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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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Overlapping Cervical Nuclei Separation using Watershed Transformation and Elliptical Approach in Pap Smear Images Izzati Muhimmah; Rahadian Kurniawan; Indrayanti Indrayanti
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.2017.11.3.1

Abstract

In this study, a robust method is proposed for accurately separating overlapping cell nuclei in cervical microscopic images. This method is based on watershed transformation and an elliptical approach. Since the watershed transformation process of taking the initial seed is done manually, the method was developed to obtain the initial seed automatically. Total initial seeds at this stage represents the number of nuclei that exist in the image of a pap smear, either overlapping or not. Furthermore, a method was developed based on an elliptical approach to obtain the area of each of the nuclei automatically. This method can successfully separate several (more than two) clustered cell nuclei. In addition, the proposed method was evaluated by experts and was proven to have better results than methods from previous studies in terms of accuracy and execution time. The proposed method can determine overlapping and non-overlapping boundaries of nuclei fast and accurately. The proposed method provides better decision-making on areas with overlapping nuclei and can help to improve the accuracy of image analysis and avoid information loss during the process of image segmentation.
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.
Social Media Text Classification by Enhancing Well-Formed Text Trained Model Phat Jotikabukkana; Virach Sornlertlamvanich; Okumura Manabu; Choochart Haruechaiyasak
Journal of ICT Research and Applications Vol. 10 No. 2 (2016)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2016.10.2.6

Abstract

Social media are a powerful communication tool in our era of digital information. The large amount of user-generated data is a useful novel source of data, even though it is not easy to extract the treasures from this vast and noisy trove. Since classification is an important part of text mining, many techniques have been proposed to classify this kind of information. We developed an effective technique of social media text classification by semi-supervised learning utilizing an online news source consisting of well-formed text. The computer first automatically extracts news categories, well-categorized by publishers, as classes for topic classification. A bag of words taken from news articles provides the initial keywords related to their category in the form of word vectors. The principal task is to retrieve a set of new productive keywords. Term Frequency-Inverse Document Frequency weighting (TF-IDF) and Word Article Matrix (WAM) are used as main methods. A modification of WAM is recomputed until it becomes the most effective model for social media text classification. The key success factor was enhancing our model with effective keywords from social media. A promising result of 99.50% accuracy was achieved, with more than 98.5% of Precision, Recall, and F-measure after updating the model three times.
Mining High Utility Itemsets with Regular Occurrence Komate Amphawan; Philippe Lenca; Anuchit Jitpattanakul; Athasit Surarerks
Journal of ICT Research and Applications Vol. 10 No. 2 (2016)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2016.10.2.5

Abstract

High utility itemset mining (HUIM) plays an important role in the data mining community and in a wide range of applications. For example, in retail business it is used for finding sets of sold products that give high profit, low cost, etc. These itemsets can help improve marketing strategies, make promotions/ advertisements, etc. However, since HUIM only considers utility values of items/itemsets, it may not be sufficient to observe product-buying behavior of customers such as information related to "regular purchases of sets of products having a high profit margin". To address this issue, the occurrence behavior of itemsets (in the term of regularity) simultaneously with their utility values was investigated. Then, the problem of mining high utility itemsets with regular occurrence (MHUIR) to find sets of co-occurrence items with high utility values and regular occurrence in a database was considered. An efficient single-pass algorithm, called MHUIRA, was introduced. A new modified utility-list structure, called NUL, was designed to efficiently maintain utility values and occurrence information and to increase the efficiency of computing the utility of itemsets. Experimental studies on real and synthetic datasets and complexity analyses are provided to show the efficiency of MHUIRA combined with NUL in terms of time and space usage for mining interesting itemsets based on regularity and utility constraints.
Research Methodology for Analysis of E-Commerce User Activity Based on User Interest using Web Usage Mining Saucha Diwandari; Adhistya Erna Permanasari; Indriana Hidayah
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.2018.12.1.4

Abstract

Visitor interaction with e-commerce websites generates large amounts of clickstream data stored in web access logs. From a business standpoint, clickstream data can be used as a means of finding information on user interest. In this paper, the authors propose a method to find user interest in products offered on e-commerce websites based on web usage mining of clickstream data. In this study, user interest was investigated using the PIE approach coupled with clustering and classification techniques. The experimental results showed that the method is able to assist in analyzing visitor behavior and user interest in e-commerce products by identifying those products that prompt visitor interest.
An Application of PSV-S in Fast Development of a Real-Time DSP System Armein Z.R. Langi
Journal of ICT Research and Applications Vol. 10 No. 2 (2016)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2016.10.2.2

Abstract

Virtual prototyping is natural in developing digital signal processing (DSP) systems using a product-service-value system (PSV-S) approach. Our DSP virtual prototyping approach consists of four development phases: (1) a generic DSP system, (2) a functional DSP system, (3) an architectural DSP system, and (4) a real-time DSP system. Such an approach results in a more comprehensive approach in the DSP system development. This paper shows an example of prototyping a voice codec on a single-chip DSP processor.
Electrical Capacitance Volume Tomography Static Imaging by Non-Optimized Compressive Sensing Framework Nur Afny Catur Andryani; Dodi Sudiana; Dadang Gunawan
Journal of ICT Research and Applications Vol. 10 No. 3 (2016)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2016.10.3.4

Abstract

Electrical capacitance volume tomography is a volumetric tomography technique that utilizes capacitance and fringing to capture behavior or perturbation in the sensing domain. One of the crucial issues in developing ECVT technology is the reconstruction algorithm. In practice, ILBP is most used due to its simplicity. However, it still presents elongation errors for certain dielectric contrasts. The high undersampling measurement of the ECVT imaging system, which is mathematically defined as an undetermined linear system, is one of the most challenging issues. Compressive sensing (CS) is a framework that enables the recovery of a sparse signal or a signal that can be represented as sparse in a certain domain, by having a lower dimension of measurement data compared to the Shanon-Nyquist theorem. Thus, mathematically, this framework is promising for solving an undetermined linear system such as the ECVT imaging system. This paper discusses the possibility of developing an ECVT imaging technique for static objects based on a CS framework. Based on the simulation results, Non-optimized CS does not completely succeed in providing better ECVT imaging quality. However, it does provide more localized imaging compared to ILBP. In addition, by having fewer requirements for the measurement data dimension, the CS framework is promising for reducing the number of required electrodes.
A Chemical Reaction Optimization Approach to Prioritize the Regression Test Cases of Object-Oriented Programs Sudhir Kumar Mohapatra; Srinivas Prasad
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.2017.11.2.1

Abstract

Regression test case prioritization is used to improve certain performance goals. Limited resources force to choose an effective prioritization technique, which makes an ordering of the test cases so that the most suitable test case will be executed first. Executing regression test cases for a fixed time is all about time aware test case prioritization. Regression test case prioritization using chemical reaction optimization (CRO) for object-oriented programs is proposed in this paper. The effectiveness of the test case ordering was measured using average percentage of faults detected (APFD). Experiments on three object-oriented subject programs involving three different techniques were performed to judge the proposed approach. The empirical results indicate that the algorithm implemented using CRO gives a higher APFD value than the other two techniques.
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.

Page 15 of 31 | Total Record : 302

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