were obtained by generating the possible combinations of match positions and don't care positions (written as *). This approach was adopted from the concept of spaced seeds in PatternHunter. The use of spaced k-mers could reduce the size of the k-mer frequency feature's dimension. To measure the accuracy of the proposed method we used the naïve Bayesian classifier (NBC). The result showed that the chromosome 111111110001, representing spaced k-mer model [111 1111 10001], was the best chromosome, with a higher fitness (85.42) than that of the k-mer frequency feature. Moreover, the proposed approach also reduced the feature extraction time. 
A Printed PAW Image Database of Arabic Language for Document Analysis and Recognition Bilal Bataineh
Journal of ICT Research and Applications Vol. 11 No. 2 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.2.6

Abstract

Document image analysis and recognition are important topics in the field of artificial intelligence. In this context, the availability of a database with good script samples is an important requirement for machine-learning processes. For Latin and Asian languages many suitable databases exist. However, there is a shortage of databases with Arabic samples. In this work, a new database of printed Arabic text is introduced. The new concept of collecting sub-words (PAWs) instead of words or individual character samples was adopted. These PAWs constitute all words in the Arabic language. The collected database consists of 83,056 images of PAWs extracted from approximately 550,000 different words. Each sample is presented in the database in five font types: Thuluth, Naskh, Andalusi, Typing Machine, and Kufi. In total, the database consists of 415,280 images. Moreover, ground truth information is included with each PAW image to describe its occurrence number, occurrence frequency, positions and the shapes of the characters. This paper presents a statistical analysis of the frequency of each PAW in the Arabic language.
Improving Floating Search Feature Selection using Genetic Algorithm Kanyanut Homsapaya; Ohm Sornil
Journal of ICT Research and Applications Vol. 11 No. 3 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.3.6

Abstract

Classification, a process for predicting the class of a given input data, is one of the most fundamental tasks in data mining. Classification performance is negatively affected by noisy data and therefore selecting features relevant to the problem is a critical step in classification, especially when applied to large datasets. In this article, a novel filter-based floating search technique for feature selection to select an optimal set of features for classification purposes is proposed. A genetic algorithm is employed to improve the quality of the features selected by the floating search method in each iteration. A criterion function is applied to select relevant and high-quality features that can improve classification accuracy. The proposed method was evaluated using 20 standard machine learning datasets of various size and complexity. The results show that the proposed method is effective in general across different classifiers and performs well in comparison with recently reported techniques. In addition, the application of the proposed method with support vector machine provides the best performance among the classifiers studied and outperformed previous researches with the majority of data sets.
Sparse Signal Reconstruction using Weight Point Algorithm Koredianto Usman; Hendra Gunawan; Andriyan B. Suksmono
Journal of ICT Research and Applications Vol. 12 No. 1 (2018)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2018.12.1.3

Abstract

In this paper we propose a new approach of the compressive sensing (CS) reconstruction problem based on a geometrical interpretation of l1-norm minimization. By taking a large l1-norm value at the initial step, the intersection of l1-norm and the constraint curves forms a convex polytope and by exploiting the fact that any convex combination of the polytope's vertexes gives a new point that has a smaller l1-norm, we are able to derive a new algorithm to solve the CS reconstruction problem. Compared to the greedy algorithm, this algorithm has better performance, especially in highly coherent environments. Compared to the convex optimization, the proposed algorithm has simpler computation requirements. We tested the capability of this algorithm in reconstructing a randomly down-sampled version of the Dow Jones Industrial Average (DJIA) index. The proposed algorithm achieved a good result but only works on real-valued signals.
Emotion Recognition from Facial Expressions using Images with Pose, Illumination and Age Variation for Human-Computer/Robot Interaction Suja Palaniswamy; Shikha Tripathi
Journal of ICT Research and Applications Vol. 12 No. 1 (2018)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2018.12.1.2

Abstract

A technique for emotion recognition from facial expressions in images with simultaneous pose, illumination and age variation in real time is proposed in this paper. The basic emotions considered are anger, disgust, happy, surprise, and neutral. Feature vectors that were formed from images from the CMU-MultiPIE database for pose and illumination were used for training the classifier. For real-time implementation, Raspberry Pi II was used, which can be placed on a robot to recognize emotions in interactive real-time applications. The proposed method includes face detection using Viola Jones Haar cascade, Active Shape Model (ASM) for feature extraction, and AdaBoost for classification in real- time. Performance of the proposed method was validated in real time by testing with subjects from different age groups expressing basic emotions with varying pose and illumination. 96% recognition accuracy at an average time of 120 ms was obtained. The results are encouraging, as the proposed method gives better accuracy with higher speed compared to existing methods from the literature. The major contribution and strength of the proposed method lie in marking suitable feature points on the face, its speed and invariance to pose, illumination and age in real time.
Automatic Title Generation in Scientific Articles for Authorship Assistance: A Summarization Approach Jan Wira Gotama Putra; Masayu Leylia Khodra
Journal of ICT Research and Applications Vol. 11 No. 3 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.3.3

Abstract

This paper presents a studyon automatic title generation for scientific articles considering sentence information types known as rhetorical categories. A title can be seenas a high-compression summary of a document. A rhetorical category is an information type conveyed by the author of a text for each textual unit, for example: background, method, or result of the research. The experiment in this studyfocused on extracting the research purpose and research method information for inclusion in a computer-generated title. Sentences are classifiedinto rhetorical categories, after which these sentences are filtered using three methods. Three title candidates whose contents reflect the filtered sentencesare then generated using a template-based or an adaptive K-nearest neighbor approach. The experiment was conducted using two different dataset domains: computational linguistics and chemistry. Our study obtained a 0.109-0.255 F1-measure score on average for computer-generated titles compared to original titles. In a human evaluation the automatically generated titles were deemed 'relatively acceptable' in the computational linguistics domain and 'not acceptable' in the chemistry domain. It can be concluded that rhetorical categories have unexplored potential to improve the performance of summarization tasks in general.
An Inter-Processor Communication (IPC) Data Sharing Architecture in Heterogeneous MPSoC for OFDMA Trio Adiono; Rian Ferdian; Febri Dawani; Imran Abdurrahman; Rachmad Vidya Wicaksana Putra; Nur Ahmadi
Journal of ICT Research and Applications Vol. 12 No. 1 (2018)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2018.12.1.5

Abstract

Multiprocessor system-on-chip (MPSoC) promises better data management for parallel processing than conventional SoC. This feature is very suitable for wireless communication systems. Better data processing management can reduce resource utilization and can potentially reduce power consumption as well. Hence, this research aimed to minimize the orthogonal frequency-division multiple access (OFDMA) processing hardware by proposing a new data sharing architecture on a heterogeneous MPSoC platform that incorporates inter-processor communication (IPC), multi-processor, multi-bus, multi-frequency and parallel processing design of the medium access controller (MAC) layer. This MPSoC was designed based on a RISC processor with an AMBA multi-bus system. To achieve high throughput, the proposed MPSoC runs at two different frequencies, 40 MHz and 80 MHz. The proposed system was implemented and verified using FPGA. The verification results showed that the proposed system can work in real-time with a maximum throughput of 11 MBps using a 40 MHz system clock. The proposed MPSoC is a promising solution to perform OFDMA processing on 4G and 5G technologies.
Improvement of Fluid Simulation Runtime of Smoothed Particle Hydrodynamics by Using Graphics Processing Unit (GPU) Wahyu Srigutomo; Ruddy Kurnia; Suprijadi Suprijadi
Journal of ICT Research and Applications Vol. 11 No. 3 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.3.2

Abstract

This study concerns an implementation of smoothed particle hydrodynamics (SPH) fluid simulation on a graphics processing unit (GPU) using the Compute Unified Device Architecture's (CUDA) parallel programming. A bookkeeping method for the neighbor search algorithm was incorporated to accelerate calculations. Based on sequence code profiling of the SPH method, particle interaction computation "“ which comprises the calculation of the continuity equation and the momentum conservation equation "“ consumes 95.2% of the calculation time. In this paper, an improvement of the calculation is proposed by calculating the particle interaction part on the GPU and by using a bookkeeping algorithm to restrict the calculation only to contributed particles. Three aspects are addressed in this paper: firstly, speed-up of the CUDA parallel programming computation as a function of the number of particles used in the simulation; secondly, the influence of double precision and single precision schemes on the computational acceleration; and thirdly, calculation accuracy with respect to the number of particles. Scott Russell's wave generator was implemented for a 2D case and a 3D dam-break. The results show that the proposed method was succesfull in accelerating the SPH simulation on the GPU.
Cover JICTRA Vol. 7 No. 1, 2013 Journal of ICT Research and Applications
Journal of ICT Research and Applications Vol. 7 No. 1 (2013)
Publisher : LPPM ITB

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

Abstract

Cover JICTRA Vol. 7 No. 2, 2013 Journal of ICT Research and Applications
Journal of ICT Research and Applications Vol. 7 No. 2 (2013)
Publisher : LPPM ITB

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

Abstract

Page 17 of 31 | Total Record : 302

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