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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Pekommas Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi dan Sistem Komputer Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat IJID (International Journal on Informatics for Development) Jurnal Penelitian Pers dan Komunikasi Pembangunan JISKa (Jurnal Informatika Sunan Kalijaga) Pilar Teknologi : Jurnal Penelitian Ilmu-ilmu Teknik International Journal of Health Science and Technology Teknomatika: Jurnal Informatika dan Komputer
Adri Priadana
Universitas Jenderal Achmad Yani Yogyakarta
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Nursing Public Health

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Journal : IJID (International Journal on Informatics for Development)

 1 
Sentiment Analysis and Topic Modeling of Indonesian Public Conversation about COVID-19 Epidemics on Twitter Habibi, Muhammad; Priadana, Adri; Rifqi Ma’arif, Muhammad
IJID (International Journal on Informatics for Development) Vol. 10 No. 1 (2021): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2021.2400

Abstract

The World Health Organization (WHO) declared the COVID-19 outbreak has resulted in more than six million confirmed cases and more than 371,000 deaths globally on June 1, 2020. The incident sparked a flood of scientific research to help society deal with the virus, both inside and outside the medical domain. Research related to public health analysis and public conversations about the spread of COVID-19 on social media is one of the highlights of researchers in the world. People can analyze information from social media as supporting data about public health. Analyzing public conversations will help the relevant authorities understand public opinion and information gaps between them and the public, helping them develop appropriate emergency response strategies to address existing problems in the community during the pandemic and provide information on the population's emotions in different contexts. However, research related to the analysis of public health and public conversations was so far conducted only through supervised analysis of textual data. In this study, we aim to analyze specifically the sentiment and topic modeling of Indonesian public conversations about the COVID-19 on Twitter using the NLP technique. We applied some methods to analyze the sentiment to obtain the best classification method. In this study, the topic modeling was carried out unsupervised using Latent Dirichlet Allocation (LDA). The results of this study reveal that the most frequently discussed topic related to the COVID-19 pandemic is economic issues.
Implementation of Web Scraping to Build a Web-Based Instagram Account Data Downloader Application Himawan, Arif; Priadana, Adri; Murdiyanto, Aris
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09201

Abstract

Instagram has been used by many groups, such as business people, academics, to politicians, to take advantage of the insights gained by processing and analyzing Instagram data for various purposes. However, before processing and analyzing data, users must first pass data collection or downloading from Instagram. The problem faced is that most data collection methods are still done manually as for many parties that offer Instagram account data download services with various price options. This research applied a web scraping method to automatically build a web-based Instagram account data download application so that several parties can use it. The web scraping method was chosen because by using this method, researchers do not need to use Instagram's Application Programming Interface (API), which has access restrictions in retrieving data on Instagram. In this study, application testing was conducted on 15 Instagram accounts with various publications, namely between 100 and 11000. Based on the download data analysis results, the application of the web scraping method to download Instagram account data can successfully download a maximum of 2412 account data. In this application, users can download Instagram account data to Data Collection and then manage it like deleting and exporting data collection in the form of CSV, Excel, or JSON.
Online Integrated Development Environment (IDE) in Supporting Computer Programming Learning Process during COVID-19 Pandemic: A Comparative Analysis Kusumaningtyas, Kartikadyota; Nugroho, Eko Dwi; Priadana, Adri
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09202

Abstract

COVID-19 has spread to various countries and affected many sectors, including education. New challenges arise in universities with study programs related to computer programming, which require a lot of practice. Difficulties encountered when students should setting up the environment needed to carry out programming practices. Furthermore, they should install a text editor called Integrated Development Environment (IDE) to support it. There is various online IDE that supports computer programming. However, students must have an internet connection to use it. After all, many students cannot afford to buy internet quotas to access online learning material during the COVID-19 pandemic. According to these problems, this study compares several online IDEs based on internet data usage and the necessary supporting libraries' availability. In this study, we only compared eleven online IDEs that support the Python programming language, free to access, and do not require logging in. Based on the comparative analysis, three online IDEs have most libraries supported. They are REPL.IT, CODECHEF, and IDEONE. Based on internet data usage, REPL.IT is an online IDE that requires the least transferred data. Moreover, this online IDE also has a user-friendly interface to place the left and right sides' code and output positions. It prevents the user from scrolling to see the results of the code that has been executed. The absence of advertisements also makes this online IDE a more focused appearance. Therefore, REPL.IT is highly recommended for users who have a limited internet quota, primarily to support the learning phase of computer programming during the COVID-19 pandemic.
Co-Authors Aditama, Candra Ahmad Irfan Abdullah Al Badawi, M. Abu Amar Arif Himawan, Arif Aris Wahyu Murdiyanto Bayu Saputra, Andika Da Costa, Jonia Nova Dwi Sandi Yulianto Eko Dwi Nugroho Eko Dwi Nugroho Fajar Syahruddin Ferry Wahyu Wibowo Jonia Nova Da Costa Kusumaningtyas, Kartikadyota M. Abu Amar Al Badawi Moch. Adji Prasetyo Muhajir Muhajir Muhammad Habibi Muhammad Rifqi Ma'arif Murdiyanto, Aris Pradnya Dhuhita, Windha Mega Prasetyo, Moch. Adji Puji Winar Cahyo Rendi Virgian Fajaryantoro Rifqi Ma’arif, Muhammad Rizal, Ahmad Ashril Saputra, Andika Bayu Syahrir Nawir Nur Syahruddin, Fajar Triharseno, Wejo