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All Journal Jurnal Teknologi dan Manajemen Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Technomedia Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Capture : Jurnal Seni Media Rekam Building of Informatics, Technology and Science TEPIAN Jurnal Teknik Informatika (JUTIF) Business Economic, Communication, and Social Sciences Journal (BECOSS) Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Pendidikan dan Teknologi Indonesia Jurnal Locus Penelitian dan Pengabdian Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Indonesian Journal of Computer Science
Santoso, Handri
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Religion Aerospace Engineering Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences

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Journal : Building of Informatics, Technology and Science

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Penerapan Gamma Correction Dalam Peningkatan Pendeteksian Objek Malam Pada Algoritma YOLOv5 Fransisca, Viviana; Santoso, Handri
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3553

Abstract

YOLOv5 (You Only Look Once) is a popular object detection method used in the field of computer vision. YOLOv5 is often used to detect objects in images and videos in real-time with high speed and accuracy. This method is easy to use because it is open-source, so it can be directly used to create a model that fits the object you want to detect. YOLOv5 can easily recognize objects detected during the day, but this method has difficulties when it is made to detect objects at night. With the improvisation of the YOLOv5 method which can accurately detect objects at night, other researchers who wish to conduct research related to object detection at night can use the exact technique to produce more accurate object detection. This study uses the Gamma Correction method by adding a Gamma of 2 so that the trained image dataset becomes bright and YOLOv5 can recognize objects at night more easily. As a result, an improvised technique using Gamma Correction can make YOLOv5 recognize objects and make detections at night more accurately, where the average accuracy obtained before improvisation is 0.846, while after improvisation the results obtained are 0.918. From these average results, it can be stated that the gamma correction method can improve the accuracy results in object detection on YOLOv5
Personal Training with Tai Chi: Classifying Movement using Mediapipe Pose Estimation and LSTM Suhandi, Vartin; Santoso, Handri
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5536

Abstract

This research aims to tackle challenges in the practice of Tai Chi Bafa Wubu (BWTC), where limited access to trained instructors and daily schedules hinder training consistency. The proposed approach combines Human Pose Estimation technology using Mediapipe with Long Short-Term Memory (LSTM) models to classify BWTC movements. This method utilizes video datasets collected from the internet and augmented to train LSTM models, focusing on An, Ji, and Zhou movements. Experimental results show that the model can predict movements with high accuracy in training and direct user trials. The development of these techniques facilitates more effective self-training in Tai Chi, leveraging advanced AI technology to improve movement supervision and user movement interpretation accuracy. This study not only offers a practical solution to enhance Tai Chi training efficiency and accessibility but also explores the potential application of pose estimation technology and machine learning in broader sports movement monitoring and evaluation. It is expected that this research will make a significant contribution to health and fitness by enabling individuals to independently practice Tai Chi with technological guidance, promoting better mental and physical health among the general public.
Co-Authors Akbar, Achmad Fathurrazi Akbariani, Nurhaliza Vania Amanda, Djaja Andrew Andrew, Andrew Aprilia, Novia Pramesti Dazki, Erick Devanda, Faustin Eko Hadianto, Eko Fransisca, Viviana Harlis Setiyowati HARYONO Heni Jusuf Hindarto, Djarot Ho, Patricia Indrajit, Eko ISWAHYUDI ISWAHYUDI Karjadi, Daniel Avian Karjadi, Markus Laurenso, Justin Lijaya, Hendra Lucia Sri Istiyowati Lupitha, Mariska Mareta, Arvin Maria Magdalena Mohammad, Saiful Azhari Perdana, Nanda Praharto, Suwandhy Prawira, Khairul Thamrin Priswanto, Budi Purwadi Purwadi Putri, Gladys Rahman, Ben Ramadhoni, Moehammad Richardus Eko Indrajit Rochadiani, Theresia Herlina Ruddin, Isra Sakti, Irwin Sandjaya, Julio Santoso, Billy Cahyo Saputra, Adi Dwifana Sari, Muh Masri Sentosa, Steve Suhandi, Vartin Suherman, Endang Sumargo, Ruly Sze, Edward Wedha, Alessandro Enriqco Putra Bayu Wedha, bayu Yasa Widjaja, Herman