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Angwarmasse, Thanel Richard
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Computer Science & IT Engineering

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COUNTRYINFO APP DEVELOPMENT TO FACILITATE ACCESS TO ANDROID-BASED COUNTRY INFORMATION Angwarmasse, Thanel Richard; Mingga Juluk, Mariano Antonino; Rafiola, Muchamad Rachel; Misni, Misni
Journal Collabits Vol 2, No 2 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i2.31258

Abstract

In the era of globalization and technological advancement, the need for fast, accurate, and easily accessible information has significantly increased. The CountryInfo App was developed as an innovative solution to simplify access to real-time country information. This application offers two main features: searching for country details by name and a randomizer feature for exploring country data interactively. Utilizing technologies such as Kotlin for user interface development, Flask for backend management, and public APIs for retrieving real-time data in JSON format, the application is designed to provide users with an intuitive and interactive experience. Testing results demonstrate a responsive performance, with an average access time of less than 2 seconds. This study aims to provide practical benefits to society in education, research, and personal needs while serving as a case study for API technology implementation in mobile application development.
Implementation of The Naive Bayes Algorithm on Online Game Addiction and Its Impact on Students Akbar, Shafrizal Fadillah; Angwarmasse, Thanel Richard
Journal Collabits Vol 1, No 3 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i3.27301

Abstract

This research aims to implement the Naive Bayes algorithm in analyzing online game addiction and its impact on individuals. Online gaming addiction has become a global phenomenon with significant psychological, social, and academic implications. For this reason, an effective analytical tool is needed to identify the factors that contribute to this addiction and its impact. The Naive Bayes algorithm was chosen because of its ability to carry out classification based on probability, which is very suitable for handling complex and diverse data. This research collects data from questionnaires that cover demographic aspects, frequency of play, duration of play, and perceived impact. The analysis results show that the Naive Bayes algorithm has quite high accuracy in classifying individuals who are addicted to online games. In addition, this study identified several key factors that are closely related to addiction, such as age, gender, and motivation to play. The most prominent impacts of this addiction include decreased academic performance, disturbed sleep patterns, and problems with social relationships. With the implementation of the Naive Bayes algorithm, it is hoped that it can contribute to prevention and early intervention efforts against online game addiction. This research also opens up opportunities for further development in the use of other machine-learning techniques for digital behavior analysis.
Co-Authors Akbar, Shafrizal Fadillah Mingga Juluk, Mariano Antonino Misni, Misni Rafiola, Muchamad Rachel