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All Journal JREC (Journal of Electrical and Electronics) Jurnal Telematika Journal of Students‘ Research in Computer Science (JSRCS) Journal of Computer Science Contributions (Jucosco)
Ekawati, Inna
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Journal : Journal of Students‘ Research in Computer Science (JSRCS)

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Analisis Sentimen Masyarakat Terhadap Kebijakan Pemerintah Selama Pandemi Covid-19 Menggunakan Algoritma Naïve Bayes Emeraldi, Muhammad Aqil; Ekawati, Inna; Sumadyo, Malikus
Journal of Students‘ Research in Computer Science Vol. 3 No. 1 (2022): Mei 2022
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/ycx3jc57

Abstract

The increase in data is very large, one of the sources comes from social media, especially Twitter which talks a lot about Covid-19 . The news through Twitter media regarding the impact of the Covid-19 virus is widely discussed because it causes unrest for the public which has led to the issuance of various government policies with the aim of preventing the spread of Covid-19 . Related to this, it is necessary to conduct a sentiment analysis of the text contained in the Twitter media. In this study, a sentiment analysis process was carried out related to public sentiment towards government policies during the Covid-19 pandemic in Indonesia on Twitter social media using the Naive Bayes Classifier method where the data used was classified into 2 sentiment values, namely positive and negative sentiment. The data used are 300 positive tweets data and 300 negative tweets data, where 80% of the total data is used as training data and 20% data is used as test data. Based on the test results, the data with a total of 120 tweets obtained the results of measuring the recall value of 93.33%, precision 93.33%, F-Score 93.33% and an average accuracy of 93.33%.
Deteksi Emosi Menggunakan Convolutional Neural Network Berdasarkan Ekspresi Wajah Ekawati, Inna; Putra, Fadilla Nidya Riyanto; Sumadyo , Malikus; Whidhiasih, Retno Nugroho
Journal of Students‘ Research in Computer Science Vol. 5 No. 1 (2024): Mei 2024
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/h0kayy31

Abstract

Facial expression recognition is an effective method for identifying someone's emotional expression. Emotional expressions can be recognized from changes in facial expressions, wrinkles on the forehead, blinking of the eyes, or changes in facial skin color. Facial expressions that a person generally has, such as neutral, angry, happy expressions. The problem that often occurs is the subjective assessment of a person's expression. This research examines how artificial intelligence can recognize facial expressions. The facial recognition process in the research uses a Convolutional Neural Network (CNN), which is a deep learning method capable of carrying out an independent learning process for object recognition, object extraction and classification and can be applied to high resolution images that have a nonparametric distribution model. The two main stages in CNN are feature learning and classification. The results of facial expression recognition can be used to detect a person's emotions. This research uses the FER2013 dataset which contains images of happy, sad, angry, afraid, surprised, disgusted and neutral emotions. The data set in the research received tests that had been carried out, namely the percentage of accuracy level in the model was 76%. It is hoped that the classification of emotions resulting from this research can contribute to the development of artificial intelligence technology and as a tool in various fields such as psychology, education and others. For further research, it can be developed further by adding other architectures such as VGG19, MobileNet, and ResNet-50 so that the resulting CNN model is more optimal.
Co-Authors Agung Sucipto, Putra Wisnu Dadan Irwan Emeraldi, Muhammad Aqil Firasanti, Annisa Haryono Haryono Hendharsetiawan, Andy Achmad Herlawati Herlawati KHUSNUL YAQIN Malikus Sumadyo Muhammad Amin Bakri Prima Dina Atika Putra, Fadilla Nidya Riyanto Rahmadya Trias Handayanto Retno Nugroho Whidhiasih Sumadyo , Malikus