COMPARISON NAÏVE BAYES CLASSIFIER, K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE IN THE CLASSIFICATION OF INDIVIDUAL ON TWITTER ACCOUNT

aristin chusnul khotimah; Ema Utami

doi:10.20884/1.jutif.2022.3.3.254

aristin chusnul khotimah universitas amikom yogyakarta
Ema Utami Magister Teknik Informatika Universitas Amikom Yogyakarta

DOI: https://doi.org/10.20884/1.jutif.2022.3.3.254

Keywords: Personality DISC, Naïve Bayes Classifier, K-Nearest Neighbors, Support Vector Machine, Twitter

Abstract

In current’s digital era, people can take advantage of the ease and effectiveness of interacting with each other. The most popular online activity in Indonesia is the use of sosial media. Twitter is a social media that allows people to build communication between users and get the latest information or news. Information obtained from twitter can be processed to get the characteristics of a person using the DISC method, DISC is a behavioral model that helps every human being why someone does. To classify the tweet into the DISC method using algorithms naïve bayes classifier, k-nearest neighbor and support vector machine with the TF-IDF weighting. The results is compare the accuracy of the naïve bayes classifier algorithm has an accuracy rate of 31.5%, k-nearest neighbor has an accuracy rate of 23.8%, while the support vector machine has an accuracy rate of 28.4%.

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References

Agustina, Dyah Auliya, Sri Subanti, and Etik Zukhronah. "Implementasi Text Mining Pada Analisis Sentimen Pengguna Twitter Terhadap Marketplace di Indonesia Menggunakan Algoritma Support Vector Machine." Indonesian Journal of Applied Statistics 3.2 (2021): 109-122.

Alita, Debby, Yusra Fernando, and Heni Sulistiani. "Implementasi Algoritma Multiclass SVM pada Opini Publik Berbahasa Indonesia di Twitter." Jurnal Tekno Kompak 14.2 (2020): 86-91.

Srisadono, Wahyu. "Komunikasi Publik Calon Gubernur Provinsi Jawa Barat 2018 dalam Membangun Personal Branding Menggunakan Twitter." Jurnal Pustaka Komunikasi 1.2 (2018): 213-227.

Hartanto, Anggit Dwi, et al. "Job seeker profile classification of twitter data using the naïve bayes classifier algorithm based on the DISC method." 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2019.

Munggaran, Nur Ihsan Putra, and Erwin Budi Setiawan. "Prediksi Kepribadian Disc Dengan K-nearest Neighbors Algorithm (knn) Menggunakan Pembobotan Tf-idf Dan Tf-chi Square." eProceedings of Engineering 6.2 (2019).

Tambunan, Maulina Gustiani, and Erwin Budi Setiawan. "Prediksi Kepribadian Disc Pada Twitter Menggunakan Metode Decision Tree C4. 5 Dengan Pembobotan Tf-idf Dan Tf-rf." eProceedings of Engineering 7.1 (2020).

Sembodo, J. Eka, E. Budi Setiawan, and Z. Abdurahman Baizal. "Data Crawling Otomatis pada Twitter." Indonesian Symposium on Computing (Indo-SC). 2016.

Ramadhan, Dery Anjas, and Erwin Budi Setiawan. "Analisis Sentimen Program Acara di SCTV pada Twitter Menggunakan Metode Naive Bayes dan Support Vector Machine." eProceedings of Engineering 6.2 (2019).

Nugroho, Agung. "Analisis Sentimen Pada Media Sosial Twitter Menggunakan Naive Bayes Classifier Dengan Ekstrasi Fitur N-Gram." J-SAKTI (Jurnal Sains Komputer dan Informatika) 2.2 (2018): 200-209.

Kurniawan, Imam, and Ajib Susanto. "Implementasi Metode K-Means dan NaÃ¯ ve Bayes Classifier untuk Analisis Sentimen Pemilihan Presiden (Pilpres) 2019." Jurnal Eksplora Informatika 9.1 (2019): 1-10.

Husada, Hendry Cipta, and Adi Suryaputra Paramita. "Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM)." Teknika 10.1 (2021): 18-26.

Prameswari, Kartika, and Erwin Budi Setiawan. "Analisis Kepribadian Melalui Twitter Menggunakan Metode Logistic Regression dengan Pembobotan TF-IDF dan AHP." eProceedings of Engineering 6.2 (2019).

Hadna, N. Muchammad Shiddieqy, P. Insap Santosa, and Wing Wahyu Winarno. "Studi literatur tentang perbandingan metode untuk proses analisis sentimen di Twitter." Semin. Nas. Teknol. Inf. dan Komun 2016 (2016): 57-64.

H. Aria, W. Titin and I. Henny, “PENGEMBANGAN STEMMING UNTUK ARTIKEL BERBAHASA INDONESIA.”, https://repository.usm.ac.id/files/research/G071/20200410024415-PENGEMBANGAN-STEMMING-UNTUK-ARTIKEL-BERBAHASA-INDONESIA.pdf, diakses pada 25 Februari 2022

Edwin, Edwin. "APLIKASI ASSESMENT KEBIJAKAN PEMERINTAH TERKAIT OPERASI OJEK ONLINE DI MASA PANDEMI (COVID-19) MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER." Jurnal Algoritma, Logika dan Komputasi 3.2 (2021).

Kurniawan, Robi, and Aulia Apriliani. "Analisis sentimen masyarakat terhadap virus corona berdasarkan opini dari Twitter berbasis web scraper." Jurnal INSTEK (Informatika Sains dan Teknologi) 5.1 (2020): 67-75.