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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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Automated Defect Detection and Characterization on Pulse Thermography Images Using Computer Vision Techniques Meghana V; Megha P. Arakeri; Sharath D; M. Menaka; B. Venkatraman
Journal of ICT Research and Applications Vol. 13 No. 1 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.1.5

Abstract

Defect detection and characterization plays a vital role in predicting the life span of materials. Defect detection using appropriate inspection technologies at various phases has gained huge importance in metal production lines. It can be accomplished through wise application of non-destructive testing and evaluation (NDE). It is important to characterize defects at an early stage in order to be able to overcome them or take corrective measures. Pulse thermography is a modern NDE method that can be used for defect detection in metal objects. Only a limited amount of work has been done on automated detection and characterization of defects due to thermal diffusion. This paper proposes a system for automatic defect detection and characterization in metal objects using pulse thermography images as well as various image processing algorithms and mathematical tools. An experiment was carried out using a sequence of 250 pulse thermography images of an AISI 316 L stainless steel sheet with synthetic defects. The proposed system was able to detect and characterize defects sized 10 mm, 8 mm, 6 mm, 4 mm and 2 mm with an average accuracy of 96%, 95%, 84%, 77%, 54% respectively. The proposed technique helps in the effective and efficient characterization of defects in metal objects.
Real-Life Optimum Shift Scheduling Design Lee Kong Weng; Sze San Nah
Journal of ICT Research and Applications Vol. 13 No. 1 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.1.2

Abstract

In many industries, manpower shift scheduling poses problems that require immediate solutions. The fundamental need in this domain is to ensure that all shifts are assigned to cover all or as many jobs as possible. The shifts should additionally be planned with minimum manpower utilization, minimum manpower idleness and enhanced adaptability of employee schedules. The approach used in this study was to utilize an existing manpower prediction method to decide the minimum manpower required to complete all jobs. Based on the minimum manpower number and shift criteria, the shifts were assigned to form schedules using random pick and criteria-based selection methods. The potential schedules were then optimized and the best ones selected. Based on several realistic test instances, the proposed heuristic approach appears to offer promising solutions for shift scheduling as it improves shift idle time, complies with better shift starting time and significantly reduces the manpower needed and the time spent on creating schedules, regardless of data size.
A Hierarchical Emotion Classification Technique for Thai Reviews Jirawan Charoensuk; Ohm Sornil
Journal of ICT Research and Applications Vol. 12 No. 3 (2018)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2018.12.3.6

Abstract

Emotion classification is an interesting problem in affective computing that can be applied in various tasks, such as speech synthesis, image processing and text processing. With the increasing amount of textual data on the Internet, especially reviews of customers that express opinions and emotions about products. These reviews are important feedback for companies. Emotion classification aims to identify an emotion label for each review. This research investigated three approaches for emotion classification of opinions in the Thai language, written in unstructured format, free form or informal style. Different sets of features were studied in detail and analyzed. The experimental results showed that a hierarchical approach, where the subjectivity of the review is determined first, then the polarity of opinion is identified and finally the emotional label is calculated, yielded the highest performance, with precision, recall and F-measure at 0.691, 0.743 and 0.709, respectively.
Individual Expert Selection and Ranking of Scientific Articles Using Document Length Fadly Akbar Saputra; Taufik Djatna; Laksana Tri Handoko
Journal of ICT Research and Applications Vol. 13 No. 1 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.1.3

Abstract

Individual expert selection and ranking is a challenging research topic that has received a lot attention in recent years because of its importance related to referencing experts in particular domains and research fund allocation and management. In this work, scientific articles were used as the most common source for ranking expertise in particular domains. Previous studies only considered title and abstract content using language modeling. This study used the whole content of scientific documents obtained from Aminer citation data. The modified weighted language model (MWLM) is proposed that combines document length and number of citations as prior document probability to improve precision. Also, the author's dominance in a single document is computed using the Learning-to-Rank (L2R) method. The evaluation results using p@n, MAP, MRR, r-prec, and bpref showed a precision enhancement. MWLM improved the weighted language model (WLM) by p@n (4%), MAP (22.5%), and bpref (1.7%). MWLM also improved the precision of a model that used author dominance by MAP (4.3%), r-prec (8.2%), and bpref (2.1%).
Identification of Image Edge Using Quantum Canny Edge Detection Algorithm Dini Sundani; Sigit Widiyanto; Yuli Karyanti; Dini Tri Wardani
Journal of ICT Research and Applications Vol. 13 No. 2 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.2.4

Abstract

Identification of image edges using edge detection is done to obtain images that are sharp and clear. The selection of the edge detection algorithm will affect the result. Canny operators have an advantage compared to other edge detection operators because of their ability to detect not only strong edges but also weak edges. Until now, Canny edge detection has been done using classical computing where data are expressed in bits, 0 or 1. This paper proposes the identification of image edges using a quantum Canny edge detection algorithm, where data are expressed in the form of quantum bits (qubits). Besides 0 or 1, a value can also be 0 and 1 simultaneously so there will be many more possible values that can be obtained. There are three stages in the proposed method, namely the input image stage, the preprocessing stage, and the quantum edge detection stage. Visually, the results show that quantum Canny edge detection can detect more edges compared to classic Canny edge detection, with an average increase of 4.05%.
Trust-based Selfish Node Detection Mechanism using Beta Distribution in Wireless Sensor Network Kanchana Devi V; Ganesan R
Journal of ICT Research and Applications Vol. 13 No. 1 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.1.6

Abstract

Wireless sensor networks (WSNs) are placed in open environments for the collection of data and are vulnerable to external and internal attacks. The cryptographic mechanisms implemented so far, such as authorization and authentication, are used to restrict external sensor node attacks but cannot prevent internal node attacks. In order to evade internal attacks trust mechanisms are used. In trust mechanisms, firstly, the sensor nodes are monitored using the popular Watchdog mechanism. However, traditional trust models do not pay much attention to selective forwarding and consecutive packet dropping. Sometimes, sensitive data are dropped by internal attackers. This problem is addressed in our proposed model by detecting selective forwarding and consecutive failure of sending packets using the Beta probability density function model.
An Energy Constraint Approach to Improve Lifetime and Reduce Routing Overhead in Heterogeneous MANET . Poonam; Hirdesh Kumar; Santar Pal Singh
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.2019.13.3.4

Abstract

Heterogeneous Mobile Ad-hoc Networks (H-MANET) contain different configuration nodes, each of which communicates over a wireless channel and is capable of movement. Heterogeneous wireless networking has attracted lot of interest from consumers in the previous few years for its applications in mobile and personal communications. One of the main constraints in MANET is the high probability of failure due to energy-exhausted nodes. So if the path selected for communication has low battery life then the path breaks prematurely and the re-discovery phase starts, which costs more overhead in the network. Therefore, there is unequal consumption of node energy, which must be prevented. The energy expenditure of the nodes should be balanced in order to minimize path breakage. This can be done by finding the communication path that is the most energy-efficient among alternative disjoint paths. This approach reduces path breakage and routing overhead caused by nodes with low battery life dying in the communication path, thus increasing the network's lifetime.
Two-stage S-Band LNA Development Using Non-Simultaneous Conjugate Match Technique Achmad Munir; Yana Taryana; Mochamad Yunus; Hardi Nusantara; Mohammad Ridwan Effendi
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.2019.13.3.3

Abstract

This paper presents the development of a two-stage low noise amplifier (LNA) operating at the S-band frequency that is implemented using the non-simultaneous conjugate match (NSCM) technique. The motivation of this work was to solve the issue of the gain of LNAs designed using the most commonly used technique, i.e. simultaneous conjugate match (SCM), which often produce an increase of other parameter values, i.e. noise figure and voltage standing wave ratio (VSWR). Prior to hardware implementation, the circuit simulation software Advanced Design System (ADS) was applied to design the two-stage S-band LNA and to determine the desired trade-off between its parameters. The proposed two-stage S-band LNA was deployed on an Arlon DiClad527 using a bipolar junction transistor (BJT), type BFP420. Meanwhile, to achieve impedances that match the two-stage S-band LNA circuit, microstrip lines were employed at the input port, the interstage, and the output port. Experimental characterization showed that the realized two-stage S-band LNA produced a gain of 22.77 dB and a noise figure of 3.58 dB at a frequency of 3 GHz. These results were 6.1 dB lower than the simulated gain and 0.76 dB higher than the simulated noise figure respectively.
Tunnel Settlement Prediction by Transfer Learning Qicai Zhou; Hehong Shen; Jiong Zhao; Xiaolei Xiong
Journal of ICT Research and Applications Vol. 13 No. 2 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.2.3

Abstract

Tunnel settlement has a significant impact on property security and personal safety. Accurate tunnel-settlement predictions can quickly reveal problems that may be addressed to prevent accidents. However, each acquisition point in the tunnel is only monitored once daily for around two months. This paper presents a new method for predicting tunnel settlement via transfer learning. First, a source model is constructed and trained by deep learning, then parameter transfer is used to transfer the knowledge gained from the source model to the target model, which has a small dataset. Based on this, the training complexity and training time of the target model can be reduced. The proposed method was tested to predict tunnel settlement in the tunnel of Shanghai metro line 13 at Jinshajiang Road and proven to be effective. Artificial neural network and support vector machines were also tested for comparison. The results showed that the transfer-learning method provided the most accurate tunnel-settlement prediction.
A Novel Watermarking Method using Hadamard Matrix Quantization Prajanto Wahyu Adi; Pramudi Arsiwi
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.2020.14.1.1

Abstract

One of the most used watermarking algorithms is Singular Value Decomposition (SVD), which has a balanced level of imperceptibility and robustness. However, SVD uses a singular matrix for embedding and two orthogonal matrices for reconstruction, which is inefficient. In this paper, a Hadamard matrix is used to get a singular matrix for the reconstruction process. Moreover, SVD works with a floating-point value, which takes long processing time, while the Hadamard matrix works with an integer range, which is more efficient. Visual measurement showed that SVD and the new method had average NC values of 0.8321 and 0.8293, whereas the average SSIM values resulted in the same value (0.9925). In terms of processing time, the proposed method ran faster than SVD with an embedding and extraction time of 0.6308 and 0.2163 seconds against 0.8419 and 0.2935 seconds. The proposed method successfully reduced the running time while maintaining imperceptibility and robustness.

Page 21 of 31 | Total Record : 302

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