Effect of Random Splitting and Cross Validation for Indonesian Opinion Mining using Machine Learning Approach

Opinion mining has been a prominent topic of research in Indonesia, however there are still many unanswered questions. The majority of past research has been on machine learning methods and models. A comparison of the effects of random splitting and cross-validation on processing performance is required. Text data is in Indonesian. The goal of this project is to use a machine learning model to conduct opinion mining on Indonesian text data using a random splitting and cross validation approach. This research consists of five stages: data collection, pre-processing, feature extraction, training & testing, and evaluation. Based on the experimental results, the TF-IDF feature is better than the Count-Vectorizer (CV) for Indonesian text. The best accuracy results are obtained by using TF-IDF as a feature and Support Vector Machine (SVM) as a classifier with cross validation implementation. The best accuracy reaches 81%. From the experimental results, it can also be seen that the implementation of cross validation can improve accuracy compared to the implementation of random splitting.

@article{Purba2022,
  title     = {Effect of Random Splitting and Cross Validation for Indonesian Opinion Mining using Machine Learning Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {9},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Mariana Purba and Ermatita Ermatita and Abdiansah Abdiansah and Handrie Noprisson and Vina Ayumi and Hadiguna Setiawan and Umniy Salamah and Yadi Yadi},
  doi       = {10.14569/IJACSA.2022.0130917},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130917}
}