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All Journal IJID (International Journal on Informatics for Development) Teknomatika: Jurnal Informatika dan Komputer
Rifqi Ma’arif, Muhammad
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Computer Science & IT

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Aplikasi Android Pencarian Coffee Shop Terbaik Menggunakan Metode Weighted Product Hasbiyah, Nina; Rifqi Ma’arif, Muhammad; Bayu Saputra, Andika; Alfi Sa'diya, Nafisa
Jurnal Teknomatika Vol 14 No 1 (2021): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v14i1.1112

Abstract

Coffee Shop is a place to gather and relax with family, friends, relatives to enjoy the weekend or just to unwind from the activities that have been carried out. Currently, there are many coffee shops in Sleman that provide products at affordable prices and comfortable places, so that many students make coffee shops an alternative place to complete assignments. However, many people still ask about the Coffee Shop along with detailed information such as taste, price, service, atmosphere, and distance. The Weighted Product method is one of the weighting methods, where multiplication is used to connect attribute ratings, and the rating of each attribute must be raised first with the weight of the attribute in question. The results of this study are in the form of an Android application to find the best Coffee Shop using the Weighted Product method so that it can make it easier for people to choose a Coffee Shop based on the selected criteria. The application is built using the Dart programming language and utilizes the Flutter framework and MySQL as a database management system.
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.
Co-Authors Adri Priadana Alfi Sa'diya, Nafisa Bayu Saputra, Andika Hasbiyah, Nina Muhammad Habibi