Penerapan Metode Multinomial Naïve Bayes untuk Klasifikasi Judul Berita Clickbait dengan Term Frequency - Inverse Document Frequency
DOI:
https://doi.org/10.26418/justin.v11i1.57452Keywords:
Berita, Clickbait, Klasifikasi, Multinomial Naïve Bayes, Term Frequency - Inverse Document Frequency, Confusion MatrixAbstract
Clickbait merupakan judul berita yang bombastis dan memberikan informasi tidak utuh sehingga membuat pembaca penasaran ingin tahu dengan cara mengklik tautan berita. Penggunaan judul berita clickbait terkadang bersifat menjebak karena judul dari artikel tersebut bersifat tidak utuh. Hal tersebut menyebabkan kesimpulan yang didapat dari judul dan isi berita terkadang tidak sesuai. Sehingga perlu dilakukan penelitian untuk mengklasifikasi judul berita yang termasuk clickbait atau bukan. Penelitian ini menggunakan metode Multinomial Naïve Bayes dan TF-IDF (Term Frequency - Inverse Document Frequency) dengan data didapat dari judul berita online yang di ambil dari beberapa situs website. Pada penelitian ini TF-IDF digunakan untuk memberikan bobot kata pada proses pelatihan, pengujian dan klasifikasi. Data yang digunakan berjumlah 1000 data dengan 800 data latih dan 200 data uji. Pengujian dilakukan menggunakan confusion matrix sehingga didapatkan akurasi sebesar 65 %, recall sebesar 65,69 % dan precision sebesar 65,69 %.
References
Y. D. Hadiyat, “Clickbait di Media Online Indonesia,†Jurnal Pekommas, pp. 1-10, 2019.
M. Reily, “Nielsen: Pembaca Media Digital Sudah Lampaui Media Cetak,†7 Desember 2017. [Online]. Available: https://katadata.co.id/berita/2017/12/07/nielsen-pembaca-media-digital-sudah-lampaui-media-cetak.
A. Manan, “Ancaman Baru dari Digital. Laporan, Jakarta: AJI, 2018.
D. Palau and Sampio, “Reference press metamorphosis in the digital context: clickbait and tabloid strategies in elpais.com,†Communication & Society, p. 29(2), 2016.
A. Anand, T. Chakraborty and N. Park, “We used neural networks to detect clickbaits: You won’t believe what happened next!,†Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10193 LNCS, p. 541–547, 2017.
Y. Chen, N. J. Conroy and V. L. Rubin, “Misleading Online Content,†Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection - WMDD ’15, (November), pp, p. 15–19, 2015.
I. Habibie, “Identifikasi Judul Berita Clickbait Berbahasa Indonesia dengan Algoritma Long Short Term Memory (LSTM) Recurrent Neural Network,†Journal USU, 2018.
R. Maulidi, M. F. Ayilillahi, L. Isyiriyah and J. F. Palandi, “PENERAPAN NEURAL NETWORK BACKPROGPAGATION UNTUK KLASIFIKASI ARTIKEL CLICKBAIT,†Seminar Nasional FST, pp. 751-757, 2018.
A. Rahman, Wiranto and A. Doewes, “Online News Classification Using Multinomial Naive Bayes,†ITSMART: Jurnal Ilmiah Teknologi dan Informasi, pp. 32-38, 2017.
M. I. Akbar, “www.muhbalak.com,†Muhbalak, 31 Agustus 2016. [Online]. Available: https://www.muhbalak.com/2016/08/positif-dan-negatif-menggunakan-judul-yang-clickbait.html. [Accessed 22 Januari 2022].
R. Feldman and J. Sanger, The Text Mining Handbook, New York: Cambridge University Press, 2007.
D. Wahyudi, T. Susyanto and D. Nugroho, “Implementasi dan Analisis Algoritma Stemming Nazief & Adriani dan Porter pada Dokumen Berbahasa Indonesia,†Jurnal Ilmiah SINUS, pp. 49-56, 2017.
A. Sabrani, I. G. P. W. Wedashwara and F. Bimantoro, “METODE MULTINOMIAL NAÃVE BAYES UNTUK KLASIFIKASI ARTIKEL ONLINE TENTANG GEMPA DI INDONESIA,†JTIKA, pp. 89-100, 2020.
A. Ryansyah and S. Andayani, “Implementasi Algoritma TF-IDF Pada Pengukuran Kesamaan Dokumen,†JURNAL SISTEM & TEKNOLOGI INFORMASI KOMUNIKASI, pp. 1-10, 2017.
D. H. Kalokasari, D. I. M. Shofi and A. H. Setyaningrum, “IMPLEMENTASI ALGORITMA MULTINOMIAL NAIVE BAYES CLASSIFIER PADA SISTEM KLASIFIKASI SURAT KELUAR (Studi Kasus : DISKOMINFO Kabupaten Tangerang),†JURNAL TEKNIK INFORMATIKA, pp. 109-118, 2017.
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