Klasifikasi Konten Pengaduan Pada Website BAKOHUMAS (Badan Koordinasi Hubungan Masyarakat) Dengan Metode Naive Bayes Classifier

Rois Yanuar Rahman Wahyudi (Fakultas Ilmu Komputer, Universitas Brawijaya)
Nanang Yudi Setiawan (Fakultas Ilmu Komputer, Universitas Brawijaya)
Fitra Abdurrachmad Bachtiar (Fakultas Ilmu Komputer, Universitas Brawijaya)

Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Vol 2 No 10 (2018): Oktober 2018

Website BAKOHUMAS KOMINFO east java is a website that serves as an intermediary for community with government agencies. Where on BAKOHUMAS website there are feature such as news, there is also sharing events that will be held by government agencies. In addition there are also feature for community to be able convey complaints against the performance of a government agency. Complaints from these communities will be distributed by KOMINFO to the responsible agencies. However, the admin must check wether the intended agency already has a match with the content of the complaint. KOMINFO need system to classify complaint content automatically so KOMINFO can save time in delivering community complaints to the appropriate agencies. The method that can be used to build this system is text mining. One method of text mining is the naive bayes classifier. Naive bayes classifier will calculate the weight of words from previously entered data train. The feasibility of this method is tested with ROC to find out the precision, recall, and F-measure of the system classification. Testing result are precision 80,5%, recall 80%, and F-measure 79,9%.