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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
 1 
Search results for , issue "Vol. 11 No. 3 (2017)" : 7 Documents clear
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.
Document Grouping by Using Meronyms and Type-2 Fuzzy Association Rule Mining Fahrur Rozi; Farid Sukmana
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.2017.11.3.4

Abstract

The growth of the number of textual documents in the digital world, especially on the World Wide Web, is incredibly fast. This causes an accumulation of information, so we need efficient organization to manage textual documents. One way to accurately classify documents is using fuzzy association rules. The quality of the document clustering is affected by phase extraction of key terms and type of fuzzy logic system (FLS) used for clustering. The use of meronyms in the extraction of key terms to obtain cluster labels helps obtaining meaningful cluster labels and in addition ambiguities and uncertainties that occur in the rules of type-1 fuzzy logic systems can be overcome by using type-2 fuzzy sets. This study proposes a method of key term extraction based on meronyms with an initialization cluster using fuzzy association rule mining for document clustering. This method consists of four stages, i.e. preprocessing of the document, extraction of key terms with meronyms, extraction of candidate clusters, and cluster tree construction. Testing of this method was done with three different datasets: classic, Reuters, and 20 Newsgroup. Testing was done by comparing the overall F-measure of the method without meronyms and with meronyms. Based on the testing, the method with meronyms in the extraction of keywords produced an overall F-measure of 0.5753 for the classic dataset, 0.3984 for the Reuters dataset, and 0.6285 for the 20 Newsgroup dataset.
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.
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.
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.
Deep Convolutional Level Set Method for Image Segmentation Agustinus Kristiadi; Pranowo Pranowo
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.2017.11.3.5

Abstract

Level Set Method is a popular method for image segmentation. One of the problems in Level Set Method is finding the right initial surface parameter, which implicitly affects the curve evolution and ultimately the segmentation result. By setting the initial curve too far away from the target object, Level Set Method could potentially miss the target altogether, whereas by setting the initial curve as general as possible "“ i.e. capturing the whole image "“ makes Level Set Method susceptible to noise. Recently, deep-learning methods, especially Convolutional Neural Network (CNN), have been proven to achieve state-of-the-art performance in many computer vision tasks such as image classification and detection. In this paper, a new method is proposed, called Deep Convolutional Level Set Method (DCLSM). The idea is to use the CNN object detector as a prior for Level Set Method segmentation. Using DCLSM it is possible to significantly improve the segmentation accuracy and precision of the classic Level Set Method. It was also found that the prior used in the proposed method is the lower and upper bound for DCLSM's precision and recall, respectively.
Cover ICT Vol. 11 No. 3, 2017 Journal of ICT Research and Applications
Journal of ICT Research and Applications Vol. 11 No. 3 (2017)
Publisher : LPPM ITB

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

Abstract

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