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Journal : Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control

Design of Application On/Off Electronic Device with Markov Model Using Speech Recognition on Android Widyatmoko, Rochman; Purwantoro E. S. G, Sugeng; Syahbana S, Yoanda Alim
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 3, No 3, August 2018
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (345.266 KB) | DOI: 10.22219/kinetik.v3i3.640

Abstract

Electronic devices are supported by a switch that is used to turn the device on and off. Manually pressed switches with distances between remote switches to cause less efficiency in saving human time and manpower. This can be solved by building a system to control electronic devices automatically. The system uses human voice commands to turn on and off electronic devices. The command will be processed into text by the Google Voice Speech Recognition library. The Android app sends human commands that have been processed by Arduino Uno R3 microcontroller. Commands are obtained after the text and data in the database are processed using the Markov Model algorithm. Communication between Android smartphone and microcontroller will be designed through a WIFI network. This system is tested based on noise level with data accuracy level with noise 0-45 dB and obtained 65% result. Based on the test response time obtained that the noise level 0-45 dB obtained results of 5.41 seconds. Based on the test results from the scenario, it can be concluded that the lower the noise generated, the better the system will also respond to commands. From the test suitability get value X = 1, meaning that the system is suitability with error rate 0. In testing accuracy to view status function get value 0 with error level 0. Testing of Markov model algorithm yields the calculated 0.125 algorithms manually and code for each command
Design of Application On/Off Electronic Device with Markov Model Using Speech Recognition on Android Rochman Widyatmoko; Sugeng Purwantoro E. S. G; Yoanda Alim Syahbana S
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 3, No 3, August 2018
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (345.266 KB) | DOI: 10.22219/kinetik.v3i3.640

Abstract

Electronic devices are supported by a switch that is used to turn the device on and off. Manually pressed switches with distances between remote switches to cause less efficiency in saving human time and manpower. This can be solved by building a system to control electronic devices automatically. The system uses human voice commands to turn on and off electronic devices. The command will be processed into text by the Google Voice Speech Recognition library. The Android app sends human commands that have been processed by Arduino Uno R3 microcontroller. Commands are obtained after the text and data in the database are processed using the Markov Model algorithm. Communication between Android smartphone and microcontroller will be designed through a WIFI network. This system is tested based on noise level with data accuracy level with noise 0-45 dB and obtained 65% result. Based on the test response time obtained that the noise level 0-45 dB obtained results of 5.41 seconds. Based on the test results from the scenario, it can be concluded that the lower the noise generated, the better the system will also respond to commands. From the test suitability get value X = 1, meaning that the system is suitability with error rate 0. In testing accuracy to view status function get value 0 with error level 0. Testing of Markov model algorithm yields the calculated 0.125 algorithms manually and code for each command
Object Detection and Monitor System for Building Security Based on Internet of Things (IoT) Using Illumination Invariant Face Recognition Chatisa, Ivan; Syahbana, Yoanda Alim; Wibowo, Agus Urip Ari
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 1, February 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i1.1622

Abstract

Theft and intrusion are crimes that often occur in neighborhoods when there is opportunity or negligence by owners and security personnel. Many studies have been carried out to improve environmental security by applying cameras as a surveillance medium. However, the camera is not optimal in detecting objects when the lighting conditions are lacking. Therefore, in this study, a monitoring and object detection system was built by applying the Illumination Invariant model. This model is used to improve the appearance of the image from light and shadow reflections. The process of detecting and identifying objects is done by using human facial features (face detection) captured by the camera. The camera used is a Logitec C270 Webcam 720p which is connected via a USB port on the Raspberry Pi 4. The Raspberry Pi 4 processes human face image data and sends the processing results to a MySQL database using the HTTP protocol. Data transmission is done using the Python Flask web framework. The system was successfully run 100% by using black box testing of all functional requirements. Tests on the object detection feature were carried out based on different lighting conditions 15 times by comparing the original image and the results of the Illumination Invariant implementation. Based on the test results obtained object detection accuracy of 86.7%.