Pemanfaatan Algoritma WIT-Tree dan HITS untuk Klasifikasi Tingkat Keberhasilan Pemberdayaan Keluarga Miskin
Abstract: The successful rate
of the poor families empowerment can be classified by characteristic patterns
extracted from the database that contains the data of the poor families
empowerment. The purpose of this research is to build a classification model to
predict the level of success from poor families, who will receive assistance
empowerment of poverty. Classification
models built with WARM, which is combining two methods, they are HITS and
WIT-tree. HITS is used to obtained the weight of the attributes from the
database. The weights are used as the attributes’s weight on methods WIT-tree.
WIT-tree is used to generate the association rules that satisfy a minimum
weight support and minimum weight confidence. The data used was 831 sample data
poor families that divided into two classes, namely poor families in the
standard of "developing" and poor families in the level of "underdeveloped".
The performance of classification model shows, weighting attribute using
HITS approaches the accuracy of 86.45% and weighted attributes defined by the
user approaches the accuracy of 66.13%. This study shows that the weight of the
attributes obtained from HITS is better than the weight of the attributes
specified by the user.
Keywords: poverity reduction,
Association Rule Classifier, Weighted Asociation Rule Classifier, WIT-tree,
HITS
Penulis: Siti Khomsah
Kode Jurnal: jptinformatikadd170103
