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All Journal International Journal of Advances in Intelligent Informatics Science and Technology Indonesia JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Infomedia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Serambi Engineering JURSIMA (Jurnal Sistem Informasi dan Manajemen) Indonesian Journal of Electrical Engineering and Computer Science jeti Jurnal Teknologi JURSIMA Journal Of Artificial Intelligence And Software Engineering Bulletin of Engineering Science, Technology and Industry
Arhami, Muhammad
Politeknik Negeri Lhokseumawe
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences

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USING THE YOU ONLY LOOK ONCE (YOLO) METHOD FOR DETECTING PHYSICAL DISTANCING AND MASKED FACES Khairul Azman; Muhammad Arhami; Azhar; Zhenyu Cui
Bulletin of Engineering Science, Technology and Industry Vol. 1 No. 3 (2023): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v1i3.17

Abstract

The flow of news regarding the development of Covid-19 has dominated various information channels in Indonesia in the last 2 years, either through print media or digital media. Various types of news related to Covid-19 continue to circulate, including hoax news. One of the hoax news that is widely circulating is news about the Covid-19 vaccine. The rise of information containing hoax news and untrue rumors about the Covid-19 vaccine in society can worsen the pandemic situation. Currently there is no intelligent system capable of classifying hoaxes regarding the Covid-19 vaccine. To maximize prevention of the spread of hoax news about the Covid-19 vaccine and overcome the problems faced, the author designed a classification system for hoax news about the Covid-19 vaccine using a machine learning approach. The system built can classify news using a combination of the Name Entity Recognition (NER) and Backpropagation algorithms. The datasets used are: 600 Covid-19 vaccine news data obtained from the sites https://turnbackhoax.id/ and https://www.kompas.com/ with the keyword "covid vaccine". The dataset is divided into two, training data and test data. The training data is preprocessed and then used in model design. Test data is used to evaluate the results of model design. This process produces a machine learning model with a good accuracy level of 97.62%.
Combination Contrast Stretching and Adaptive Thresholding for Retinal Blood Vessel Image Anita Desiani; Irmeilyana Irmeilyana; Endro Setyo Cahyono; Des Alwine Zayanti; Sugandi Yahdin; Muhammad Arhami; Irvan Andrian
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1654

Abstract

To diagnose diabetic retinopathy is to segment the blood vessels of the retinal, but the retinal images in the DRIVE and STARE datasets have varying contrast, so the enhancement is needed to obtain a stable image contrast. In this study, image enhancement was performed using the Contrast Stretching and continued with segmentation using the Adaptive Thresholding on retinal images. The image that has been extracted with green channels will be enhanced with Contras Stretching and segmented with Adaptive Thresholding to produce a binary image of retinal blood vessels. The purpose of this study was to combine image enhancement techniques and segmentation methods to obtain valid and accurate retinal blood vessels. The test results on DRIVE were 95.68 for accuracy, 65.05% for sensitivity, and 98.56% for specificity. The test results of Adam Hoover’s ground truth on STARE were 96.13% for, 65.90% for sensitivity, and 98.48% for specificity. The test results for Valentina Kouznetsova’s ground truth on the STARE were 93.89% for accuracy, 52.15% for sensitivity, and 99.02% for specificity. The conclusion obtained is that the processing results on the DRIVE and STARE datasets are very good with respect to their accuracy and specificity values. This method still needs to be developed to be able to detect thin blood vessels with the aim of being able to improve and increase the sensitivity value obtained.
Co-Authors Ajeng Islamia Putri Al Khaidar Amalia, Ismi Anita Desiani Annisa Nur Fauza Anwar Anwar Azhar Azhar Kholiq Affandi Cut Adnin Nalisa Derry Fajirwan Des Alwine Zayanti Dwi Meilvinasvita Endang Sri Kresnawati Endro Setyo Cahyono Guntur Syahputra Hendrawati Hendrawati Hendrawaty Husaini Husaini Husaini Husaini Huzaeni Huzeni Huzeni Indra Mawardi Indrawati Irmeilyana Irmeilyana Irvan Andrian Khairul Azman Khairunnisa Atami Mahdi Mahdi Muhammad Irmansyah Muhammad Muhammad Muhammad Nawawi Muhammad Nawawi Muhammad Rizka Muhammad Rizki Mustainul Abdi Nabila Mufida Nadia Ulfa Naufal Rachmatullah Naziruddin Naziruddin Nelly Safitri Ning Eliyati Novi Rustiana Dewi Qatrun Nada Renol Herwinsyah Rifkie Primartha Rizaldo Setiawan Safriadi Safriadi Salahuddin Salahuddin Salma Sheila Maulina Putri Shania Putri Andhini Simbolon, Zulfan Khairil Sugandi Yahdin Sugandi Yahdin Sugandi Yahdin Titania Jeanni Charissa Yuli Andirani Yulia Resti Zamzami Zhenyu Cui