Penerapan K-Nearest Neighbor Menggunakan Pendekatan Random Walk Over-Sampling Menangani Ketidakseimbangan Kelas Pada Prediksi Cacat Software

Penerapan K-Nearest Neighbor Menggunakan Pendekatan Random Walk Over-Sampling Menangani Ketidakseimbangan Kelas Pada Prediksi Cacat Software

• By Anna;
• Tesis
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Currently the high quality of software is more increasing. The quality of a software is usually measured by the number of defects in the product. To find and fix software defects at the testing stage is expensive and time consuming. However, until now there is no predictive model of software defect that is generally accepted because almost all classification algorithms show very poor performance when working on data with an unbalanced class. The K-Nearest Neighbor (k-NN) method is one of the most popular and widely applied methods for building predictive Software defect models but is not equipped with the ability to handle class imbalance issues resulting in low accuracy. This study aims to apply the resampling method of Random Walk Over-Sampling (RWO-S) to overcome the problem which will be compared with Random Over Sampling (ROS). The experiments performed were to compare the results obtained were k-NN without resampling and k-NN integrated resampling (ROS and RWO-S). The results shown that the application of resampling will increase the value of accuracy, sensitivity, precision, and the value of AUC to k-NN. The proposed model in this study, RWO-S and k-NN is better than the other models result.

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