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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Proceeding of the Electrical Engineering Computer Science and Informatics
Iping Supriana Suwardi
Institut Teknologi Bandung
Computer Science & IT Electrical & Electronics Engineering

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K Means Clustering and Meanshift Analysis for Grouping the Data of Coal Term in Puslitbang tekMIRA Rolly Maulana Awangga; Syafrial Fachri Pane; Khaera Tunnisa; Iping Supriana Suwardi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.8910

Abstract

Indonesian government agencies under the Ministry of Energy and Mineral Resources have problems in classifying data dictionary of coal. This research conduct grouping coal dictionary using K-Means and MeanShift algorithm. K-means algorithm is used to get cluster value on character and word criteria. The last iteration of Euclidian distance calculation data on k-means combine with Meanshift algorithm. The meanshift calculates centroid by selecting different bandwidths. The result of grouping using k-means and meanshift algorithm shows different centroid to find optimum bandwidth value. The data dictionary of this research has sorted in alphabetically.
Optimization of Salient Object Segmentation by using the influence of color in Digital Image Edvin Ramadhan; Iping Supriana Suwardi; Bambang Riyanto Trilaksono
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 2: EECSI 2015
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1273.409 KB) | DOI: 10.11591/eecsi.v2.775

Abstract

Human attention is more likely to be interested indifferent objects or striking in image processing called salientobject. Existing approaches worked well in finding the salientobject in this image, but they have not been able to accuratelydetect where objects should stand out due to the influence of lightintensity, there are various object results of salient object detectionin which area is still cut off or do not appear because they do notinclude salient area. We offer solutions to fix these problems byoptimizing salient object detection prioritizing object area aftersalient area, through checking comparison of the color regionlocated around the area of the salient. This Optimization of theapplication is able to improve to 83% from 100 salient object whichhas this problem, and able to produce more natural Saliency Cut.
Co-Authors Bambang Riyanto Trilaksono Cahyawijaya, Samuel Edvin Ramadhan Edvin Ramadhan Khaera Tunnisa, Khaera Ramadhan, Edvin Rolly Maulana Awangga, Rolly Maulana Syafrial Fachri Pane