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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics Publication Library and Information Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Wina Permana Sari
Bina Nusantara Institute of Creative Technology Malang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Library & Information Science

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Journal : kinetik game technology information system computer network computing electronics and control

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2D Mapping and boundary detection using 2D LIDAR sensor for prototyping Autonomous PETIS (Programable Vehicle with Integrated Sensor) Hanugra Aulia Sidharta; Sidharta Sidharta; Wina Permana Sari
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 2, May 2019
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (326.052 KB) | DOI: 10.22219/kinetik.v4i2.731

Abstract

PETIS (Programable Vehicle with Integrated Sensor) is a research project with goal make a robot that move independently with specific purpose. Due complexity of PETIS, research divide into several important sequence. In this research author focus on sense of sight for PETIS, LIDAR chosen due flexible and comprehensive. There is many LIDAR sensor in marketplace, LDS-01 as one of commercial LIDAR sensor available on market, produced by ROBOTIS as one of low-cost LIDAR sensor. Compare with another sensor that cost more than $1000, LDS-01 just cost lower than $500. On this research study focus with LDS-01 sensor reading, include hardware, software connection, and data handling. Based on this research LDS-01 as LIDAR sensor can read obstacle with minimum 29,9 cm and maximal 290,7 cm. Comparing with datasheet LDS-01 should work from 12 cm through 350 cm. 
The Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning Wina Permana Sari; Hisyam Fahmi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vo. 6, No. 3, August 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i3.1272

Abstract

Digital image modification or image forgery is easy to do today. The authenticity verification of an image become important to protect the image integrity so that the image is not being misused. Error Level Analysis (ELA) can be used to detect the modification in image by lowering the quality of image and comparing the error level. The use of deep learning approach is a state-of-the-art in solving cases of image data classification. This study wants to know the effect of adding ELA extraction process in the image forgery detection using deep learning approach. The Convolutional Neural Network (CNN), which is a deep learning method, is used as a method to do the image forgery detection. The impacts of applying different ELA compression levels, such as 10, 50, and 90 percent, were also compared in this study. According to the results, adopting the ELA feature increases validation accuracy by about 2.7% and give the better test accuracy. However, the use of ELA will slow down the processing time by about 5.6%.
Co-Authors Ari Kusumastuti Dana I. Sensuse Elin Cahyaningsih Erzi Hidayat Hanugra Aulia Sidharta Hisyam Fahmi Ika Arthalia Wulandari Sidharta Sidharta Sukmasetya, Pristi