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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Master Pariwisata (JUMPA) Jurnal Sistem Cerdas Jurnal Mantik
Yohanssen Pratama
Institut Teknologi Del
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Deep convolutional neural network for hand sign language recognition using model E Yohanssen Pratama; Ester Marbun; Yonatan Parapat; Anastasya Manullang
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (723.859 KB) | DOI: 10.11591/eei.v9i5.2027

Abstract

An image processing system that based computer vision has received many attentions from science and technology expert. Research on image processing is needed in the development of human-computer interactions such as hand recognition or gesture recognition for people with hearing impairments and deaf people. In this research we try to collect the hand gesture data and used a simple deep neural network architecture that we called model E to recognize the actual hand gestured. The dataset that we used is collected from kaggle.com and in the form of ASL (American Sign Language) datasets. We doing accuracy comparison with another existing model such as AlexNet to see how robust our model. We find that by adjusting kernel size and number of epoch for each model also give a different result. After comparing with AlexNet model we find that our model E is perform better with 96.82% accuracy.
Detection roasting level of Lintong coffee beans by using euclidean distance Yohanssen Pratama; I Gde Eka Dirgayussa; Paian Fernando Simarmata; Mia Hotmaria Tambunan
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3153

Abstract

Coffee roasting is the process by which raw coffee beans (green beans) are roasted until they reach a certain roast level. In general, the roast level of roasted coffee beans is divided into 3 levels, namely the roast level of light, medium and dark. One way to find out the roast level of roasted coffee beans is to see the color change of the coffee beans. However, it is very difficult to know the exact color conditions of each roast level of roasted coffee beans and this can be overcome by build an automatic coffee roasting equipment. In this research, an automatic coffee roaster was done with a system that is able to control the roasting temperature and stirring of coffee beans. This tool can also monitor the change in color of the coffee beans during the roasting process. The system that has been implemented can detect color changes and classify the level of dark roast of roasted coffee beans using the Euclidean distance algorithm. The Euclidean distance give a threshold to classified the roast level. The system accuracy for predicting coffee beans color at the level of dark roast is 90% and 80% for overall.
Co-Authors Ade Erispra Rismanda Anastasya Manullang Astri Monica Sianturi Bona Simanullang Emma Hannisa Laurencia Nainggolan Ester Marbun Henny Flora Panjaitan I Gde Eka Dirgayussa Josua Rumahorbo Lit Malem Ginting Mega Sari Pasaribu Mia Hotmaria Tambunan Mukhammad Solikhin Paian Fernando Simarmata Parmonangan R. Togatorop Pebri Sangmajadi Sinaga Purnama Pasaribu Riyanthi Angrainy Sianturi Romauli Sianipar Simanjuntak, Mariana Sinaga, Arnaldo Marulitua Tegar Arifin Prasetyo Verawaty Situmorang Victor Lambok Desrony Yonatan Parapat