PENGELOMPOKKAN BERITA KESEHATAN PADA SOSIAL MEDIA TWITTER DENGAN METODE K-MEANS CLUSTERING

Anita Anita, Jeffer Oliando

Sari


Twitter is one of the most influential social media in the world with 15.7 million users in Indonesia and is ranked 6th as the country with the most Twitter users. Besides being used as social media, Twitter is also used as a medium to read or send news. News information that continues to increase causes users to have difficulty in finding certain news information. One solution that can be applied to overcome this problem is through clustering of news information on Twitter. In this study, the researcher used quantitative research with the K-Means Clustering method, which is one of the clustering methods used in grouping data. The data used in this study is a dataset taken from the UCI Machine Learning Repository, namely Health News in Twitter Data Set as many as 14,970 tweet data. The results showed that the determination of the best cluster using the Elbow method on the dataset resulted in empirical evidence that the best cluster was K=3. The results of grouping health news on Twitter social media using the K-Means Clustering method with K=3 resulted in the number of clusters, namely C1 as many as 4,991 data tweets, C2 as many as 4,482 tweets data, and C3 as many as 5,497 tweets.

Keywords: Health News Grouping; Twitter Social Media; Elbow Method; K-Means Clustering Method.


Teks Lengkap:

PDF

Referensi


Dihni, V. A., & Bayu, D. J. (2021). Inilah 10 Negara dengan Pengguna Twitter Terbanyak, Ada Indonesia? Retrieved November 4, 2021, from https://databoks.katadata.co.id/datapublish/2021/11/04/inilah-10-negara-dengan-pengguna-twitter-terbanyak-ada-indonesia

Kasanah, A. N., Muladi, & Pujianto, U. (2019). Penerapan Teknik SMOTE untuk Mengatasi Imbalance Classdalam Klasifikasi Objektivitas Berita Online Menggunakan Algoritma KNN. Jurnal Rekayasa Sistem dan Teknologi Informasi (RESTI), III(2), 196-201.

Kurniawan, B., Fauzi, M. A., & Widodo, A. W. (2017). Klasifikasi Berita Twitter Menggunakan Metode Improved Naïve Bayes. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, I(10), 1193-1200.

Kom, HARIYADI S. "Pengembangan Sistem Pakar Berbasis Aturan Untuk Menentukan Mata Kuliah Yang Akan Diambil Ulang (Remedial) Dengan Metode Forward Chaining." Menara Ilmu 10.60-65 (2016).

Larasati, A., Maren, R., & Wulandari, R. (2021). Utilizing Elbow Method for Text Clustering Optimization in Analyzing Social Media Marketing Content of Indonesian e-Commerce. Jurnal Teknik Industri, XXIII(2), 111-119.

Madhulatha, T. S. (2012). An Overview on Clustering Methods. IOSR Journal of Engineering, II(4), 719-725.

Nugroho, A. (2018). Analisis Sentimen Pada Media Sosial Twitter Menggunakan Naive Bayes Classifier Dengan Ekstrasi Fitur N-Gram. Jurnal Sains Komputer & Informatika (J-SAKTI), II(2), 200-209.

Pramudita, Y. D., Putro, S. S., & Makhmud, N. (2018). Klasifikasi Berita Olahraga Menggunakan Metode Naive Bayes Dengan Enchanted Confix Stripping Stemmer. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), V(3), 269-276.

Purba, N., Poningsih, & Tambunan, H. S. (2021). Penerapan Algoritma K-Means Clustering Pada Penyebaran Penyakit Infeksi Saluran Pernapasan Akut (ISPA) di Provinsi Riau. Journal of Information System Research (JOSH), II(3), 220-226.

Rachman, D. A., Goejantoro, R., & Amijaya, F. D. (2020). Implementasi Text Mining Pengelompokkan Dokumen Skripsi Menggunakan Metode K-Means Clustering. Jurnal EKSPONENSIAL, XI(2), 167-174.

Sugiyamto, Surarso, B., & Sugiharto, A. (2021). Analisa Performa Metode Cosine dan Jacard Pada Pengujian Kesamaan Dokumen. Jurnal Masyarakat Informatika, V(10), 1-8.

Sugiyono. (2017). Metode Penelitian Kuantitatif, Kualitatif, dan R&D. Bandung: CV. Alfabeta.




DOI: https://doi.org/10.33559/eoj.v4i3.877

Refbacks

  • Saat ini tidak ada refbacks.


Jumlah Kunjungan

Negara Pengunjung

Flag Counter

Lisensi Creative Commons
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi 4.0 Internasional.