KETEPATAN KLASIFIKASI STATUS KERJA DI KOTA TEGAL MENGGUNAKAN ALGORITMA C4.5 DAN FUZZY K-NEAREST NEIGHBOR IN EVERY CLASS (FK-NNC)

Atika Elsadining Tyas, Dwi Ispriyanti, Sudarno Sudarno

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


Keywords


Classification, C4.5 Algorithm, Fuzzy K-Nearest Neighbor in every Class ­(FK-NNC), APER

Full Text:

PDF

Refbacks

  • There are currently no refbacks.


Copyright (c)




Creative Commons License
Jurnal Gaussian by Departemen Statistika Undip is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Flag Counter