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RISS 인기검색어
- 검색키워드
- 저자 : I Ketut Eddy Purnama (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Semi-Automatic Indonesian WordNet Establishment : From Synset Extraction to Visual Editor
Gunawan,I Ketut Eddy Purnama,Mochamad Hariadi 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.8
In this study, we have developed an Indonesian WordNet through four main phases: synonym set extraction (synset) as the smallest entity of lexical database from a natural language, semantic relation establishment between synsets (hypernym-hyponym and holonym-meronym), gloss extraction for synset collection, and the visual editor creation. The Semi-automatic term refers to the three initial phases which are automatically done using a number of machine learning approaches, while using visual editor to collaboratively complement the results collected from the previous phases. A number of raw data used on synset acquisition, semantic relations and glosses come from Kamus Besar Bahasa Indonesia (Great Dictionary of the Indonesian Language, abbreviated as KBBI) and Tesaurus Bahasa Indonesia (Indonesian Language Thesaurus), large collection of web pages from search engines, Wikipedia, and even Princeton WordNet for mapping purpose. This study shows that the proposed system successfully achieve 37,485 synsets, 24,256 hypernym-hyponym relations, 11,044 holonym-meronym relations and 6,520 gloss synsets. Similar approach is believed to accelerate lexical database development like WordNet for other languages.
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Pujiono,Eko Mulyanto Yuniarno,I Ketut Eddy Purnama,Mochamad Hariadi 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.11
Low quality of underwater image due to color spread and absorption deals with the propagation of light wave length. In this paper we propose exponential equation approach to enhance underwater image color and maintaining color constancy. Exponential approach is conducted through two steps: first, determining the relation between the color intensity of an image underwater and the color intensity of an image in a certain depth; second, determining the coefficient of underwater image color absorption by using least square. The result of exponential approach is measured by using Peak Signal to Noise Ratio, yielding an average value of 19.18 and visually the result of the image color approximates its original color. We concluded that exponential approach can determine the color constancy level which in turns can enhance the underwater image just as its original color.
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Basuki Rahmat,Endra Joelianto,I Ketut Eddy Purnama,Mauridhi Hery Purnomo 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.12
Vehicle License Plate Images Segmentation is a substantial stage for developing an Automatic License Plate Recognition (ALPR) system. In this paper, it is considered an efficient segmentation algorithm for extracting vehicle license plate images using Cellular Neural Networks (CNN). The learning CNN templates values are formulated as an optimization problem to achieve the desired performances which can be found by means of Adaptive Fuzzy (AF) algorithm and Neuro-Fuzzy (NF) algorithm techniques. The main objective of the paper is to compare the performances of standard CNN, Adaptive Fuzzy (AF), and Neuro-Fuzzy (NF) on real data of several vehicle license plate images of standard Indonesia License Plates. The results are then compared with ideal vehicle license plate images. Quantitative analysis between ideal vehicle license plate images and segmented vehicle license plate images is presented in terms of Peak signal-to-noise ratio (PSNR), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). From the performance analysis, the CNN template optimized by ANFIS algorithm is more recommended than the standard CNN edge detector or the CNN template optimized by Adaptive Fuzzy algorithm in vehicle license plate image segmentation. It is shown from the calculation that PSNR is 80% better than the standard CNN, and the resulted MSE and RMSE are 70% better than the standard CNN. Whereas the CNN template optimized by Adaptive Fuzzy algorithm achieves the PSNR 90% better than the standard CNN, but it yields the MSE and RMSE 40% worse than the standard CNN.
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The Development of Web-Based Framework of Vessel Information System using AIS Data
Christyowidiasmoro,Shidqon Famulaqih,Hanny B. Nugroho,Eko Pramunanto,I Ketut Eddy Purnama 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.10
In this paper we describe the development of a web-based framework of vessel information system using AIS data to monitor the movement of ship or vessel. The system consist of data acquisition module, data storage module, data management module, visualization and monitoring module. The data acquisition module functions to receive AIS data from all vessels within certain radius and stored in the database by storage module and data management module. Then, the visualization module will display interactively the movement of the vessels. The results show that marine traffic can be visualized by the system and information can be provided in a real time manner through web technology.
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