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All Journal CHIPSET (Journal on Computer Hardware, Signal Processing, Embedded System and Networking) The Indonesian Journal of Computer Science
Derisma
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Implementasi Voice Recognition dan Sensor Ultrasonik pada Televisi Ivani Resti Eisa; Derisma
CHIPSET Vol. 2 No. 02 (2021): Journal on Computer Hardware, Signal Processing, Embedded System and Networkin
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (443.291 KB) | DOI: 10.25077/chipset.2.02.41-46.2021

Abstract

Voice or speaker recognition is the ability of a machine or program to receive and interpret dictation or to understand and carry out spoken commands. Library is used by Voice recognition in this system is keras and tensorflow. Keras is one library often use for machine learning. Watching TV is a daily routine that is carried out by almost everyone from all walks of life.Most of them don't pay attention to the process and distance in watching TV.Sensor ultrasonic is used for detect object from TV in ideal distance.
Portable Cough Classification System Based on Sound Feature Extraction Using Tiny Machine Learning Arief, Lathifah; Risky, Mutiah; Derisma; Kasoep, Werman; Puteri, Nefy
The Indonesian Journal of Computer Science Vol. 10 No. 2 (2021): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v10i2.3001

Abstract

Cough is one of the most common markers that can provide information in diagnosing a disease. More specifically, cough is a common symptom of many respiratory infections. There are several types of cough, including: dry cough, wet cough (cough with phlegm), croup cough and whooping cough. This study aims to create a system that can classify the sounds of coughing up phlegm, dry cough, whooping cough and croup cough. The system development uses the concept of tiny machine learning. In the system built, Arduino Nano 33 BLE Sense is used as a control device and LED is used as an output device. In this study, the classification of dry cough, wet cough, croup cough and whooping cough was performed using the MFCC voice feature extraction. In the process of classifying coughing sounds using the Neural Network Classifier, the system has a percentage of dataset training accuracy from a total of 5 classes (croup, dry, noise, wet, whooping) of 97.1% by applying an epoch value of 500, window size 3000ms and a window increase of 500ms.
Co-Authors Ivani Resti Eisa Kasoep, Werman Lathifah Arief, Lathifah Puteri, Nefy Risky, Mutiah