Berbagai website dengan hasil review dan ulasan memudahkan kita dalam memenentukan
sebuah keputusan. Namun keputusan tersebut belumlah maksimal dan akurat. Seperti review
makanan di yelp.com. Pengguna cukup banyak melihat review pada web tersebut sebelum
memutuskan untuk memesan makanan. Permasalahan disini adalah jika hasil review terbukti
kurang objektif, maka hasil keputusan menjadi tidak akurat. Tujuan dari penelitian ini untuk
membantu para pengguna review untuk menghasilkan sebuah keputusan yang optimal. Naïve
Bayes terbukti sebagai slah satu metode klasifikasi text yang menghasilkan akurasi tinggi.
Sedangkan Particle Swarm Optimization dikenal sebagai algoritma optimasi yang baik untuk
penyelesaian masalah berdasarkan parameter proses yang ada. Pada penelitan ini akan
digunakan metode Naïve Bayes yang dilakukan ujicoba menggunakan Particle Swarm
Optimization untuk pembobotan sehingga hasil akurasinya lebih tinggi. Berdasarkan hasil
pengolahan data maka dihasilkan nilai akurasi Naïve Bayes sebesar 81.00% dan 83.80% adalah
hasil pengolahan akurasi untuk Naïve Bayes Berbasis Particle Swarm Optimization (PSO).
Kesimpulan pada percobaan metode Naïve Bayes tersebut yaitu bahwa PSO dapat
meningkatkan nilai optimasi dari sebuah algoritma Naïve Bayes sehingga mampu diterapkan
sebagai solusi untuk pemecahan masalah di atas.
Redaksional Jutim
[1] T. Kwartler, “What is Text
Mining?,” Text Min. Pract. with R,
pp.
1–15,
2017,
doi:
10.1002/9781119282105.ch1.
[2] R. Feldman and J. Sanger, The Text
Mining Handbook: Advanced
Approaches
in Analyzing
Unstructured Data. United States Of
America: Cambridge University
Press, 2007.
[3] H. Hashimi, A. Hafez, and H.
Mathkour, “Selection criteria for
text mining approaches,” Comput.
Human Behav., vol. 51, pp. 729–
733, Oct.
2015,
doi:
10.1016/j.chb.2014.10.062.
[4] M. Ghiassi, J. Skinner, and D.
Zimbra, “Twitter brand sentiment
analysis: A hybrid system using ngram
analysis
and
dynamic
artificial
neural
network,” Expert Syst. Appl.,
vol. 40, no. 16, pp. 6266–6282, Nov.
2013,
doi:
10.1016/j.eswa.2013.05.057.
[5]
I. Habernal, T. Ptáček, and J.
Steinberger, “Reprint of ‘Supervised
sentiment analysis in Czech social
media,’” Inf. Process. Manag., vol.
51, no. 4, pp. 532–546, Jul. 2015,
doi: 10.1016/j.ipm.2015.05.006.
[6] L. Zhang, K. Hua, H. Wang, G.
Qian, and L. Zhang, “Sentiment
Analysis on Reviews of Mobile
Users,” Procedia Comput. Sci., vol.
34, pp. 458–465, 2014, doi:
10.1016/j.procs.2014.07.013.
[7] H. Kang, S. J. Yoo, and D. Han,
“Senti-lexicon and improved Naïve
Bayes algorithms for sentiment
analysis of restaurant reviews,”
Expert Syst. Appl., vol. 39, no. 5, pp.
6000–6010, Apr. 2012, doi:
10.1016/j.eswa.2011.11.107.
[8] A. Bagheri, M. Saraee, and F. de
Jong, “Care more about customers:
Unsupervised domain-independent
aspect detection for sentiment
analysis of customer reviews,”
Knowledge-Based Syst., vol. 52, pp.
201–213, Nov. 2013, doi:
10.1016/j.knosys.2013.08.011.
[9] A. Hicks, S. Comp, J. Horovitz, M.
Hovarter, M. Miki, and J. L. Bevan,
“Why people use Yelp.com: An
exploration of uses
and
gratifications,” Comput. Human
Behav., vol. 28, no. 6, pp. 2274–
2279, Nov.
2012, doi:
10.1016/j.chb.2012.06.034.
[10] S. Tan, “Neighbor-weighted Knearest
neighbor
for
unbalanced
text
corpus,”
Expert
Syst.
Appl.,
vol.
28,
no.
4, pp. 667–671, May 2005, doi:
10.1016/j.eswa.2004.12.023.
[11] Z. Yao and C. Zhi-Min, “An
Optimized NBC Approach in Text
Classification,” Phys. Procedia, vol.
24, pp. 1910–1914, 2012, doi:
10.1016/j.phpro.2012.02.281.
[12] D. Farid, L. Zhang, C. Mofizur, M.
A. Hossain, and R. Strachan,
“Expert Systems with Applications
Hybrid decision tree and naïve
Bayes classifiers for multi-class
classification tasks,” Expert Syst.
Appl., vol. 41, no. 4, pp. 1937–1946,
2014,
doi:
10.1016/j.eswa.2013.08.089.
[13] H. Alshalabi, S. Tiun, N. Omar, and
M. Albared, “Experiments on the
Use of Feature Selection and
Machine Learning Methods in
Automatic
Malay
Text
Categorization,” Procedia Technol.,
vol. 11, no. Iceei, pp. 748–754,
2013,
doi:
10.1016/j.protcy.2013.12.254.
[14] Y. Zhang, S. Wang, P. Phillips, and
G. Ji, “Binary PSO with mutation
operator for feature selection using
decision tree applied to spam
detection,” Knowledge-Based Syst.,
vol. 64, pp. 22–31, Jul. 2014, doi:
10.1016/j.knosys.2014.03.015.
[15] J. Xiao, C. He, X. Jiang, and D. Liu,
“A dynamic classifier ensemble
selection approach for noise data,”
Inf. Sci. (Ny)., vol. 180, no. 18, pp.
3402–3421, Sep. 2010, doi:
10.1016/j.ins.2010.05.021.
[16] C. Kim, H. Li, S.-Y. Shin, and K.-B.
Hwang, “An Efficient and Effective
Wrapper based on Paired t-test for
Learning Naive Bayes Classifiers
from Large-scale Domains,”
Procedia Comput. Sci., vol. 23, pp.
102–112,
2013,
doi:
10.1016/j.procs.2013.10.014.
[17] G. Suresh kumar and G. Zayaraz,
“Concept relation extraction using
Naïve Bayes classifier for ontologybased
question answering systems,”
J. King Saud Univ. - Comput. Inf.
Sci., vol. 27, no. 1, pp. 13–24, Jan.
2015,
doi:
10.1016/j.jksuci.2014.03.001.
[18] P. Bermejo, J. a. Gámez, and J. M.
Puerta, “Speeding up incremental
wrapper feature subset selection
with Naive Bayes classifier,”
Knowledge-Based Syst., vol. 55, pp.
140–147,
Jan. 2014, doi:
10.1016/j.knosys.2013.10.016.
[19] R. Liu, Y. Chen, L. Jiao, and Y. Li,
“A particle swarm optimization
based
simultaneous
learning
framework for clustering and
classification,” Pattern Recognit.,
vol. 47, no. 6, pp. 2143–2152, 2014,
doi: 10.1016/j.patcog.2013.12.010.
[20] X. Bai, X. Gao, and B. Xue,
“Particle Swarm Optimization Based
Two-Stage Feature Selection in Text
Mining,” 2018 IEEE Congr. Evol.
Comput. CEC 2018 - Proc., pp. 1–8,
2018,
doi:
10.1109/CEC.2018.8477773.
[21] A. Idrus, H. Brawijaya, and
Maruloh, “Sentiment Analysis of
State Officials News on Online
Media Based on Public Opinion
Using Naive Bayes Classifier
Algorithm and Particle Swarm
Optimization,” 2018 6th Int. Conf.
Cyber IT Serv. Manag. CITSM
2018, no. Citsm, pp. 1–7, 2019, doi:
10.1109/CITSM.2018.8674331.
[22] A. Pandhu Wijaya and H. Agus
Santoso, “Improving the Accuracy
of Naïve Bayes Algorithm for Hoax
Classification Using Particle Swarm
Optimization,” Proc. - 2018 Int.
Semin. Appl. Technol. Inf. Commun.
Creat. Technol. Hum. Life,
iSemantic 2018, pp. 482–487, 2018,
doi:
10.1109/ISEMANTIC.2018.854970
0.