Authors:
Tantri Sinaga
1
;
Arie Rafika Dewi
1
;
Masdiana Sagala
2
;
Yulia Dalimunthe
3
and
Solikhun
4
Affiliations:
1
Universitas Harapan Medan, Medan, Indonesia
;
2
Universitas Katolik Santo Thomas, Medan, Indonesia
;
3
Politeknik Negeri Medan, Medan, Indonesia
;
4
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Keyword(s):
Hebbian Algorithm, Input and Output Patterns, Prediction of Lung Cancer.
Abstract:
The Hebbian algorithm learning method is a learning method that is carried out by fixing the weight values so that if there are 2 neurons connected and both are alive at the same time, the weight between the two is increased. This study’s main problem is finding the best performance of the Hebbian algorithm to predict lung cancer in smokers. There are 15 attributes to determine lung cancer, namely: gender(x1), age(x2), smoking(x3), yellow finger(x4), anxiety(x5), social pressure(x6), chronic diseases(x7), fatigue(x8), allergies(x9 ), wheezing(x10), consumption of alcohol(x11), cough (x12), shortness of Breath(x13), hard to swallow(x14) and chest Pain(x15). This study compares the Hebbian algorithm with four forms of test simulation with four states of input and output patterns. The test simulation results show the best accuracy is with binary data input patterns and binary and bipolar output patterns. The accuracy obtained is 65%.