Perbaikan Prediksi Kesalahan Perangkat Lunak Menggunakan Seleksi Fitur dan Cluster-Based Classification
Abstract: High balance value
of software fault prediction can help in conducting test effort, saving test
costs, saving test resources, and improving software quality. Balance values in
software fault prediction need to be considered, as in most cases, the class
distribution of true and false in the software fault data set tends to be
unbalanced. The balance value is obtained from trade-off between probability
detection (pd) and probability false alarm (pf). Previous researchers had
proposed Cluster-Based Classification (CBC) method which was integrated with
Entropy-Based Discretization (EBD). However, predictive models with irrelevant
and redundant features in data sets can decrease balance value. This study
proposes improvement of software fault prediction outcomes on CBC by integrating
feature selection methods. Some feature selection methods are integrated with
CBC, i.e. Information Gain (IG), Gain Ration (GR), One-R (OR), Relief-F (RFF),
and Symmetric Uncertainty (SU). The result shows that combination of CBC with
IG gives best average balance value, compared to other feature selection
methods used in this research. Using five NASA public MDP data sets, the
combination of IG and CBC generates 63.91% average of balance, while CBC method
without feature selection produce 54.79% average of balance. It shows that IG
can increase CBC balance average by 9.12%.
Keywords: Cluster-based
Classification, Entropy-based Discretization, kesalahan perangkat lunak,
seleksi fitur
Penulis: Fachrul Pralienka
Bani Muhamad, Daniel Oranova Siahaan, Chastine Fatichah
Kode Jurnal: jptlisetrodd170489
