Peningkatan Performa Klasifikasi Machine Learning Melalui Perbandingan Metode Machine Learning dan Peningkatan Dataset

Fikri Baharuddin(1*), Aris Tjahyanto(2)

(1) Institut Teknologi Sepuluh Nopember
(2) Institut Teknologi Sepuluh Nopember
(*) Corresponding Author

Abstract


Classification using machine learning is an alternative that is widely used to classify data. There are various classification methods or also known as machine learning classification algorithms that can be used. However, to get the best classification results, we need a classifier that fits the dataset type to provide the best classification performance. In addition, the quality and quantity of data contained in a dataset also has an influence on the classification performance. In this study, several attempts were made to improve the classification performance of the dataset of Indonesian language exam questions at the elementary school level based on the category of difficulty level. The efforts made consist of improving the quality of the dataset and using the StringToWordVector filter algorithm to manage textual data, as well as the use of several classification algorithms such as the nave Bayes algorithm, Random Forest, and REPTree. Classification is done by using WEKA Tools. The results of the experiments carried out showed the highest performance increase of 15% after improving the quality of the dataset and using the right classification method.

Keywords


Classification; StringToWordVector; Machine Learning; Exam Classification

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DOI: https://doi.org/10.32736/sisfokom.v11i1.1337

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