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All Journal Journal of Blockchain, Nfts and Metaverse Technology
Agung Triayudi
Informatics Study Program, Faculty of Communication and Information Technology, Universitas Nasional, Jakarta, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science

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Journal : Journal of Blockchain, Nfts and Metaverse Technology

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Sentiment Analysis on Twitter Using Naïve Bayes and Logistic Regression for the 2024 Presidential Election Alisya Mutia Mantika; Agung Triayudi; Rima Tamara Aldisa
SaNa: Journal of Blockchain, NFTs and Metaverse Technology Vol. 2 No. 1 (2024): February 2024
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/sana.v2i1.267

Abstract

In accordance with the notion of democracy which is the basis of the state of Indonesia, general elections will be held in 2024. In the implementation of the General Election there is a campaign to lead the public vote to choose the best candidate according to public opinion. Twitter social media is one of the media to voice opinions as well as share information to become one of the indirect campaigning platforms. Social media also does not escape negative issues, community rumors, and even the digital footprint of presidential candidates which can be a very important consideration in campaigning. This research aims to see the public's response to the 2024 presidential candidates. This research is conducted based on public opinion on presidential candidates, then public opinion data taken from Twitter social media will go through a pre-processing process to clean the data before the data is classified into Naive Bayes and Linear Regression modeling. The two classification models are then sought for the highest performance accuracy value and confusion matrix with 80:20 splitting data. The results showed that the Naive Bayes classification model had a higher accuracy value than the Logistic Regression classification model, which was 63% for Anies Baswedan candidate, 77% for Ganjar Pranowo candidate, and 44% for Prabowo Subianto. The highest accuracy value was obtained by the sentiment data of 2024 presidential candidate Ganjar Pranowo, which was 77%.
Implementation of Face Recognition for Lecturer Attendance Using Deep Learning CNN Algorithm Fajhar Muhammad; Agung Triayudi; Eri Mardiani
SaNa: Journal of Blockchain, NFTs and Metaverse Technology Vol. 2 No. 1 (2024): February 2024
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/sana.v2i1.270

Abstract

Using the Convolutional Neural Network (CNN) algorithm, this research aims to create a better lecturer attendance application that improves the attendance system and creates peace of mind when lecturers arrive at national universities. The author analyses the results of applying deep learning algorithms to an experimental face recognition system that uses convolutional neural networks. The purpose of this study is to show that deep learning algorithms can improve the accuracy and efficiency of recording presence. In addition, the goal of this research is to create a timekeeping application using face recognition technology that is expected to have a high level of accuracy. In addition, this research includes a modification of the CNN model. This modification resulted in an epoch value of 75 for training of 100% and test of 95%. Analysis of results, drawing conclusions, and suggestions for additional development are the final stages of this research. Evaluation of the integrated system is done by collecting actual attendance data and comparing it with the attendance records created by the system. This validation will help explain the performance of the system and find problems or vulnerabilities that may need to be fixed.
Implementation of Face Recognition for Lecturer Attendance Using Deep Learning CNN Algorithm Fajhar Muhammad; Agung Triayudi; Eri Mardiani
SaNa: Journal of Blockchain, NFTs and Metaverse Technology Vol. 2 No. 2 (2024): August 2024
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/sana.v2i2.275

Abstract

Using the Convolutional Neural Network (CNN) algorithm, this research aims to create a better lecturer attendance application that improves the attendance system and creates peace of mind when lecturers arrive at national universities. The author analyzes the results of applying deep learning algorithms to an experimental face recognition system that uses convolutional neural networks. The purpose of this study is to show that deep learning algorithms can improve the accuracy and efficiency of recording presence. In addition, the goal of this research is to create a timekeeping application using face recognition technology that is expected to have a high level of accuracy. In addition, this research includes a modification of the CNN model. This modification resulted in an epoch value of 75 for training of 100% and test of 95%. Analysis of results, drawing conclusions, and suggestions for additional development are the final stages of this research. Evaluation of the integrated system is done by collecting actual attendance data and comparing it with the attendance records created by the system. This validation will help explain the performance of the system and find problems or vulnerabilities that may need to be fixed
Co-Authors Alisya Mutia Mantika Eri Mardiani Fajhar Muhammad Rima Tamara Aldisa