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All Journal Journal of Applied Electrical Engineering Jurnal Rekayasa elektrika
Abdullah Sani
Politeknik Negeri Batam
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Pembuatan Alat Otomatis Hand Sanitizer sebagai Salah Satu Antisipasi Penyebaran COVID-19 di Politeknik Negeri Batam Budiana Budiana; Abdullah Sani; Daniel Sutopo Pamungkas; Muhammad Prihadi Eko Wahyudi; Lindawani Siregar; Sumantri Kurniawan Risandriya; Kamarudin Kamarudin; Nur Sakinah Asaad; Nadhrah Wivanius; Rizky Pratama Hudhajanto; Aditya Gautama Darmoyono; Rahmi Mahdaliza; Ardian Budi Kusuma Atmaja; Arif Wahyu Budiarto; Yulfiana Harini; Bayu Prayogo Setiawan; Indra Daulay; Dodi Radot Lumbantoruan
Journal of Applied Electrical Engineering Vol 4 No 2 (2020): JAEE, December 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaee.v4i2.2730

Abstract

COVID-19 menyerang manusia pada akhir tahun 2019. Penyebaran COVID-19 terjadi melalui droplet/cairan yang keluar dari mulut/hidung manusia. Antisipasi penyebaran COVID-19 dilakukan dengan menerapkan pola hidup bersih dan sehat. Salah satu caranya adalah dengan mencuci tangan menggunakan hand sanitizer. Penggunaan hand sanitizer di tempat umum memungkinkan terjadinya kontak fisik antar pengguna sehingga diperlukan cara untuk mengurangi kontak fisik tersebut. Cara yang bisa diterapkan adalah dengan menggunakan hand sanitizer otomatis. Prinsip dari hand sanitizer otomatis ini adalah ketika tangan didekatkan dengan botol hand sanitizer maka secara otomatis cairan akan keluar dengan sendirinya ke telapak tangan. Berdasarkan hasil penelitian yang telah dilakukan, hand sanitizer telah berhasil dibuat dan dapat digunakan di Politeknik Negeri Batam.
Deteksi Gestur Tangan Berbasis Pengolahan Citra Abdullah Sani; Suci Rahmadinni
Jurnal Rekayasa Elektrika Vol 18, No 2 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2649.209 KB) | DOI: 10.17529/jre.v18i2.25147

Abstract

Hand sign language is a medium of communication for people with disabilities (deaf and speech impaired). However, in social practice, persons with disabilities may have to communicate with non-disable persons who do not understand sign language. These problems can be overcome with the help of translators or normal people learning sign language through existing media such as videos. Unfortunately, this method will probably cost a lot of money and time. In respons to this issue, the present study designed a sistem to detect hand gestures based on image processing. The method used is the You Only Look Once (YOLO) algorithm. The YOLO algorithm can detect and classify objects at once without being influenced by the light intensity and background of the object. This algorithm is a deep learning method that is more accurate than other deep learning methods. From this research, the system can detect and classify hand gestures with different backgrounds, light intensity, and distances with an accuracy rate above 90%.
Deteksi Gestur Tangan Berbasis Pengolahan Citra Abdullah Sani; Suci Rahmadinni
Jurnal Rekayasa Elektrika Vol 18, No 2 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i2.25147

Abstract

Hand sign language is a medium of communication for people with disabilities (deaf and speech impaired). However, in social practice, persons with disabilities may have to communicate with non-disable persons who do not understand sign language. These problems can be overcome with the help of translators or normal people learning sign language through existing media such as videos. Unfortunately, this method will probably cost a lot of money and time. In respons to this issue, the present study designed a sistem to detect hand gestures based on image processing. The method used is the You Only Look Once (YOLO) algorithm. The YOLO algorithm can detect and classify objects at once without being influenced by the light intensity and background of the object. This algorithm is a deep learning method that is more accurate than other deep learning methods. From this research, the system can detect and classify hand gestures with different backgrounds, light intensity, and distances with an accuracy rate above 90%.
Co-Authors Aditya Gautama Darmoyono Ardian Budi Kusuma Atmaja Arif Wahyu Budiarto Bayu Prayogo Setiawan Budiana Budiana Daniel Sutopo Pamungkas Dodi Radot Lumbantoruan Indra Daulay Kamarudin Kamarudin Lindawani Siregar Muhammad Prihadi Eko Wahyudi Nadhrah Wivanius Nur Sakinah Asaad Rahmi Mahdaliza Rizky Pratama Hudhajanto Suci Rahmadinni Sumantri Kurniawan Risandriya Yulfiana Harini