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  • 标题:Penggunaan Naïve Bayes Classifier Untuk Pengelompokan Pesan Pada Ruang Percakapan Maya Dalam Lingkungan Kemahasiswaan
  • 本地全文:下载
  • 作者:Muhammad Saiful Islam ; M. Imam Fauzan P. P. N ; Muhammad Taufiq Prata
  • 期刊名称:Jurnal Computech & Bisnis
  • 印刷版ISSN:1978-9629
  • 电子版ISSN:2442-4943
  • 出版年度:2017
  • 卷号:11
  • 期号:2
  • 页码:87-97
  • 语种:Indonesian
  • 出版社:E Jurnal LPPM STMIK Mardira Indonesia, Bandung
  • 摘要:Group chat feature provided by instant messaging service like LINE or WhatsApp in student environment has high importance, but not all of the conversation discussed were relevant with the mission of the group itself. With certain reasons, when lot of messages are unread, unaware users will see the first chats displayed on the screen, and will ignore or clear the rest. Thus, it is common for important messages to be ignored this way if they were placed on the middle of the unread chats. That stack of messages could be classified automatically, so users could get the information about the categories of the messages. This research test Naïve Bayes Classifier usage to classify messages in student usage environment, and we show that with good classification model, Naïve Bayes classifier performs well with F-measure score reaching 90,57% for one of the categories.
  • 关键词:Group chat feature provided by instant messaging service like LINE or WhatsApp in student environment has high importance; but not all of the conversation discussed were relevant with the mission of the group itself; With certain reasons; when lot of messages are unread; unaware users will see the first chats displayed on the screen; and will ignore or clear the rest; Thus; it is common for important messages to be ignored this way if they were placed on the middle of the unread chats; That stack of messages could be classified automatically; so users could get the information about the categories of the messages; This research test Naïve Bayes Classifier usage to classify messages in student usage environment; and we show that with good classification model; Naïve Bayes classifier performs well with F-measure score reaching 90;57% for one of the categories
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