Jurnal Stekom

  • 05 Nov
  • 2022

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
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




Goodreads, “GoodRead
. https://www.goodreads.com/ (accessed Oct. 20, 2020).
M. Thelwall and K. Kousha, “Goodreads: A social network site for book readers,”
J. Assoc. Inf. Sci.
, vol. 68, no. 4, pp. 972
983, 2017, doi:
M. A. Ghani and A. Subekti, “Email Spam Filtering Dengan Algoritma Random Forest,”
(Indonesian J. Comput. Inf. Technol.
, vol. Vol.3, No., no. 2, p. 216~221, 2018.
A. Franseda, “Integrasi Metode Decision Tree dan SMOTE untuk
Klasifikasi Data Kecelakaan Lalu
Lintas Integration of Decision Tree and SMOTE Methods for Classification of Traffic Accidents
Data,” vol. 08, no. 3, 2020, doi: 10.26418/justin.v8i3.40982.
A. M. Maghari, I. A. Al
najjar, S. J. Al
laqtah, and S. S. Abu
naser, “Books ’ Rating Prediction Using
Just Neural Network,” vol. 4, no. 10, pp. 17
22, 2020.
R. A. Tyas, M. Anggraini, I. A. Sulasiyah, and Q. Aini, “Implementasi Algoritma Naïve Bayes Dalam
Penentuan Rating Buku,”
, vol. 9, no. 3, p. 557,
2020, doi: 10.32520/stmsi.v9i3.915.
A. Suryanto, I. Alfarobi, and T. A. Tutupoly, “Komparasi Algoritma C4.5, Naive Bayes Dan Random
Forest Untuk Klasifikasi Data Kelulusan Mahasiswa Jakarta,”
Mitra dan Teknol. Pendidik.
, vol. iv
nomor 1, pp. 2
14, 2018
, [Online]. Available: https://www.publikasiilmiah.com/jurnal
https://www.kaggle.com/jealousleopard/goodreadsbooks (accessed Oct. 15, 2020
F. Teknik and U. M. Semarang, “Deteksi Penyakit Algoritma ID3 Gagal Ginjal Kronis
Menggunakan,” vol. 13, no. 1, pp. 8
17, 2020.
T. A. Tutupoly and I. Alfarobi, “Jurnal Mitra Pendidikan ( JMP Online ),”
J. Mitra Pendidik.
, vol. 3,
no. 1, pp. 11
103, 2019.
M. Lestandy, L. Syafa’ah, and A. Faruq, “Classification of potential blood donors using machine
learning algorithms approach,”
J. Teknol. dan Sist. Komput.
, vol. 8, no. 3, pp. 217
221, 2020, doi: