SENTIMENT ANALYSIS ABOUT UNHAN RI THROUGH TWITTER SOCIAL MEDIA
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Keywords:
Sentiment Analysis, UNHAN RI, Naïve Bayes
Abstract
This study aims to determine public opinion regarding Universitas Pertahanan Republik Indonesia (UNHAN RI). As a new university, responses regarding public opinion need to be carried out so that the university can provide better services and educational programs. To see the public response to UNHAN RI, a Machine Learning method is used, namely Naïve Bayes. This Naïve Bayes modeling can help to classify public sentiment analysis towards UNHAN RI. By Naïve Bayes modeling, an accuracy value of 60.78% was obtained with three classification results, namely positive, negative, and neutral. The largest classification results were obtained in the positive class of 60.8%, the neutral class of 33.3%, and the negative class of 5.9%..
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References
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[2] R. Saptari, R. Rianto, and A. I. Gufroni, “Analisis Sentimen Pengguna Twitter Terhadap Pelayanan Unit Gawat Darurat Rumah Sakit Umum di Indonesia Menggunakan Seleksi Fitur Information Gain dan Support Vector Machine,” Joined Journal, vol. 4, no. 2, pp. 104–110, 2021, doi: https://doi.org/10.31331/joined.v4i2.1925.
[3] R. Mursyid and A. D. Indriyanti, “Perbandingan Akurasi Metode Analisis Sentimen Untuk Evaluasi Opini Pengguna Pada Platform Media Sosial (Studi Kasus: Twitter),” Journal of Informatics and Computer Science (JINACS), vol. 06, pp. 371–383, Jun. 2024, [Online]. Available: https://ejournal.unesa.ac.id/index.php/jinacs/article/view/61322
[4] M. Alfarizi, M. Rizqy, R. I. Ghufroni, D. Fathurahman, R. D. Saputra, and F. Kurniawan, “Analisis Sentimen Persepsi Publik Terhadap Kasus Bullying Siswa Cilacap Menggunakan Pendekatan Machine Learning,” Journal of Information Technology Ampera, vol. 4, no. 3, pp. 265–276, Dec. 2023, doi: 10.51519/JOURNALITA.V4I3.436.
[5] M. Haikal, B. Prayogi, N. P. Ganatha, M. R. Ferdiansyah, and N. A. R. Rakhmawati, “Analisis Persepsi Masyarakat Dunia Terhadap Virtual Youtuber yang Menawarkan Pengalaman Kekasih,” E-Link: Jurnal Teknik Elektro dan Informatika, vol. 19, no. 1, pp. 90–98, May 2024, doi: 10.30587/E-LINK.V19I1.6746.
[6] H. D. Al Assyam and F. N. Hasan, “Analisis Sentimen Twitter Terhadap Perpindahan Ibu Kota Negara Ke IKN Nusantara Menggunakan Orange Data Mining,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 1, pp. 341–349, Aug. 2023, doi: 10.30865/KLIK.V4I1.957.
[7] R. H. Sukarna and Y. Ansori, “Implementasi Data Mining Menggunakan Metode Naive Bayes dengan Feature Selection untuk Prediksi Kelulusan Mahasiswa Tepat Waktu,” Jurnal Ilmiah Sains dan Teknologi, vol. 6, no. 1, pp. 50–61, Feb. 2022, doi: 10.47080/SAINTEK.V6I1.1467.
[8] K. V. S. Toy, Y. A. Sari, and I. Cholissodin, “Analisis Sentimen Twitter menggunakan Metode Naive Bayes dengan Relevance Frequency Feature Selection (Studi Kasus: Opini Masyarakat mengenai Kebijakan New Normal),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 5, no. 11, pp. 5068–5074, Oct. 2021, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/10172
[9] R. Mubarak, M. Hanafi, and D. Sasongko, “Komparasi Performa Naive Bayes Gaussian dan K-NN Untuk Prediksi Kelulusan Mahasiswa dengan CRISP-DM,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 6, pp. 2982–2991, Jun. 2024, doi: 10.30865/KLIK.V4I6.1924.
[10] H. Asyraf and M. E. Prasetya, “Implementasi Metode CRISP DM dan Algoritma Decision Tree Untuk Strategi Produksi Kerajinan Tangan pada UMKM A,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 8, no. 1, pp. 94–105, Jan. 2024, doi: 10.30865/MIB.V8I1.7050.
[11] S. A. Perdana, S. F. Florentin, and A. Santoso, “ANALISIS SEGMENTASI PELANGGAN MENGGUNAKAN K-MEANS CLUSTERING STUDI KASUS APLIKASI ALFAGIFT,” Sebatik, vol. 26, no. 2, pp. 446–457, Dec. 2022, doi: 10.46984/SEBATIK.V26I2.1991.
[12] H. Nuraliza, O. N. Pratiwi, and F. Hamami, “Analisis Sentimen IMBd Film Review Dataset Menggunakan Support Vector Machine (SVM) dan Seleksi Feature Importance,” Jurnal Mirai Management, vol. 7, no. 1, pp. 1–17, Aug. 2022, doi: 10.37531/MIRAI.V7I1.2222.
[13] A. Pramudiansyah and H. Munte, “Segmentasi Pelanggan Menggunakan Algoritma K-Means Berdasarkan Model Recency Frequency Monetary,” Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar, vol. 7, no. 2, pp. 06–19, Sep. 2021, doi: 10.35329/JIIK.V7I2.201.
[14] Y. A. Singgalen, “Penerapan CRISP-DM dalam Klasifikasi Sentimen dan Analisis Perilaku Pembelian Layanan Akomodasi Hotel Berbasis Algoritma Decision Tree (DT),” Jurnal Sistem Komputer dan Informatika (JSON), vol. 5, no. 2, pp. 237–248, Dec. 2023, doi: 10.30865/JSON.V5I2.7081.
[15] M. Y. Abdilah and S. Suparni, “Rancang Bangun Sistem Informasi Iventory Stock Barang Usaha Fashion Pada Toko Online Mezzaluna Signature Depok,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 6, pp. 3852–3859, Jan. 2023, doi: 10.36040/JATI.V7I6.7735.
[16] A. Fitri Ariani, K. Aulia, and A. A. La Ode, “Pengembangan Dashboard Interaktif Menggunakan Looker Studio untuk Visualisasi dan Prediksi Harga Komoditas Cabe di Jawa Timur,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 4, pp. 8067–8074, Aug. 2024, doi: 10.36040/JATI.V8I4.10616.
Published
2024-11-15
How to Cite
[1]
N. Lediwara, S. Denrineksa Bimorogo, A. Khamas Heikmakhtiar, I. Jaya Gainal, S. Naylil Karomah, and K. Fahmi, “SENTIMENT ANALYSIS ABOUT UNHAN RI THROUGH TWITTER SOCIAL MEDIA”, antivirus, vol. 18, no. 2, pp. 212 - 221, Nov. 2024.
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Articles
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