Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Vol 5 No 5 (2021): Mei 2021

Pengenalan Gesture Tangan Untuk Otomatisasi Switching Saklar Menggunakan Metode KNN Berbasis Raspberry Pi

Misran Misran (Fakultas Ilmu Komputer, Universitas Brawijaya)
Fitri Utaminingrum (Fakultas Ilmu Komputer, Universitas Brawijaya)
Rizal Maulana (Fakultas Ilmu Komputer, Universitas Brawijaya)



Article Info

Publish Date
30 Apr 2021

Abstract

Research on switching automation has been done a lot, both by using smartphone control, infrared sensors to using voice commands. The switching automation process used to turn lights on or off can also be done using hand gestures. By using the K-Nearest Neighbor method the computer can understand human interaction quite well using the method of decision making from existing patterns. In this study, the K-Nearest Neighbor method was used to translate hand signals or hand gestures into a command to control the LED. The test was carried out using 5 volunteers, each of whom tested each hand gesture given. To get the results of gesture recognition, there are several steps that must be taken, namely skin detection, preprocessing process, Feature Extraction, K-NN, and finally the system output. 3. The accuracy produced by the system is very good, where by conducting several experiments, the accuracy results obtained for five volunteers is 80%. Research on switching automation has been done a lot, both by using smartphone control, infrared sensors to using voice commands. The switching automation process used to turn lights on or off can also be done using hand gestures. By using the K-Nearest Neighbor method the computer can understand human interaction quite well using the method of decision making from existing patterns. In this study, the K-Nearest Neighbor method was used to translate hand signals or hand gestures into a command to control the LED. The test was carried out using 5 volunteers, each of whom tested each hand gesture given. To get the results of gesture recognition, there are several steps that must be taken, namely skin detection, preprocessing process, Feature Extraction, K-NN, and finally the system output. 3. The accuracy produced by the system is very good, where by conducting several experiments, the accuracy results obtained for five volunteers is 80%.

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Journal Info

Abbrev

j-ptiik

Publisher

Subject

Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Description

Jurnal Pengembangan Teknlogi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya merupakan jurnal keilmuan dibidang komputer yang memuat tulisan ilmiah hasil dari penelitian mahasiswa-mahasiswa Fakultas Ilmu Komputer Universitas Brawijaya. Jurnal ini diharapkan dapat mengembangkan penelitian ...