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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JURNAL ISTEK RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jiko (Jurnal Informatika dan komputer) Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada Masyarakat Intellect : Indonesian Journal of Learning and Technological Innovation Coreid Journal Integrated : Journal Of Information Technology and Vocational Education
Nur Lukman
UIN Sunan Gunung Djati Bandung
Religion Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Journal : Coreid Journal

 1 
Attendance System Face Recognition Using Convolutional Neural Network (CNN) Setiawan, Rama Bhagadhara; Lukman, Nur
CoreID Journal Vol. 1 No. 3 (2023): November 2023
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v1i3.16

Abstract

This article discusses the development of technology in various fields, with a focus on the implementation of digital technology and machine learning. Digitalization has influenced various aspects of life, including education and tourism. Machine learning, particularly convolutional neural networks (CNNs) and deep learning play an important role in these advancements, with applications extending from biology to healthcare. Face recognition technology, as part of biometrics, is highlighted in this article, used in various contexts such as security and enterprise management. This research implements CNN and Haar Cascade Classifier methods to build a face recognition system in the context of library attendance. With the tests conducted, the system achieved 95% accuracy, showing a good ability to detect faces in various conditions. In conclusion, the CNN algorithm can produce an effective face recognition system for use in library attendance systems, with reliable performance and high accuracy.
Long Short Term Memory Approach for Sentiment Analysis on COVID-19 Vaccination Policy Tubagus Putra, Fauzan Herdika; Zulfikar, Wildan Budiawan; Lukman, Nur
CoreID Journal Vol. 2 No. 2 (2024): July 2024
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v2i2.33

Abstract

COVID-19 vaccination is one of the efforts to reduce the spread of COVID-19 and reduce the impact or severe symptoms of COVID-19. On social media, many Indonesians have expressed their opinions regarding the COVID-19 vaccine. With technology, we can classify Indonesian public opinion on the COVID-19 vaccine on social media, including pros or cons. Sentiment analysis using the LSTM (Long Short Term Memory) algorithm is one way. The data that has been taken will go through a cleaning and weighting process using Word2Vec before entering the LSTM algorithm. With the evaluation method of the K-Fold Cross Validation model, we can determine the performance of this LSTM algorithm. The results of the performance of this LSTM model show an average accuracy of 74.1% and have the best accuracy in the 4th Fold, which is 81%. The data that has been taken will be tested on this best model, and the results of the sentiment analysis of Indonesian public opinion on the COVID-19 vaccine are 49.4% Positive and 50.6% Negative.
Co-Authors Andriyan, Acep Razif Arief Fatchul Huda Cecep Nurul Alam Cecep Nurul Alam, Cecep Nurul Deden Eka Purwanto Devi Novitasari Fatimah Umar Gladis Fadila Hannah Fauzia Rahma Herdiyanto, Reza Fahlevi Indah Sri Lestari, Indah Sri Jumadi Jumadi Maylawati, Dian Sa'adillah Maylawati, Maylawati Mohamad Irfan Mohamad Irfan, Mohamad Putri Apriyanti Rizky Agung Ardiansyah Sa’adillah, Dian Setiawan, Rama Bhagadhara Tubagus Putra, Fauzan Herdika Undang Syaripudin, Undang Wildan Budiawan Zulfikar Yana Aditia Gerhana Yana Aditia Gerhana, Yana Aditia