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All Journal Journal of ICT Research and Applications JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Riset Informatika eProceedings of Engineering
Marisa W. Paryasto
Telkom University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Journal : Jurnal Riset Informatika

 1 
ALEXNET ARCHITECTURE AND FUZZY ANALYSIS ON TALENT JUDGE DECISION PREDICTION BASED ON FACIAL EXPRESSION Muhammad Zaki; Anggunmeka Luhur Prasasti; Marisa W. Paryasto
Jurnal Riset Informatika Vol 4 No 4 (2022): Period of September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (971.731 KB) | DOI: 10.34288/jri.v4i4.435

Abstract

The expression on the human face is a means of non-verbal communication. In the talent search event, the facial expressions shown by the judges when watching the participants' performances became one of the components to see whether the contestant who was performing could qualify for the next round or he would fail. Haar cascade is used to provide the location of the face in the frame and to classify the expressions on the face, a CNN model with modified AlexNet architecture is used which increases the accuracy by 5% from the original alexnet. A fuzzy Algorithm is used to predict the judge's decision based on how many facial expressions appear during the participant's appearance. The decision prediction system for talent search judges based on facial expressions using fuzzy is considered effective in predicting decisions, after being tested the system can predict decisions with an accuracy rate of 83%.
RESTAURANT DENSITY PREDICTION SYSTEM USING FEED FORWARD NEURAL NETWORK Muhammad Kurnia Sandi; Anggunmeka Luhur Prasasti; Marisa W. Paryasto
Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i2.58

Abstract

In this day and age, information about something is so important. The level of trust of modern society depends on the testing of information. Tested and accurate information will have a good impact on the community. One of the important but often missed information is information about the density of a restaurant. Information about restaurant density is important to know because it can affect the actions of someone who will visit the restaurant. This information is also useful to provide information in advance so that diners avoid full restaurants to avoid the spread of the Covid-19 virus, among other things. With limited operating hours as well as the number of restaurant visitors, information about the density of a restaurant becomes much needed. The lack of information on restaurant density is a major problem in the community. The needs of the community, made this study aims to predict the density of a restaurant an hour later. Based on survey data and existing literature data, with simulation methods and also system analysis built using feedforward neural network artificial intelligence architecture and then trained with Backpropagation algorithms produced an accuracy of 97.8% with literature data.
ALEXNET ARCHITECTURE AND FUZZY ANALYSIS ON TALENT JUDGE DECISION PREDICTION BASED ON FACIAL EXPRESSION Muhammad Zaki; Anggunmeka Luhur Prasasti; Marisa W. Paryasto
Jurnal Riset Informatika Vol. 4 No. 4 (2022): September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (706.456 KB) | DOI: 10.34288/jri.v4i4.171

Abstract

The expression on the human face is a means of non-verbal communication. In the talent search event, the facial expressions shown by the judges when watching the participants’ performances became one of the components to see whether the contestant who was performing could qualify for the next round or he would fail. Haar cascade is used to provide the location of the face in the frame and to classify the expressions on the face, a CNN model with modified AlexNet architecture is used which increases the accuracy by 5% from the original alexnet. A fuzzy Algorithm is used to predict the judge’s decision based on how many facial expressions appear during the participant’s appearance. The decision prediction system for talent search judges based on facial expressions using fuzzy is considered effective in predicting decisions, after being tested the system can predict decisions with an accuracy rate of 83%.
Co-Authors Andi Tiana Putra ANGGUNMEKA LUHUR PRASASTI Annisa Farhah Apendi, Siroojuddin Budi Rahardjo Casi Setianingsih Chianyung, Chianyung Fajar Yuliawan Farradita Imanda Firdaus, Ilham Muhamad Fuadi, Farhan Gemilang, Galih Karya GHOFAR, MUHAMMAD ABDUL Heriansyah, Rifqi Baihaqi Putra Ida Wahidah Hamzah Intan Muchtadi Alamsyah Islam , Muhammad Izzudin Jaya Santoso Sirait Kalista, Meta KUSPRIYANTO Luthfy, Dicky M. Luthfan Alwafi Maulana, Erwan Muhammad Kurnia Sandi Muhammad Zaki Muhammad, Ario Syawal Nugroho, Muhamad Ikhsan Pratama, Muhammad Farras Adi Rahma, Alifia Mutiara Riswandi, M. Ghalib Rumani M Rumani M Rumani Rumani Sarfina Adilah Savrylia, Dewi Intan Syam, Rizky Ramadhani Thalib, Wildan Toscana, Alwi Zulfauzi Zakir, Gilman Muslih