@inproceedings{ab4fe0f9b8014b67adc72427ad851dbd,
title = "Enhancing Software Defect Prediction Using Principle Component Analysis and Self-Organizing Map",
abstract = "Application of SDLC in software is needed to produce a quality software. One of the way to improve software quality is determined by checking and testing, to find defects in the software that can degrade software quality. Software defect datasets generally have problems with class imbalance and unrelated features that may cause performance degradation in learning algorithm. This research proposes the selection of Principle Component Analysis (PCA) feature to solve the problem of unrelated features, while to overcome the problem of class imbalance using Self Organizing Maps model (SOM) by compare some data classifications algorithm to find the optimum result. The experimental results in this study shows that Random Forest get the highest average of accuracy 96.87%, average of precision 96.88%, and average of recall 96.88%. For JMl dataset got the highest average of 96.06% accuracy, average of 96.44% Precision, and average of 96.06% Recall.",
keywords = "class imbalance, pea, software defect, som",
author = "Hadi, {Novi Trisman} and Siti Rochimah",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018 ; Conference date: 09-10-2018 Through 11-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/EECCIS.2018.8692889",
language = "English",
series = "2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "320--325",
booktitle = "2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018",
address = "United States",
}