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All Journal Niagawan Jurnal Biologi dan Pembelajarannya (JBP) Jurnal Profesi Medika: Jurnal Kedokteran dan Kesehatan Majalah Pembelajaran Geografi Jurnal Berdaya Mandiri Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Progressive Mathematics Education Jurnal Pengabdian Kepada Masyarakat Abdi Nusa Jurnal Masyarakat Indonesia Linggau Journal Science Education (LJSE) Indonesian Journal on Data Science Teknomatika: Jurnal Informatika dan Komputer Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah Cakrawala : Management Science Journal JCSRD Journal of Islamic Economics and Business Studies (JIEBS) Public Health Journal
Kharisma
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Journal : Indonesian Journal on Data Science

 1 
Analisis Sentimen Di Media Sosial Twitter Dengan Studi Kasus Vaksinasi Covid-19 Nufia Alfi Rohyana; Aris Wahyu Murdiyanto; Kharisma
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 1 No. 1 (2023): Indonesian Journal on Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v1i1.861

Abstract

With the COVID-19 pandemic, the World Health Organization or WHO conducted research and research trials on the COVID-19 vaccine. The Indonesian government has made several policies, one of which is the "Mass Vaccination Program". However, the COVID-19 vaccination program in the field received mixed responses in the community, there were those who supported the vaccine program and some who rejected the vaccine program. In this study, researchers conducted research on sentiment analysis on the opinion of vaccination programs against anti-vaccine community groups based on Twitter social media data using the Naïve Bayes Classifier algorithm to provide information on opinion assessments that lead to positive and negative sentiments. Objective: The purpose of this study is to find out the public perception of AntiVaccine against the COVID-19 Vaccination Program in Indonesia. This study uses the Naïve Bayes Classification. The use of the Naïve Bayes Classifier (NBC). This research uses tweets obtained from Twitter with the keywords/hashtags “Anti Covid-19 Vaccines” or by collecting data based on accounts related to news about vaccination programs such as @ The Ministry of Health of the Republic of Indonesia. Data collection was carried out in the period August 2021-December 2021, with a total of 889 data. This study has succeeded in obtaining an accuracy of 72 % for testing. The result of the final sentiment analysis in the classification of the Anti-Vaccine group in this study is "Negative Sentimen".
Sistem Chabot Layanan Informasi Mahasiswa Menggunakan Algoritma Long Short-Term Memory Arumsari, Dewi; kharisma; Aesyi, Ulfi Saidata
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 2 No. 2 (2024): Indonesian Journal On Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v2i2.1489

Abstract

In the era of globalization and rapid information flow, the demand for efficient and accurate information, especially within academic institutions, is rising. Students often face challenges in accessing educational resources and real-time information, particularly outside official working hours. Existing online information services have limitations in providing continuous access. This research focuses on developing and evaluating a student information service chatbot system at Universitas Jenderal Achmad Yani Yogyakarta (UNJAYA) using the Long Short-Term Memory (LSTM) algorithm. The primary objective is to create a system that delivers real-time, accurate, and efficient information services to students. The Machine Learning Development Cycle (MLDC) is employed in the model development process, including stages such as data collection, processing, model training, evaluation, and implementation. The system's performance is tested using a questionnaire distributed to students, with responses measured on a Likert scale. The results demonstrate a chatbot with a 97.76% accuracy rate, 98.34% precision, and 97.76% recall. The overall system evaluation yielded an average score of 3.87, categorized as good. This research concludes that the LSTM-based chatbot successfully enhances information services at the Faculty of Engineering and Information Technology, providing an innovative solution to meet student needs in real-time
Co-Authors Ahmad Hanafi Ahmad Hanafi Ajmain Alfirna Rizqi Lahitani Alfun Roehatul Jannah Aris Wahyu Murdiyanto Arumsari, Dewi Ayunita, Elia Batjo, Athanaya Citra Savitri Dewi Rosa Indah, Dewi Rosa Elvika Fit Ari Shanti, Elvika Fit Ari Endah Puji Astuti, Endah Puji Fitrina Lestari Husaini Irmma Dwijayanti Irmma Dwijayanti Kusumaningtyas, Kartikadyota Lisa Solikhatin Sumantri Muhammad Erwan Syah Muhammad Habibi Muhammad Syairazi Nanik Lestariningsih Nguyen, Nga Thi Thu Nopiyanti, Nopa Nufia Alfi Rohyana Purnamaningsih, Nur’Aini Purwanto, Sigid Edy Putra, Bagja Rahma Rahmi Meutia Risky Amelia Rizki Wahyuning Damayanti Rukmi, Dwi Kartika Setyawati, Heni Siti Dila Nuratih SITI FATIMAH Sunarsih Suwarno Syifa Pramudita Faddila Tran, Thu-Giang Tri Sunarsih Tri, Elvika Fit Ari Shanti Ulfi Saidata Aesyi Wardianti, Yunita Yulinda, Dwi