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All Journal Efisiensi : Kajian Ilmu Administrasi Jurnal Ilmu Komputer Jurnal Ilmiah Kursor Telematika SMATIKA Jurnal Informatika Upgris JURNAL MEDIA INFORMATIKA BUDIDARMA InComTech: Jurnal Telekomunikasi dan Komputer JURNAL ILMIAH INFORMATIKA JCES (Journal of Character Education Society) SINTECH (Science and Information Technology) Journal Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Tekno Insentif IJISTECH (International Journal Of Information System & Technology) The IJICS (International Journal of Informatics and Computer Science) JUTEKIN (Jurnal Manajemen Informatika) JUMANJI (Jurnal Masyarakat Informatika Unjani) KOMPUTIKA - Jurnal Sistem Komputer Jurnal Manajemen Informatika Jurnal Sistem Cerdas Jurnal Tekno Kompak MULTINETICS Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Dinasti International Journal of Education Management and Social Science Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika JATI (Jurnal Mahasiswa Teknik Informatika) Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS Journal of Computer System and Informatics (JoSYC) JIKA (Jurnal Informatika) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Journal of Research in Instructional Merpati JUSTIN (Jurnal Sistem dan Teknologi Informasi) JOMLAI: Journal of Machine Learning and Artificial Intelligence Competitive Jurnal Informatika: Jurnal Pengembangan IT Jurnal Ilmiah Sistem Informasi The Indonesian Journal of Computer Science
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Journal : IJISTECH

Sentiment Analysis of Student Emotion During Online Learning Using Recurrent Neural Networks (RNN) Nisa Hanum Harani; Cahyo Prianto
IJISTECH (International Journal of Information System and Technology) Vol 5, No 3 (2021): October
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v5i3.144

Abstract

There are many limitations in online learning process where communication effect student productivity, such as interpretation in the delivery of information can be different if it is in text form . The unstable internet network in some parts of Indonesia is also an obstacle in the learning process. Emotional factors are very influential on student motivation in learning, in online learning emotions can be read from textual dialogue in providing responses. We propose trainable model capable of identifying  the tendency of emotions / responses felt by students. With using natural language processing we can extract information and insights contained in conversations from WhatsApp, then organize them into their respective categories. The selection of the RNN algorithm can increase the accuracy by 75% in analyzing student emotions in online learning.
Sentiment Analysis of Covid-19 As A Social Media Pandemic Cahyo Prianto; Nisa Hanum Harani
IJISTECH (International Journal of Information System and Technology) Vol 4, No 1 (2020): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v4i1.90

Abstract

A large amount of information about Covid-19 that spreads quickly can lead to a perception of opinion and sentiment for those who read it. This research studies how text networking is formed, sentiment analysis and topics modelling that is widely discussed related to the Covid-19 theme. The text networking analysis was carried out on data taken from 4 different times, namely on 26 March, 29 March, 28 June and 23 July 2020 giving the result that the largest edge, nodes and modularity were in the conversation data on July 23, 2020. Sentiment analysis shows how the public responds to the Covid-19 pandemic. Sentiment analysis from tweet data in March 2020 showed 51% as positive sentiment and 49% as negative sentiment, with an accuracy rate of 0.7586, specificity 0.6667, prevalence 0.5862. Then tweet data in June 2020 showed 59% as negative sentiment and 41% as positive sentiment, with an accuracy rate of 0.6486, specificity 0.6111, prevalence 0.5135. Analysis of topic modelling has succeeded in collecting words related to certain topics, such as the data on March 26, 2020, representing talks related to the topic of "doing activities from home", "health", and "government policy". The data on March 29, 2020, represent talks related to the topic of "activities from home", "expression of feelings", "new habits". The data on June 28, 2020, represent talks related to the topic of "health protocol", "social assistance", "health". And on July 23, 2020 data represents talks related to the topic of "data security", "fine policy", and "policy".
The Covid-19 Chatbot Application Using A Natural Language Processing Approach Cahyo Prianto; Nisa Hanum Harani
IJISTECH (International Journal of Information System and Technology) Vol 5, No 2 (2021): August
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v5i2.133

Abstract

Cases exposed to the Covid-19 virus in Indonesia until June 2021 continue to experience a spike in increases, to handle it, various government policies continue to be rolled out and the public needs to be given correct, precise and fast information so that mutual awareness can be built to suppress cases exposed to COVID-19. With this background, this study aims to design and build a COVID-19 chatbot system based on artificial intelligence based on the Natural Language Processing algorithm. This chatbot is expected to be a place to ask questions about all things related to covid-19 so that it can become a personal assistant with two-way communication that can be accessed quickly for 24 hours. This chatbot system was built using the Python programming language, Node.js server and MariaDB as the database. As a client, this chatbot is integrated with the popular instant messaging application in Indonesia, namely WhatsApp. The data set used to train the chatbot was 369 question data and spread into 46 question tags. Testing the chatbot system using blackbox testing, and to test the expected output, the chatbot was tested using 350 testing data and the accuracy rate of the chatbot in answering reached 54%.
Co-Authors Adiningrum, Nur Tri Ramadhanti Adiningrum, Nur Tri Ramadhanti Alfadian Owen Amalia, Fahriza Rizky Aminuyati Andarsyah, Roni Andi Tenri Wali Andri Fajar Sunandhar Arjun Yuda Firwanda Azzahra, Fedhira Syaila Putri Burhanudin Zuhri Dellavianti Nishfi Ilmiah Huda Dian Markuci Fahira Fahira Fedhira Fikri Aldi Nugraha Firwanda, Arjun Yuda Habib Abdul Rasyid Hanna Theresia Siregar Hanum, Nisa Harani, Nisa Hanum Harun Ar-Rasyid Helmi Azhar Hutabarat, Rizkyria Angelina Pandapotan Ilyas Tri Khaqiqi, M Indra Firmansyah Kamaluddin, Rendy Kezia Tirza Naramessakh Kezia Tirza Naramessakh Kishendrian, Hanan M Ilyas Tri Khaqiqi Mariana Rospilinda Siki Markuci, Dian Mohamad Nurkamal Fauzan Mubassiran Mubassiran, Mubassiran Muh Kusnadi Muhammad Ibnu Choldun Muhammad Nazhim Maulana Muhammad Rifqi Daffa Ulhaq Muhammad Yusril Helmi Setyawan Muhammad Yusuf, Hadi Nawaf Naofal Nico Ekklesia Sembiring Nisa Hanum Nisa Hanum Harani Nisa Hanum Harani Nisa Hanum Harani Nisa Hanum Harani Nisa Hanum Harani Nisa Hanum Harani Nisa Hanum Harani Nurkamal Fauzan, Mohamad Nurul Izza Hamka Nurul Izza Hamka Nurul Lutfiasih Oktaviami Manullang Oktaviami Manullang Pertiwi, Aryka Anisa Rahayu, Woro Isti Rd Nuraini Rd.Nuraeni Siti Fatonah Riza, Noviana Rolly Maulana Awangga Rolly Maulana Awangga, Rolly Maulana Roni Andarsyah Roni Andarsyah Roni Andarsyah Roni Andarsyah Rukmi Juwita Setiadi, Hilman Setyawan, Muhammad Yusril Helmi Shinta Amelia Shinta Amelia Sulaksono, Al Novianti Ramadhani Supriady, Supriady Syafrial Fachri Pane Syafrial Fachri Pane Syafrial Fachri Pane, Syafrial Fachri Syahra, anita alfi Vegita, Yola Zian Asti Dwiyanti Zuhri, Burhanudin