TEXT MINING DALAM MEMBANDINGKAN METODE NAÏVE BAYES DENGAN C.45 DALAM MENGIDENTIFIKASI BERITA HOAX PADA MEDIA SOSIAL

Yola Tri Handika; Sarjon Defit; Gunadi Widi Nurcahyo

doi:10.31869/rtj.v5i1.2855

TEXT MINING DALAM MEMBANDINGKAN METODE NAÏVE BAYES DENGAN C.45 DALAM MENGIDENTIFIKASI BERITA HOAX PADA MEDIA SOSIAL

Yola Tri Handika, Sarjon Defit, Gunadi Widi Nurcahyo

Sari

Hoax news (hocus to trick) has a very big influence in disseminating information, especially in the world of social media. News has an important impact on social and political conditions, and news can move the economy of a country. For this reason, it is necessary to have an analysis to classify hoax news and not hoaxes, and have high accuracy in classifying the news. In this study, two methods were used as a comparison in achieving high accuracy, namely the Naïve Bayes method which is famous for having high accuracy in classification with little data, and the C.45 method which can minimize noise in the data. The data used are 300 articles with 10 topics which contain hoax and non-hoax news. The data is obtained from the internet through social media, such as Twitter

, Instagram and Facebook. Testing using the Naïve Bayes method has a higher accuracy than the C.45 method. The amount of data used has a major influence on the test results, if more data enters the training stage, then this study will have higher accuracy. However, the results of this test can be recommended to increase accuracy in the construction of a hoax news detection system.

Teks Lengkap:

PDF

Referensi

Kim, H., Soibelman, L., & Grobler, F. (2008). Factor selection for delay analysis using Knowledge Discovery in Databases. Automation in Construction, 17(5), 550–560. doi:10.1016/j.autcon.2007.10.001

Khairunnisa, S., Adiwijaya, A., & Faraby, S. A. (2021). Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19). JURNAL MEDIA INFORMATIKA BUDIDARMA, 5(2), 406. doi:10.30865/mib.v5i2.2835

Englmeier, K. (2021). The Role of Text Mining in Mitigating the Threats from Fake News and Misinformation in Times of Corona. Procedia Computer Science, 181, 149–156. doi:10.1016/j.procs.2021.01.115

Chintalapudi, N., Battineni, G., Canio, M. D., Sagaro, G. G., & Amenta, F. (2021). Text mining with sentiment analysis on seafarers’ medical documents. International Journal of Information Management Data Insights, 1(1), 100005. doi:10.1016/j.jjimei.2020.100005

Hubert, Phoenix, P., Sudaryono, R., & Suhartono, D. (2021). Classifying Promotion Images Using Optical Character Recognition and Naïve Bayes Classifier. Procedia Computer Science, 179, 498–506. doi:10.1016/j.procs.2021.01.033

Singh, M., Wasim Bhatt, M., Bedi, H. S., & Mishra, U. (2020). Performance of bernoulli’s naive bayes classifier in the detection of fake news. Materials Today:Proceedings. doi:10.1016/j.matpr.2020.10.896

Khan, J. Y., Khondaker, M. T. I., Afroz, S., Uddin, G., & Iqbal, A. (2021). A benchmark study of machine learning models for online fake news detection. Machine Learning with Applications, 4, 100032. doi:10.1016/j.mlwa.2021.100032

Devita, R. N., Herwanto, H. W., & Wibawa, A. P. (2018). Perbandingan Kinerja Metode Naive Bayes dan K-Nearest Neighbor untuk Klasifikasi Artikel Berbahasa indonesia. Jurnal Teknologi Informasi Dan Ilmu Komputer, 5(4), 427. doi:10.25126/jtiik.201854773

Indrayuni, E. (2019). Klasifikasi Text Mining Review Produk Kosmetik Untuk Teks Bahasa Indonesia Menggunakan Algoritma Naive Bayes. Jurnal Khatulistiwa Informatika, 7(1). doi:10.31294/jki.v7i1.5740

Kurniawan, Y. I. (2018). Perbandingan Algoritma Naive Bayes dan C.45 dalam Klasifikasi Data Mining. Jurnal Teknologi Informasi Dan Ilmu Komputer, 5(4), 455. doi:10.25126/jtiik.201854803

Irena, B., & Erwin Budi Setiawan. (2020). Fake News (Hoax) Identification on Social Media Twitter using Decision Tree C4.5 Method. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(4), 711–716. doi:10.29207/resti.v4i4.2125

Prasetijo, A. B., Isnanto, R. R., Eridani, D., Soetrisno, Y. A. A., Arfan, M., & Sofwan, A. (2017). Hoax detection system on Indonesian news sites based on text classification using SVM and SGD. 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). doi:10.1109/icitacee.2017.8257673

Negara, A. B. P., Muhardi, H., & Putri, I. M. (2020). Analisis Sentimen Maskapai Penerbangan Menggunakan Metode Naive Bayes dan Seleksi Fitur Information Gain. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(3), 599. doi:10.25126/jtiik.2020711947

Pujianto, U., & Ristanti, P. Y. (2019). Perbandingan kinerja metode C4.5 dan Naive Bayes dalam klasifikasi artikel jurnal PGSD berdasarkan mata pelajaran. TEKNO, 29(1), 50. doi:10.17977/um034v29i1p50-67

Rahutomo, F., Pratiwi, I. Y. R., & Ramadhani, D. M. (2019). Eksperimen Naïve Bayes Pada Deteksi Berita Hoax Berbahasa Indonesia. JURNAL PENELITIAN KOMUNIKASI DAN OPINI PUBLIK, 23(1). doi:10.33299/jpkop.23.1.1805

Dyo fatra Abdul Hadiy, Hayatin, N. H., & Aditya, C. S. K. (2020). Analisa Sentimen Tweet Berbahasa Indonesia Dengan Menggunakan Metode Lexicon Pada Topik Perpindahan Ibu Kota Indonesia. Jurnal Repositor, 2(11), 1562. doi:10.22219/repositor.v2i11.933

W, B., Riski, I., Dwi, K., Nooraeni, R., Siahaan, T., & Dhea, Y. (2019). Analisis Text Mining dari Cuitan Twitter Mengenai Infrastruktur di Indonesia dengan Metode Klasifikasi Naïve Bayes. EIGEN MATHEMATICS JOURNAL, 1(2), 92. doi:10.29303/emj.v1i2.36

DOI: https://doi.org/10.31869/rtj.v5i1.2855