PENERAPAN ALGORITMA RANDOM UNDER DAN OVER SAMPLING UNTUK MENGATASI CLASS IMBALANCE DALAM KLASIFIKASI TOPIK FORUM

Nawwab Zia Ajnaden, NIM.: 18106050042 (2023) PENERAPAN ALGORITMA RANDOM UNDER DAN OVER SAMPLING UNTUK MENGATASI CLASS IMBALANCE DALAM KLASIFIKASI TOPIK FORUM. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

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).

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