Penerapan Algoritma Adaboost Untuk Peningkatan Kinerja Klasifikasi Data Mining Pada Imbalance Dataset Diabetes
DOI:
https://doi.org/10.30865/mib.v6i2.4017Keywords:
Improvement, Performance, Classification, Adaboost, Data MiningAbstract
According to the World Health Organization (WHO), it has been recorded that up to now more than 150 million people have diabetes, whether they are elderly people, adults, teenagers, men or women. Early knowledge of diabetes can be seen based on data from patients who already have diabetes. The patient's disease data has previously been stored and arranged in a data warehouse or what is commonly referred to as a dataset. Therefore, it is necessary to process the data contained in the dataset. But the use of data mining techniques themselves must be assisted by using the techniques contained in the data mining, namely classification techniques. K-Nearest Neighbor (K-NN) is one of the methods used in the classification technique. In the results of the classification of the level of confidence obtained in the process, it is seen based on the amount of accuracy. However, there are important issues that need special attention. In the dataset used for the classification process, the data collected contains unbalanced class results (balance). The unbalanced data classification process becomes an important problem, this is because it can cause a decrease in performance. Adaboost is a technique in data mining that can be used to increase the level of accuracy in classification methods. The results showed that the adaboost algorithm can help improve classification performance. This can be seen from the increasing level of accuracy obtained from the process carried out before and after using the adaboost algorithm. The results obtained from the research show that the adaboost algorithm can be used properly to help the performance of the K-Nearest Neighbor algorithm for the classification process on diabetes datasets. It can be seen from 5 tests with values of K = 7, 13, 19, 25 and 31 there is an increase in the accuracy results obtained after using the adaboost algorithm.References
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