Machine Learning Prediksi Karakter Pengguna Hastag (#) Bahasa Generasi Milenial Di Sosial Media

Anita Sindar RM Sinaga

Abstract

Activity on the internet leaves a traceable digital trail. Users who are expressive of social media and have a habit of pouring everything on Instagram are more considerate of the cause and effect of status updates. The problem discussed in this study is to describe the character of the Instagram user account according to the hashtags (#) of the most widely used millennial language such as #awesome and so on. With machine learning, computers can work alone. This digital technology has long been applied to Google search, search engines and social media (Facebook, Twitter, Instagram). The benefits of machine learning are the ease of obtaining digital data from online users. The stages of the study consisted of the application of algorithms that produced predictions for classification using the K-Nearest Neighbors Algorithm. The formulation of the problem in this research is how to process data sourced from millennial language hashtags based on the most popular hashtags (#) on instagram using machine learning by identifying names in the text into Connected, Creative and Confident. From the results of the calculation of the closest distance and proximity of the neighboring obtained 10 popular hashtags. Creative Classifications become dominan type user.

Keywords

Hashtag, Instagram, Classification, K-Nearest Neighbors, Machine Learning

Full Text:

PDF

References

P. K. L. Utama, “Identifikasi Hoax pada Media Sosial dengan Pendekatan Machine Learning”, Widya Duta Vol. 13, No. 1 2018, hal 69-76.

Purnomo, M, H, Sumpeno, S, Setiawan, E, I, Purwitasari, D, “Biomedical Engineering Research in the Social Network Analysis Era: Stance Classification for Analysis of Hoax Medical News in Social Media”, Procedia Computer Science 116 2017, pp. 3–9.

R, R, Sani,, J. Zeniarja, A. Luthfiarta, “Penerapan Algoritma K-Nearest Neighbor pada Information Retrieval dalam Penentuan Topik Referensi Tugas Akhir”, Journal of Applied Intelligent System, Vol. 1, No. 2, Juni 2016: 123 – 133.

S. M. Hutabarat, Anita Sindar, “Data Mining Penjualan Suku Cadang Sepeda Motor Menggunakan Algoritma K-Means”, Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) 2 (2), 126-132.

M. Rivki, A. M. Bachtiar, “Implementasi Algoritma K-Nearest Neighbor Dalam Pengklasifikasian Follower Twitter Yang Menggunakan Bahasa Indonesia”, Jurnal Sistem Informasi (Journal of Information Systems), 2017, 31-37.

D. Simanjuntak, A. S. RM. Sinaga, “Sistem Pakar Deteksi Gizi Buruk Balita Dengan Metode Naïve Bayes Classifier”, Jurnal Inkofar * Volume 1 No. 2 Desember 2019, hl: 54-60.

Chairani, Widyawan, S. S. Kusumawardani, “Machine Learning Untuk Estimasi Posisi Objek Berbasis RSS Fingerprint Menggunakan IEEE 802.11g Pada Lantai 3 Gedung JTETI UGM”, Jurnal Infotel Vol. 7 No. 1 Mei 2015.

D. A. Ambarsari, R. N. Nurfalah, S. J. Kuryanti, “Penerapan Deep Learning dalam Pendeteksian Autism Toddler”, Infotekjar :Jurnal Nasional Informatikadanteknologijaringan- Vol. 4 No. 1, 2019.

F. Thabtah, “Autism spectrum disorder screening: Machine learning adaptation and DSM-5 fulfillment,” ACM Int. Conf. Proceeding Ser., vol. Part F129311, 2017, pp. 1–6.

Mustakim, G. Oktaviani, “Algoritma K-Nearest Neighbor Classification Sebagai Sistem Prediksi Predikat Prestasi Mahasiswa”, Jurnal Sains Teknologi dan Industri, Vol. 13, No.2, Juni 2016, pp.195 – 202.

A. S. RM. Sinaga, R. N. Zendrato, “Optimasi Penugasan Pegawai Menggunakan Metode Hungarian”, Journal of Innovation Information Technology and Application (JINITA), Volume 1 Issue 01 Pages 16-24 Tahun 2019.

Razzaghi, T., Roderick, O., Safro, I., Marko, N, Multilevel weighted support vector machine for classification on healthcare data with missing values. PloS ONE, 11(5), e0155,119.P. K. L. Utama. 2018. Identifikasi Hoax pada Media Sosial dengan Pendekatan Machine Learning”, Widya Duta Vol. 13, No. 1. hal 69-76.

. Ramadhan, R. S. Junta, Z., Ardytha, L. 2016. Penerapan Algoritma K-Nearest Neighbor pada Information Retrieval dalam Penentuan Topik Referensi Tugas Akhir. Journal of Applied Intelligent System, Vol. 1, No. 2, 123 – 133.

DOI: https://doi.org/10.20961/ijai.v4i2.41764

Refbacks

  • There are currently no refbacks.