APPLICATION OF PC GAME RECOMMENDATION ENCYCLOPEDIA WITH SAW METHOD BASED ON MOBILE ANDROID

ADNAN, RENALDI FAUZI (2024) APPLICATION OF PC GAME RECOMMENDATION ENCYCLOPEDIA WITH SAW METHOD BASED ON MOBILE ANDROID. Tugas Akhir thesis, Informatics.

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Abstract

ABSTRACT In the contemporary digital landscape, access to precise and comprehensive information is crucial, particularly for gamers seeking to enhance their gaming experience. The traditional manual process of obtaining such information can often be convoluted and susceptible to errors, especially for individuals with limited technical expertise. The proposed mobile application addresses this issue by allowing users to input their computer credentials and select their desired game for compatibility matching. This application is conceptualized as a comprehensive encyclopedia for gamers, offering detailed, accurate, and prompt information regarding various games. Given the diversity of personal computer configurations, it is important to note that not all games are compatible with every system. This application mitigates this challenge by recommending games optimally suited to the user’s PC settings. Furthermore, considering the high cost associated with original games, players must ascertain the compatibility of a game with their PC prior to purchase. The application is developed utilizing the Kotlin programming language and employs MySQL as its database, ensuring high performance and reliable data processing capabilities. It incorporates the Simple Additive Weighting (SAW) method to deliver swift and accurate game recommendations tailored to the specifications of each PC. Designed with user-friendliness and engagement in mind, this program empowers players to comprehend and utilize the PC game comparison system, thereby facilitating informed decision-making regarding their game purchases. A study was conducted to evaluate the effectiveness of this application, revealing that it can provide detailed guidance on the hardware components of the user’s computer. The study's findings indicate that the application achieves an accuracy rate of 85%. Keywords: Encyclopedia, Information, Android, SAW Method, Recommendation System.

Item Type: Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir)
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Sains Dan Teknologi > S1 Informatika
Depositing User: Kaprodi S1 Informatika UTY
Date Deposited: 05 Dec 2024 02:03
Last Modified: 05 Dec 2024 02:03
URI: http://eprints.uty.ac.id/id/eprint/17074

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