Repository logo
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Српски
  • Yкраї́нська
  • Log In
    New user? Click here to register. Have you forgotten your password?
  • Communities & Collections
  • All of DSpace
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Српски
  • Yкраї́нська
  • Log In
    New user? Click here to register. Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Rochyadi, Fachri Romi"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • No Thumbnail Available
    Item
    KLASIFIKASI ARTEFAK PADA GAME GENSHIN IMPACT MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER
    (Elfitra, 2023-01) Rochyadi, Fachri Romi; Mahdiyah, Evfi
    One type of entertainment that people often do is playing games. Genshin Impact is a very popular game. In this game, there are many characters that can be obtained during the game. Players need to strengthen their character to defeat enemies during adventures and complete missions. One way to strengthen the character is to use artifacts. There are so many artifacts in Genshin Impact that each artifact has a random value that is confusing for new players. This study aims to classify artifacts using the Naive Bayes Classifier method. This Naive Bayes Classifier algorithm model uses a dataset of Gladiator and Wanderer artifacts of 349 data, 279 data as training data and 70 data as testing data. The data attributes used are 8 attributes, the name of the artifact set, the type of artifact, the type of main stat, sub stat 1, sub stat 2, sub stat 3, and sub stat 4. This study resulted in a classification model with an accuracy of 87.1% measured using the Confusion Matrix

DSpace software copyright © 2002-2025 LYRASIS

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback