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All Journal Jurnal Buana Informatika Jurnal Pendidikan dan Kajian Seni Techno Nusa Mandiri : Journal of Computing and Information Technology JURIKOM (Jurnal Riset Komputer) JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH Minda Baharu JURSIMA (Jurnal Sistem Informasi dan Manajemen) Journal Sensi: Strategic of Education in Information System JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Digit : Digital of Information Technology Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jutsi: Jurnal Teknologi dan Sistem Informasi Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer J-Com (Journal of Computer) J-Intech (Journal of Information and Technology) BEES: Bulletin of Electrical and Electronics Engineering Jurnal Komtekinfo JURSIMA Inspiration: Jurnal Teknologi Informasi dan Komunikasi JCOME (Journal of Computing and Multimedia Enginering Bulletin of Informatics and Data Science Jurnal Sistem Informasi dan Manajemen Scientific literacy Innovation and Technology Journal JoDENS: Journal of Digital Ecosystem for Natural Sustainability Journal of Information Technology
Pernando, Yonky
Universitas Universal
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Journal : Bulletin of Informatics and Data Science

 1 
Penerapan Metode Weighted Product dalam Penyeleksian Supervisor Terbaik Adrianus Gultom; Titus Kristanto; Yonky Pernando; Joko Kuswanto; Nursaka Putra; Amsar Amsar
Bulletin of Informatics and Data Science Vol 2, No 1 (2023): May 2023
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In this study, the authors used the Weighted Product (WP) method to select the best supervisor at PT PLN (Persero). The Weighted Product (WP) method is the most appropriate method to be used in the selection stage for candidates with the potential to be the best supervisors. In applying the WP (Weighted Product) method, managers can rank by looking for the weight value of each attribute. The results of the study stated that alternative A2 had the best value with a value of 0.11877
Lightweight Deep Learning for Object Detection on Mobile Device Lika, Sudiharyanto; Pernando, Yonky; Kurniawan, Ade
Bulletin of Informatics and Data Science Vol 2, No 2 (2023): November 2023
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v2i2.82

Abstract

Computer vision is a research in the development of technology to obtain information from images and replicate or imitate human visual processes, so that computers can know the objects around them. Deep learning is now the key word as a new era in machine learning that trains computers in finding patterns from large amounts of data. The Convolution Neural Networks (CNN) algorithm has proven impressive in terms of performance for detecting objects, image classification and semantic segmentation. Object detection is a technique used to identify the type of object in an image and also the exact location of the object in the image. Face detection is one of the most challenging problems of pattern recognition. Effective training needs to be done to be able to detect faces effectively. The accuracy in face detection using machine learning does not give good results. This research focuses on the level of accuracy of detecting faces using deep learning methods. This study compares the level of accuracy of deep learning and machine learning in detecting faces effective and efficient. This study uses the Convolution Neural Networks (CNN) model in the deep learning method to detect faces in real time on Android. According to the test results, the accuracy obtained in this study reached 97.97% in several normal facial conditions and face masks.
Co-Authors Adinda Tria Suci Adrianus Gultom Aldi Setiawan, Aldi Amsar Amsar Andhika Anggeliani, Cynthia angreyani, jeny Anton, Oey Axel Arya Maitri Cundana Axel Arya Maitri Cundana Bahar, Hardi Candli, Candli Delvin Desyanti Febrianti, Eka Lia Givi Ofgivia Gusnardi Fernandus Gago Hendra, Doni Febri Herdiansya Herdiansya Huang Wijaya, Jacky Ilwan Syafrinal Ismail, Asriyani Joko Kuswanto Jordan Hue Kaharuddin Kaharuddin Kaharuddin Kaleb Yefune Sun Keven Leonando Hong Kurniawan, Ade Lika, Sudiharyanto lim, sheren LUBIS, SYARIF HIDAYAH M. Ibnu Safari Marfuah Marfuah Meidianto, Meidianto Metta Paramita Musliadi K H Nursaka Putra Oey Anton Ofgivia, Givi Prativi, Martina Purnajaya, Akhmad Rezki Raymond Erz Saragih Robi Robi Roni Zeki Roza, Yuni Ryan Prayoga Samsul Lutfi Saragih, Raymond Erz Simanjuntak, Nurchaya Sintia Sintia song, Meidianto Steffi Adam Thionuartha, Kevin Titus Kristanto Verdian, Ihsan Vinson Deslian Wijaya Massie, Alamsyah Yanto, Endri Yodi Yuni Roza Yuni Roza Yuni Roza