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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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Design and Implementation of Moving Object Visual Tracking System using μ-Synthesis Controller Saripudin Saripudin; Modestus Oliver Asali; Bambang Riyanto Trilaksono; Toto Indriyanto
Journal of ICT Research and Applications Vol. 13 No. 3 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.3.1

Abstract

Considering the increasing use of security and surveillance systems, moving object tracking systems are an interesting research topic in the field of computer vision. In general, a moving object tracking system consists of two integrated parts, namely the video tracking part that predicts the position of the target in the image plane, and the visual servo part that controls the movement of the camera following the movement of objects in the image plane. For tracking purposes, the camera is used as a visual sensor and applied to a 2-DOF (yaw-pitch) manipulator platform with an eye-in-hand camera configuration. Although its operation is relatively simple, the yaw-pitch camera platform still needs a good control method to improve its performance. In this study, we propose a moving object tracking system on a prototype yaw-pitch platform. A m-synthesis controller was used to control the movement of the visual servo part and keep the target in the center of the image plane. The experimental results showed relatively good results from the proposed system to work in real-time conditions with high tracking accuracy in both indoor and outdoor environments.
Paraphrasing Method Based on Contextual Synonym Substitution Ari Moesriami Barmawi; Ali Muhammad
Journal of ICT Research and Applications Vol. 13 No. 3 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.3.6

Abstract

Generating paraphrases is an important component of natural language processing and generation. There are sev­eral applications that use paraphrasing, for example linguistic steganography, recommender systems, machine translation, etc. One method for paraphrasing sentences is by using synonym substitution, such as the NGM-based paraphrasing method proposed by Gadag et al. The weakness of this method is that ambiguous meanings frequently occur because the paraphrasing process is based solely on n-gram. This negatively affects the naturalness of the paraphrased sentences. For overcoming this problem, a contextual synonym substitution method is proposed, which aims to increase the naturalness of the paraphrased sentences. Using the proposed method, the paraphrasing process is not only based on n-gram but also on the context of the sentence such that the naturalness is increased. Based on the experimental result, the sentences generated using the proposed method had higher naturalness than the sentences generated using the original method.
User Efficiency Model in Usability Engineering for User Interface Design Refinement of Mobile Application Gladina Desideria; Yoanes Bandung
Journal of ICT Research and Applications Vol. 14 No. 1 (2020)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2020.14.1.2

Abstract

Designers are often confronted with challenges or difficulties when designing user interfaces for mobile applications. The information must be clearly delivered to the user and also have an aesthetic appearance and good discoverability. One widely used method for conveying information is directing user attention to a component that is related to his or her task. We propose a recommender system by considering user efficiency in a user attention model. It can give suggestions for designers to improve the appearance of the most efficient component. This recommender system is aimed to help designers in the iteration process of usability engineering, especially to direct user attention to the most efficient component. This system analyzes actual user attention and then refines the user interface based on the energy of each component compared with the baseline energy. Our proposed model successfully increased the efficiency of a mobile learning application from 83.65% to 85.58% and improved discoverability of the most efficient component, thus reducing the task completion time.
Design and Implementation of Poka-Yoke System in Stationary Spot-Welding Production Line Utilizing Internet-of-Things Platform Santo Wijaya; Slamet Hariyadi; Fransisca Debora; Galih Supriadi
Journal of ICT Research and Applications Vol. 14 No. 1 (2020)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2020.14.1.3

Abstract

This paper presents the design and implementation of a poka-yoke system in a stationary spot welding (SSW) production line. The human-based SSW production process in a local automotive component company was considered in this study. Due to the repetitive and fast cycle-time of the production process, human errors are inevitable. Such errors lead to customer claims with the subject company. Based on the data of customer claims, there were three major quality issues (missing nuts, wrong-size nuts, asymmetrical spot weld marks). Due to the production line being manual, control of planned production and actual production was poor, leading to delivery issues (delayed delivery). Together these major issues contributed to 34.7% of customer claims on average from May to December 2018. The objective of this study was to solve the issues in the subject company through design and implementing a poka-yoke system utilizing the internet-of-things (IoT) platform to ensure data acquisition and information storage, and production progress monitoring and data analysis to meet user requirements. The combined approach of the poka-yoke system utilizing IoT in the SSW production line yielded satisfactory results with reduced customer claims to 5.3% for the stated problems from February to May 2019. Hence, the design objective was achieved.
Ultrasound Nerve Segmentation Using Deep Probabilistic Programming Iresha Rubasinghe; Dulani Meedeniya
Journal of ICT Research and Applications Vol. 13 No. 3 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.3.5

Abstract

Deep probabilistic programming concatenates the strengths of deep learning to the context of probabilistic modeling for efficient and flexible computation in practice. Being an evolving field, there exist only a few expressive programming languages for uncertainty management. This paper discusses an application for analysis of ultrasound nerve segmentation-based biomedical images. Our method uses the probabilistic programming language Edward with the U-Net model and generative adversarial networks under different optimizers. The segmentation process showed the least Dice loss ("‘0.54) and the highest accuracy (0.99) with the Adam optimizer in the U-Net model with the least time consumption compared to other optimizers. The smallest amount of generative network loss in the generative adversarial network model gained was 0.69 for the Adam optimizer. The Dice loss, accuracy, time consumption and output image quality in the results show the applicability of deep probabilistic programming in the long run. Thus, we further propose a neuroscience decision support system based on the proposed approach.
A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images Bilal Mohammed Bataineh; Mohd Khaled Yousef Shambour
Journal of ICT Research and Applications Vol. 13 No. 3 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.3.2

Abstract

The increasing use of smartphones and social media apps for communication results in a massive number of screenshot images. These images enrich the written language through text and emojis. In this regard, several studies in the image analysis field have considered text. However, they ignored the use of emojis. In this study, a robust two-stage algorithm for detecting emojis in screenshot images is proposed. The first stage localizes the regions of candidate emojis by using the proposed RGB-channel analysis method followed by a connected component method with a set of proposed rules. In the second verification stage, each of the emojis and non-emojis are classified by using proposed features with a decision tree classifier. Experiments were conducted to evaluate each stage independently and assess the performance of the proposed algorithm completely by using a self-collected dataset. The results showed that the proposed RGB-channel analysis method achieved better performance than the Niblack and Sauvola methods. Moreover, the proposed feature extraction method with decision tree classifier achieved more satisfactory performance than the LBP feature extraction method with all Bayesian network, perceptron neural network, and decision table rules. Overall, the proposed algorithm exhibited high efficiency in detecting emojis in screenshot images.
Enhanced Image Encryption Using Two Chaotic Maps Fatimah Abdulnabi Salman; Khitam Abdulnabi Salman
Journal of ICT Research and Applications Vol. 14 No. 2 (2020)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2020.14.2.3

Abstract

Information security is an important aspect in various communication areas, multimedia frameworks, medical imaging and militant communications. However, most of them encounter issues such as insufficient robustness or security. Recently, the approach of achieving information security by using chaotic techniques has gained popularity, since they provide ergodic and random generated keys. This paper introduces a combination of two chaotic maps (3D logistic map and Arnold's cat map) that meet the general security requirements of image transmission. First the image is encrypted using Arnold's cat map, which shuffles the image pixels. 3D logistic map is applied to the encrypted image for transformation and permutation purposes. Then the XOR operation for the encrypted image and a chaotic sequence key are used to provide more security after the pixel values have been changed. The performance of the proposed security method was evaluated using MATLAB by analyzing the correlation between adjacent pixels, histogram analysis, and entropy information. The simulation results showed that the proposed method is robust and resilient. It can achieve an average of 7.99 for entropy information, 99.6% for NPCR, and 33.77 % for UCAI.      
Efficient Utilization of Dependency Pattern and Sequential Covering for Aspect Extraction Rule Learning Fariska Zakhralativa Ruskanda; Dwi Hendratmo Widyantoro; Ayu Purwarianti
Journal of ICT Research and Applications Vol. 14 No. 1 (2020)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2020.14.1.4

Abstract

The use of dependency rules for aspect extraction tasks in aspect-based sentiment analysis is a promising approach. One problem with this approach is incomplete rules. This paper presents an aspect extraction rule learning method that combines dependency rules with the Sequential Covering algorithm. Sequential Covering is known for its characteristics in constructing rules that increase positive examples covered and decrease negative ones. This property is vital to make sure that the rule set used has high performance, but not inevitably high coverage, which is a characteristic of the aspect extraction task. To test the new method, four datasets were used from four product domains and three baselines: Double Propagation, Aspectator, and a previous work by the authors. The results show that the proposed approach performed better than the three baseline methods for the F-measure metric, with the highest F-measure value at 0.633.
A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS Agus Mulyanto; Wisnu Jatmiko; Petrus Mursanto; Purwono Prasetyawan; Rohmat Indra Borman
Journal of ICT Research and Applications Vol. 14 No. 3 (2021)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2021.14.3.6

Abstract

Intelligent transport systems (ITS) are a promising area of studies. One implementation of ITS are advanced driver assistance systems (ADAS), involving the problem of obstacle detection in traffic. This study evaluated the YOLOv4 model as a state-of-the-art CNN-based one-stage detector to recognize traffic obstacles. A new dataset is proposed containing traffic obstacles on Indonesian roads for ADAS to detect traffic obstacles that are unique to Indonesia, such as pedicabs, street vendors, and bus shelters, and are not included in existing datasets. This study established a traffic obstacle dataset containing eleven object classes: cars, buses, trucks, bicycles, motorcycles, pedestrians, pedicabs, trees, bus shelters, traffic signs, and street vendors, with 26,016 labeled instances in 7,789 images. A performance analysis of traffic obstacle detection on Indonesian roads using the dataset created in this study was conducted using the YOLOv4 method.
A Global Two-Stage Histogram Equalization Method for Gray-Level Images Khaled Almotairi
Journal of ICT Research and Applications Vol. 14 No. 2 (2020)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/10.5614/itbj.ict.res.appl.2020.14.2.1

Abstract

Digital image histogram equalization is an important technique in image processing to improve the quality of the visual appearance of images. However, the available methods suffer from several problems such as side effects and noise, brightness and contrast problems, loss of information and details, and failure in enhancement and in achieving the desired results. Therefore, the Adaptive Global Two-Stage Histogram Equalization (GTSHE) method for visual property enhancement of gray-level images is proposed. The first stage aims to clip the histogram and equalize the clipped histogram based on the number of occurrences of gray-level values. The second stage adaptively adjusts the space between occurrences by using a probability density function and different cumulative distribution functions that depend on the available and missing gray-level occurrences. Experiments were conducted using a number of benchmark datasets of images such as the Galaxies, Biomedical, Miscellaneous, Aerials, and Texture datasets. The results of the experiments were compared with a number of well-known methods, i.e. HE, AHEA, ESIHE, and MVSIHE, to evaluate the performance of the proposed method. The evaluation analysis showed that the proposed GTSHE method achieved a higher accuracy rate compared to the other methods.

Page 22 of 31 | Total Record : 302

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