DETEKSI CYBERBULLYING PADA DATA TWEET MENGGUNAKAN METODE RANDOM FOREST DAN SELEKSI FITUR INFORMATION GAIN

Rachmad Masbadi Hatullah Nurnaryo, Mulaab Mulaab, Ika Oktavia Suzanti, Doni Abdul Fatah, Andharini Dwi Cahyani, Fifin Ayu Mufarroha

Abstract


Indonesia merupakan salah satu negara dengan pengguna media sosial terbanyak. Dengan banyaknya pengguna media sosial, hal ini dapat memicu munculnya cyberbullying. Cyberbullying adalah tindakan berulang yang melecehkan, mempermalukan, mengancam, atau mengganggu orang lain melalui komputer, ponsel, dan perangkat elektronik lainnya, termasuk situs web jejaring sosial online. Twitter merupakan salah satu media sosial yang sering digunakan untuk melakukan cyberbullying. Deteksi cyberbullying merupakan langkah penting untuk membuat lingkungan yang baik dalam interaksi media sosial. Penelitian ini mendeteksi cyberbullying yang berasal dari tweet berbahasa Indonesia dengan menggunakan metode Random Forest sebagai pengklasifikasi. Seleksi fitur information gain juga digunakan untuk menyeleksi fitur yang berupa atribut. Penelitian ini bertujuan untuk mengetahui akurasi deteksi cyberbullying dari metode Random Forest dan memilih fitur penting untuk meningkatkan kinerja metode. Dari hasil pengujian, didapatkan nilai Accuracy tertinggi sebesar 72.1% dengan atribut berjumlah 1295 dari 2277 atribut. Hal ini berarti, pemilihan fitur yang baik dapat meningkatkan performa dari metode machine learning.

Kata kunci: Cyberbullying, Information Gain, Random Forest, Tweet


References


T. Febriana and A. Budiarto, “Twitter Dataset for Hate Speech and Cyberbullying Detection in Indonesian Language,” Proc. 2019 Int. Conf. Inf. Manag. Technol. ICIMTech 2019, vol. 1, no. August, pp. 379–382, 2019, doi: 10.1109/ICIMTech.2019.8843722.

L. Anindyati, A. Purwarianti, and A. Nursanti, “Optimizing Deep Learning for Detection Cyberbullying Text in Indonesian Language,” Proc. - 2019 Int. Conf. Adv. Informatics Concepts, Theory, Appl. ICAICTA 2019, pp. 1–5, 2019, doi: 10.1109/ICAICTA.2019.8904108.

H. Nurrahmi and D. Nurjanah, “Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility,” 2018 Int. Conf. Inf. Commun. Technol. ICOIACT 2018, vol. 2018-Janua, pp. 543–548, 2018, doi: 10.1109/ICOIACT.2018.8350758.

H. K. Sharma, K. Kshitiz, and Shailendra, “NLP and Machine Learning Techniques for Detecting Insulting Comments on Social Networking Platforms,” 2018 Int. Conf. Adv. Comput. Commun. Eng., no. June, pp. 265–272, 2018.

M. A. Al-garadi, K. D. Varathan, and S. D. Ravana, “Computers in Human Behavior Cybercrime detection in online communications : The experimental case of cyberbullying detection in the Twitter network,” Comput. Human Behav., vol. 63, pp. 433–443, 2016, doi: 10.1016/j.chb.2016.05.051.

D. Ramachandran and R. Parvathi, “ScienceDirect Analysis Analysis of of Twitter Twitter Specific Specific Preprocessing Preprocessing Technique Technique for for Tweets Tweets,” Procedia Comput. Sci., vol. 165, pp. 245–251, 2020, doi: 10.1016/j.procs.2020.01.083.

E. Odhiambo Omuya, G. Onyango Okeyo, and M. Waema Kimwele, “Feature Selection for Classification using Principal Component Analysis and Information Gain,” Expert Syst. Appl., vol. 174, no. February, p. 114765, 2021, doi: 10.1016/j.eswa.2021.114765.

S. Chormunge and S. Jena, “Efficient feature subset selection algorithm for high dimensional data,” Int. J. Electr. Comput. Eng., vol. 6, no. 4, pp. 1880–1888, 2016, doi: 10.11591/ijece.v6i4.9800.

Y. Zhang, X. Ren, and J. Zhang, “Intrusion detection method based on information gain and ReliefF feature selection,” Proc. Int. Jt. Conf. Neural Networks, vol. 2019-July, no. July, pp. 1–5, 2019, doi: 10.1109/IJCNN.2019.8851756.

X. Ji, B. Yang, and Q. Tang, “Seabed sediment classification using multibeam backscatter data based on the selecting optimal random forest model,” Appl. Acoust., vol. 167, p. 107387, 2020, doi: 10.1016/j.apacoust.2020.107387.

R. R. Dalvi, S. Baliram Chavan, and A. Halbe, “Detecting A Twitter Cyberbullying Using Machine Learning,” Proc. Int. Conf. Intell. Comput. Control Syst. ICICCS 2020, no. Iciccs, pp. 297–301, 2020, doi: 10.1109/ICICCS48265.2020.9120893.

M. Fortunatus, P. Anthony, and S. Charters, “Combining textual features to detect cyberbullying in social media Combining textual features to detect cyberbullying in social media posts posts,” Procedia Comput. Sci., vol. 176, pp. 612–621, 2020, doi: 10.1016/j.procs.2020.08.063.

M. Z. Islam, J. Liu, J. Li, L. Liu, and W. Kang, “A Semantics Aware Random Forest for Text Classification,” CIKM, vol. 19, pp. 1061–1070, 2019, doi: 10.1145/3357384.3357891.

P. Jiang and J. Chen, “Neurocomputing Displacement prediction of landslide based on generalized regression neural networks with K -fold cross-validation,” Neurocomputing, pp. 1–8, 2016, doi: 10.1016/j.neucom.2015.08.118.

D. Kim, D. Seo, S. Cho, and P. Kang, “Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec,” Inf. Sci. (Ny)., 2018, doi: 10.1016/j.ins.2018.10.006




DOI: https://doi.org/10.21107/simantec.v11i1.17256

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Rachmad asbadi Hatullah Nurnaryo1, Mulaab Mulaab, Ika Oktavia Suzanti

coktogel

kakaktogel

slot gacor

toto togel

situs togel

laetoto

laetoto

slot gacor

QQ188

slot gacor

Presidenslot

bonus new member

slot

batmantoto

slot gacor

slot gacor

slot gacor

slot olympus

slot demo

slot deposit 1000

slot gacor

slot88

rokokbet

slot88

slot gacor

rokokbet

slot resmi

rokokbet

https://sandiegohills.org/

Situs Toto

Situs Toto

Dana4d

Situs Toto

Pan4D

Toto 4d

slot777

https://lppm.stba-jia.ac.id/

slot88

https://ppki.stba-jia.ac.id/

https://pmb.iainkudus.id/

https://jim.iainkudus.id/

slot777

Situs Toto

Slot777

SITUS TOTO

PAN4D

PAN4D

Slot88

Toto 4d

situs toto

slot88

https://iboninternational.org/about-us

https://jurnalkommas.com/docs/Jurnal

https://www.wulaguda.com.au/gallery

slot thailand

bonus new member

togelin

situs toto

hokijp168

paris88

slot gacor

vegas123

situs slot

miya4d

slot gacor

mahjong ways 2

DPRBET

dollar4d

levis4d

buku mimpi

laetoto

Slot Gacor

slot pulsa

slot777

slot gacor gampang menang

Indexed By

slot

slot777

slot gacor maxwin

slot777

slot gacor

slot gacor

slot777

merahtoto

merahtoto

slot777

hijautoto

kuningtoto

game slot

kuningtoto slot

kuningtoto

link kuningtoto

birutoto

birutoto

slot gacor

ungutoto

link ungutoto

slot gacor

ungutoto

togel online 4d

tototogel

bandar togel

game online

slot gacor

situs gacor

merahtoto

kuningtoto

kuningtoto

merahtoto

hijautoto

hijautoto

hijautoto

hijautoto

birutoto

slot88

merahtoto

merahtoto

merona4d

merona4d

merona4d

merona4d

merona4d

merona4d

merona4d

merona4d

slot gacor maxwin

birutoto

birutoto

hijautoto

kuningtoto

slot777

slot88

scatter hitam

rokokbet

rokokbet

rokokbet

paito hk

raja togel

toto macau

https://global-jws.com/ojs/

situs toto

slot gacor

merahtoto

merona4d

Slot Gacor

merona4d

hijautoto

slot gacor

merahtoto

merona4d

merona4d

birutoto

situs togel

birutoto

slot88

merahtoto

merona4d

kuningtoto

hijautoto

birutoto

kuningtoto

slot gacor hari ini

merahtoto

merahtoto

birutoto

birutoto

ungutoto

ungutoto

ungutoto

ungutoto

violetslot

violetslot

hijautoto

kuningtoto

kuningtoto

kuningtoto

kuningtoto

birutoto

birutoto

birutoto

birutoto

birutoto

merona4d

ungutoto

ungutoto

ungutoto

ungutoto

ungutoto

ungutoto

merahtoto

kuningtoto

birutoto

birutoto

situs toto

birutoto