Jurnal Stekom

With  the  increasing  development  of  technology  the 

more variety of 

books circulating on the internet. As 

is  the  recommendation  system  on  online  book  sites 

that  provide  books  relevantly  and  as  needed  with 

one's  preferences.  One  alternative  is  GoodReads,  a 

social  networking  site  that  specializes  in  cataloging 

books    and    user

s    can    share    reading    book 

recommendations    with    each    other    by    rating, 

reviewing,   and   commenting.   As   a   large   book 

recommendation site, it has a lot of data that can be 

processed by applying machine learning methods, but 

still not known as the most accurate mo

del. By using 

the  right  model,  we  can  provide  more  accurate 

recommendations. Therefore, this study  will analyze 

the  data obtained from  the  www.kaggle.com  namely 

the  goodreads

-

books dataset. This study  proposed a 

data mining classification model to get the 

best model 

in    recommending    books    on    GoodReads.    The 

algorithms   used   are   Decision   Tree,   K

-

Nearest 

Neighbor, Naïve Bayes, Random Forest, and Support 

Vector  Classifier,  then  for  model  evaluation  using 

accuracy,   precision,   recall,   f1

-

score,   confusion 

matrix,  AU

C,  and  Mean  Error  Absolute.  The  test 

results of several classification algorithms found that 

Decision  Tree  has  the  highest  accuracy  among  the 

methods  presented  by  99.95%,  precision  by  100%, 

recall by 96%, f1

-

score of 98% with MAE of 0.05 and 

AUC  of  99.96%.

This  is  proof  that  decision  tree 

algorithms  can  be  used  as  book  recommendations 

based on book categories on GoodReads

Unduhan

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[Online]

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