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Yuliah Qotimah
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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 7 Documents
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Search results for , issue "Vol. 11 No. 1 (2017)" : 7 Documents clear
Adjusting Time of Flight in Ultrasound B-mode Imaging for Accurate Measurement of Fat using Image Segmentation Technique Norlida Buniyamin; M. Hazwan Abdul Halim
Journal of ICT Research and Applications Vol. 11 No. 1 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.1.1

Abstract

This research attempted to measure chicken intramuscular fat content using improved ultrasound B-mode images and image segmentation. Adapted B-mode imaging is proposed to increase the positioning accuracy of B-mode images with the objective to correct the phase error due to the use of predetermined ultrasonic velocity in conventional B-mode imaging. The pre-determined velocity is replaced by actual velocity measured using A-mode imaging. The positioning accuracy of adapted and conventional B-mode imaging was validated using 144 chicken samples. The adapted B-mode image had better positioning accuracy compared to a conventional B-mode image since the method used was able to detect the thickness of the chicken sample with a lower mean difference (0.036±0.034mm vs. 0.113±0.010). Both methods were then applied for measurement of intramuscular fat content. The histogram mean and the percentage of fat pixels were the B-mode image characteristics that were extracted and their correlation with the fat content, measured using the Soxhlet method, was analyzed. The properties of the adapted B-mode images correlated better with the Soxhlet-measured fat content compared to the properties of the conventional B-mode images as reflected in the correlation coefficient, r, for the histogram mean (0.357 vs. 0.129) and the percentage of fat pixels (0.406 vs. 0.289). The results indicate the potential of using ultrasound adapted B-mode imaging to measure chicken intramuscular fat.
Parallel Technique for Medicinal Plant Identification System using Fuzzy Local Binary Pattern Ngakan Nyoman Kutha Krisnawijaya; Yeni Herdiyeni; Bib Paruhum Silalahi
Journal of ICT Research and Applications Vol. 11 No. 1 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.1.5

Abstract

As biological image databases are growing rapidly, automated species identification based on digital data becomes of great interest for accelerating biodiversity assessment, research and monitoring. This research applied high performance computing (HPC) to a medicinal plant identification system. A parallel technique for medicinal plant image processing using Fuzzy Local Binary Pattern (FLBP) is proposed. The FLBP method extends the Local Binary Pattern (LBP) approach by employing fuzzy logic to represent texture images. The main goal of this research was to measure the efficiency of using the proposed parallel technique for medicinal plant image processing and evaluation in order to find out whether this approach is reasonable for handling large data sets. The parallel processing technique was designed in a message-sending model. 30 species of Indonesian medical plants were analyzed. Each species was represented by 48 leaf images. Performance evaluation was measured using the speed-up, efficiency, and isoefficiency of the parallel computing technique. Preliminary results show that HPC worked well in reducing the execution time of medical plant identification. In this work, parallel processing of training images was 7.64 times faster than with sequential processing, with efficiency values greater than 0.9. Parallel processing of testing images was 6.73 times faster than with sequential processing, with efficiency values over 0.9. The system was able to identify images with an accuracy of 68.89%.
An Energy Aware Unequal Clustering Algorithm using Fuzzy Logic for Wireless Sensor Networks Dibya Ranjan Das Adhikary; Dheeresh Kumar Mallick
Journal of ICT Research and Applications Vol. 11 No. 1 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.1.4

Abstract

In wireless sensor networks, clustering provides an effective way of organising the sensor nodes to achieve load balancing and increasing the lifetime of the network. Unequal clustering is an extension of common clustering that exhibits even better load balancing. Most existing approaches do not consider node density when clustering, which can pose significant problems. In this paper, a fuzzy-logic based cluster head selection approach is proposed, which considers the residual energy, centrality and density of the nodes. In addition, a fuzzy-logic based clustering range assignment approach is used, which considers the suitability and the position of the nodes in assigning the clustering range. Furthermore, a weight function is used to optimize the selection of the relay nodes. The proposed approach was compared with a number of well known approaches by simulation. The results showed that the proposed approach performs better than the other algorithms in terms of lifetime and other metrics.
Rainfall Prediction in Tengger, Indonesia Using Hybrid Tsukamoto FIS and Genetic Algorithm Method Ida Wahyuni; Wayan Firdaus Mahmudy
Journal of ICT Research and Applications Vol. 11 No. 1 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.1.3

Abstract

Countries with a tropical climate, such as Indonesia, are highly dependent on rainfall prediction for many sectors, such as agriculture, aviation, and shipping. Rainfall has now become increasingly unpredictable due to climate change and this phenomenon also affects Indonesia. Therefore, a robust approach is required for more accurate rainfall prediction. The Tsukamoto Fuzzy Inference System (FIS) is one of the algorithms that can be used for prediction problems, but if its membership functions are not specified properly, the prediction error is still high. To improve the results, the boundaries of the membership functions can be adjusted automatically by using a genetic algorithm. The proposed genetic algorithm employs two selection processes. The first one uses the Roulette wheel method to select parents, while the second one uses the elitism method to select chromosomes for the next generation. Based on this approach, a rainfall prediction experiment was conducted for Tengger, Indonesia using historical rainfall data for ten-year periods. The proposed method generated root mean square errors (RMSE) of 6.78 and 6.63 for the areas of Tosari and Tutur respectively. These results are better compared with the results using Tsukamoto FIS and the Generalized Space Time Autoregressive (GSTAR) model from previous studies.
Improvement of Fuzzy Geographically Weighted Clustering-Ant Colony Optimization Performance using Context-Based Clustering and CUDA Parallel Programming Nila Nurmala; Ayu Purwarianti
Journal of ICT Research and Applications Vol. 11 No. 1 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.1.2

Abstract

Geo-demographic analysis (GDA) is the study of population characteristics by geographical area. Fuzzy Geographically Weighted Clustering (FGWC) is an effective algorithm used in GDA. Improvement of FGWC has been done by integrating a metaheuristic algorithm, Ant Colony Optimization (ACO), as a global optimization tool to increase the clustering accuracy in the initial stage of the FGWC algorithm. However, using ACO in FGWC increases the time to run the algorithm compared to the standard FGWC algorithm. In this paper, context-based clustering and CUDA parallel programming are proposed to improve the performance of the improved algorithm (FGWC-ACO). Context-based clustering is a method that focuses on the grouping of data based on certain conditions, while CUDA parallel programming is a method that uses the graphical processing unit (GPU) as a parallel processing tool. The Indonesian Population Census 2010 was used as the experimental dataset. It was shown that the proposed methods were able to improve the performance of FGWC-ACO without reducing the clustering quality of the original method. The clustering quality was evaluated using the clustering validity index.
A Comprehensive Performance Analysis of IEEE 802.11p based MAC for Vehicular Communications Under Non-saturated Conditions Akram A. Abdullah Almohammedi; Nor K. Noordin; Aduwati Sali; Fazirulhisyam Hashim; Sabri Saeed
Journal of ICT Research and Applications Vol. 11 No. 1 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.1.6

Abstract

Reliable and efficient data broadcasting is essential in vehicular networks to provide safety-critical and commercial service messages on the road. There is still no comprehensive analysis of IEEE 802.11p based MAC that portrays the presence of buffer memory in vehicular networks. Besides, most of the analytical works do not fulfill some of the IEEE 802.11p specifications, such as short retry limit and back-off timer freezing. This paper proposes a 1-D and 2-D Markov model to analyze mathematically IEEE 802.11p based MAC for safety and non-safety messages respectively. The work presented in this paper takes into account the traffic arrival along with the first-order buffer memory and freezing of the back-off timer as well, to utilize the channel efficiently and provide higher accuracy in estimation of channel access, yielding more precise results of the system throughput for non-safety messages and lower delay for safety messages. Furthermore, back-off stages with a short retry limit were applied for non-safety messages in order to meet the IEEE 802.11p specifications, guaranteeing that no packet is served indefinitely, avoiding the overestimation of system throughput. A simulation was carried out to validate the analytical results of our model.
Cover JICTRA Vol. 11 No. 1, 2017 Journal of ICT Research and Applications
Journal of ICT Research and Applications Vol. 11 No. 1 (2017)
Publisher : LPPM ITB

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

Abstract

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