Automatic Essay Scoring for Indonesian Language with K-Nearest Neighbor Algorithm

This application created for experiment for final project on Politeknik Negeri Bandung 2017. This final project focus on compare two method for term weighting:

1. Unsupervised Term Weighting : Term Frequency - Inverse Document Frequency (TF-IDF)
2. Supervised Term Weighting : Term Frequency - Chi Square (TF-Chi²) Term Frequency - Relevance Frequency (TF-RF)

This application use Multiple K-Fold Cross Validation for estimating accuracy of Automatic Scoring on Essay, with value of K is 2 - 10.

Accuracy obtained for comparing score given by human rater and score given by application.

This project using K-Nearest Neighbor as Classification Algorithm, with K of KNN value is 1, 3, 5, 7, and 9