%0 Thesis
%9 Skripsi
%A Nawwab Zia Ajnaden, NIM.: 18106050042
%B FAKULTAS SAINS DAN TEKNOLOGI
%D 2023
%F digilib:58350
%I UIN SUNAN KALIJAGA YOGYAKARTA
%K balanced accuracy; classification; class imbalance; resampling
%P 53
%T PENERAPAN ALGORITMA RANDOM UNDER DAN OVER  SAMPLING UNTUK MENGATASI CLASS IMBALANCE  DALAM KLASIFIKASI TOPIK FORUM
%U https://digilib.uin-suka.ac.id/id/eprint/58350/
%X Classification of user-generated content in social media applications  provides several opportunities to provide useful benefits to developers and users.  Factors such as limited time to create features and limited data sources at a certain  time can cause an imbalance in the number of classes in a particular label in the  dataset. Resampling techniques such as random over and under sampling are one  of the solutions to solving this problem.  This research compares three models (with the naive bayes classifier) in  classifying two datasets, namely, post data and combined data between posts and  comments, the three models are: models that are not subject to resampling,  models with Random Over Sampling (ROS), and models with Random Under  Sampling (RUS). All data has a total of 12 topic classes.  The results showed an increase in the balanced accuracy value in all models  equipped with resampling (Without resampling: 0.5792 and 0.5078, ROS: 0.6148  and 0.5570, RUS: 0.6040 and 0.5225 went to post data, and combined post and  comment data, respectively). Improvement occurred only in models trained with  post data on F1-score (Without resampling: 0.5780 and 0.5315, ROS: 0.6027 and  0.5260, RUS: 0.5754 and 0.4711 went to post data, and combined post and  comment data, respectively) and precision (without resampling: 0.5816 and  0.5774, ROS: 0.6027 and 0.5155, RUS: 0.5754 and 0.4653 went to post data, and  combined post and comment data, respectively). However, all models with  resampling improved recall values (without resampling: 0.5792 and 0.5078, ROS:  0.6148 and 0.5570, RUS: 0.6040 and 0.5225 went to post data, and combined post  and comment data, respectively) and correspond to an increase in the number of  predicted results in some minority classes (both true positives and false negatives  prediction).
%Z Pembimbing: Nurochman, S.Kom., M.Kom.