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All Journal International Journal of Electrical and Computer Engineering SinarFe7 Electron: Jurnal Ilmiah Teknik Elektro JEECS (Journal of Electrical Engineering and Computer Sciences)
Adiananda Adiananda
Universitas Bhayangkara Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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Journal : JEECS (Journal of Electrical Engineering and Computer Sciences)

 1 
Image Based Object Tracking Target on Ship Robot for Oil Waste Cleaner Richa Watiasih; Ahmadi; Adiananda
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 7 No. 2 (2022): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1111.069 KB) | DOI: 10.54732/jeecs.v7i2.17

Abstract

The existing oil waste water contains oil, solids, water and heavy metals. This oil waste is a contaminant material that can cause negative impacts to the aquatic environment as well as the existing living creatures around it so that it requires a careful and fast handling to clean. The research resulted in the tracking system on the image-based-ship robot by using the method of histogram and fuzzy logic controller that can detect the image of waste water well. The result of the testing of the ship robot done on the pool indicated that it took about ± 196.92 seconds for the robot to detect the image of oil waste objects. The oil waste suctioning process took a maximum of 60 seconds for once.
Pattern Recognition of Signature Verification Using Cellular Automata Methods Adiananda; Retantyo Wardoyo
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 1 No. 2 (2016): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (501.932 KB) | DOI: 10.54732/jeecs.v1i2.170

Abstract

A pattern recognition technique in the field of machine learning and can be defined as "the act of taking raw data and act upon data classification". Much research has been done on the topic of pattern recognition using a variety of methods one of which is by using the cellular automata. In this study used cellular automata method for finding and extracting characteristics of an image of the signature and realized in a pattern recognition software that can verify the authenticity of the signature image by using cellular automata method for the extraction process characteristics. In this study used data 57 respondents with 6 signatures used as a reference image. Three pieces of the original signature image and 10 pieces of counterfeit signature image is used as the test images (query). From the testing that has been done precision 88.30%, recall 65.37% and accuracy 71.31%.
Co-Authors Agus Kiswantono Ahmadi Amirullah Amirullah Dani Ardiyansyah Mukhammad Fatoni Retantyo Wardoyo Richa Watiasih Richa Watiasih