BibTex Citation Data :
@article{J.Gauss10127, author = {Atika Tyas and Dwi Ispriyanti and Sudarno Sudarno}, title = {KETEPATAN KLASIFIKASI STATUS KERJA DI KOTA TEGAL MENGGUNAKAN ALGORITMA C4.5 DAN FUZZY K-NEAREST NEIGHBOR IN EVERY CLASS (FK-NNC)}, journal = {Jurnal Gaussian}, volume = {4}, number = {4}, year = {2015}, keywords = {Classification, C4.5 Algorithm, Fuzzy K-Nearest Neighbor in every Class (FK-NNC), APER}, abstract = { Unemployment is a very crucial problem that always deal a developing country and affected a national foundation. It used two methods for classifying a employment status on productive society in Tegal City on August 2014, the methods are C4.5 Algorithm and Fuzzy K-Nearest Neighbor in every Class (FK-NNC). C4.5 Algorithm is a way of classifying methods from data mining that use to construct a decision tree. FK-NNC is another classification technique that predict using the amount of closest neighbor of K in every class from a testing data. The predictor variables that used on classifying an employment status are neighborhood status, sex, age, marriage status, education, and a work training. To evaluate the result of classification use APER calculation. Based on this analysis, classification of employment status using C4.5 Algorithm obtained APER = 28,3784% and 71,6216% of accuracy, while FK-NNC methods obtained APER = 21,62% and 78,38% of accuracy. So, it can be concluded that FK-NNC is better than C4.5 Algorithm. Keywords: Classification, C4.5 Algorithm, Fuzzy K-Nearest Neighbor in every Class (FK-NNC), APER }, issn = {2339-2541}, pages = {735--744} doi = {10.14710/j.gauss.4.4.735-744}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/10127} }
Refworks Citation Data :
Unemployment is a very crucial problem that always deal a developing country and affected a national foundation. It used two methods for classifying a employment status on productive society in Tegal City on August 2014, the methods are C4.5 Algorithm and Fuzzy K-Nearest Neighbor in every Class (FK-NNC). C4.5 Algorithm is a way of classifying methods from data mining that use to construct a decision tree. FK-NNC is another classification technique that predict using the amount of closest neighbor of K in every class from a testing data. The predictor variables that used on classifying an employment status are neighborhood status, sex, age, marriage status, education, and a work training. To evaluate the result of classification use APER calculation. Based on this analysis, classification of employment status using C4.5 Algorithm obtained APER = 28,3784% and 71,6216% of accuracy, while FK-NNC methods obtained APER = 21,62% and 78,38% of accuracy. So, it can be concluded that FK-NNC is better than C4.5 Algorithm.
Keywords: Classification, C4.5 Algorithm, Fuzzy K-Nearest Neighbor in every Class (FK-NNC), APER
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