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All Journal ComTech: Computer, Mathematics and Engineering Applications Engineering, Mathematics and Computer Science Journal (EMACS) International Journal of Computer Science and Humanitarian AI
Edy Irwansyah
Bina Nusantara University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : engineering mathematics and computer science journal emacs

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Semantic Segmentation for Aerial Images: A Literature Review Yongki Christian Sanjaya; Alexander Agung Santoso Gunawan; Edy Irwansyah
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 2 No. 3 (2020): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v2i3.6737

Abstract

Semantic image segmentation is one of the fundamental applications of computer vision which can also be called pixel-level classification. Semantic image segmentation is the process of understanding the role of each pixel in an image. Over time, the model for completing Semantic Image Segmentation has developed very rapidly. Due to this rapid growth, many models related to Semantic Image Segmentation have been produced and have also been used or applied in many domains such as medical areas and intelligent transportation. Therefore, our motivation in making this paper is to contribute to the world of research by conducting a review of Semantic Image Segmentation which aims to provide a big picture related to the latest developments related to Semantic Image Segmentation. In addition, we also provide the results of performance measurements on each of the Semantic Image Segmentation methods that we discussed using the Intersectionover-Union (IoU) method. After that, we provide a comparison for each semantic image segmentation model that we discuss using the results of the IoU and then provide conclusions related to a model that has good performance. We hope this review paper can facilitate researchers in understanding the development of Semantic Image Segmentation in a shorter time, simplify understanding of the latest advancements in Semantic Image Segmentation, and can also be used as a reference for developing new Semantic Image Segmentation models in the future
Comparing SVM and Naïve Bayes Classifier for Fake News Detection Nurhasanah Nurhasanah; Daniel Emerald Sumarly; Jason Pratama; Ibrahim Tan Kah Heng; Edy Irwansyah
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 4 No. 3 (2022): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v4i3.8670

Abstract

Fake news has been evolving into a problem that is getting even more challenging. Technology has been misused to spread false information about many things, such as war, pandemics, and the stock market. Unfortunately, this issue is not a big deal for some people without conscious consumption of that news. Hence, being part takes a role in combating the spread of false information using the advancement of technology. This study proposed two methods of machine learning model, Support Vector Machine (SVM) and Naïve Bayes, to classify fake news. Furthermore, to assert the applicability of models by examining news articles dataset which contain two labels, reliable and unreliable news. The higher accuracy is 0.96 using the SVM model
Co-Authors Alexander Agung Santoso Gunawan Andiyono Andiyono Daniel Emerald Sumarly Djunaidy Santoso Ibrahim Tan Kah Heng Iqbal S. Ivan William Lianata Jason Pratama Kang Nicholas Darren Nugroho M. Ikhsan Neilson Christopher Nurhasanah Nurhasanah R. I Made Oka Yoga Rokhana Dwi Bekti Yongki Christian Sanjaya Yosua Nathanael Zulfany Erlisa Rasjid