Abstract:
Nowadays, it is difficult for us to imagine a life without devices that is
controlled by software. Software quality has become the main concern during the
software development. Software quality is a field of study and practice that describes
the desirable attributes of software products. Software quality prediction is a process
of utilizing software metrics such as code-level measurements and defect data to build
classification models that are able to estimate the quality of program modules. The
major problem that affects the quality of datasets is high dimensionality and class
imbalanced. A more useful and efficient mechanism is k Nearest Neighbor method,
where Nearest Neighbor classify classes of testing dataset based on k nearest neighbor
of training dataset. Another mechanism is Class Based Weighted k Nearest Neighbor
with BINER Algorithm for classifying classes of testing dataset. By using BINER
Algorithm, it narrows down the training dataset range instead of whole training
dataset that has the maximum likelihood of occurrence and then CBW k-NN classifies
classes of testing dataset based on this range. This thesis is the comparison of two
classification methods by classifying classes of testing dataset focuses on NASA
MDP (PC1, CM1 and JM1) datasets. The comparison results of two methods based on
Accuracy, Reliability, Mean Absolute Error and Root Mean Squared Error.
