JURNAL NASIONAL TEKNIK ELEKTRO DAN TEKNOLOGI INFORMASI: Perbaikan Prediksi Kesalahan Perangkat Lunak Menggunakan Seleksi Fitur dan Cluster-Based Classification - Fachrul Pralienka Bani Muhamad, Daniel Oranova Siahaan, Chastine Fatichah (Teknik elektro UGM, 2017)
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