Analisis Sentimen Publik terhadap Virus HMPV Berdasarkan Media Sosial X dengan Algoritma Logistic Regression
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Feri Aldi Wijaya
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
- Parjito Parjito Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia (*) Corresponding Author
Abstract
Human Metapneumovirus (HMPV) is a virus that affects the respiratory tract, causing flu-like symptoms such as cough, fever, and nasal congestion. This virus was first discovered in 2001 and generally causes mild infections. However, certain groups, such as children, the elderly, and individuals with weakened immune systems, are at higher risk of developing severe conditions like bronchitis or pneumonia. Based on this issue, a sentiment analysis of public responses to Human Metapneumovirus (HMPV) cases was conducted using data collected from the X platform, consisting of 10,199 tweets. The data was gathered between December 1, 2024, and January 30, 2025, using Tweet Harvest in Google Colab with the Twitter API. This study applied the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance, with an 80% to 20% split between training and testing data. The results showed that before applying SMOTE, the logistic regression algorithm had an accuracy of 83%, with precision for positive sentiment at 90%, neutral at 80%, negative at 85%, while recall for positive sentiment was 89%, neutral 89%, negative 92%. After applying SMOTE, accuracy increased to 90%, with the most significant improvement observed in positive sentiment. The precision for positive sentiment reached 90%, neutral 87%, and negative 95%, while recall for positive sentiment was 96%, neutral 90%, negative 84%. This research provides insights into the use of logistic regression algorithms in sentiment analysis related to HMPV and serves as a reference for governments and health organizations in designing more effective communication strategies and interventions.
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References
S. Ma Et Al., “Development Of A Novel Multi-Epitope Mrna Vaccine Candidate To Combat Hmpv Virus,” Hum Vaccin Immunother, Vol. 19, No. 3, 2023, Doi: 10.1080/21645515.2023.2293300.
S. M. Aji Muhawarman, “Wabah Virus Hmpv Merebak Di China, Kemenkes Imbau Publik Untuk Waspada,” Https://Sehatnegeriku.Kemkes.Go.Id/. Accessed: Feb. 23, 2025. [Online]. Available: Https://Sehatnegeriku.Kemkes.Go.Id/Baca/Umum/20250103/5547197/Wabah-Virus-Hmpv-Merebak-Di-China-Kemenkes-Imbau-Publik-Untuk-Waspada/
J. C. Muñoz-Escalante, G. Mata-Moreno, G. Rivera-Alfaro, And D. E. Noyola, “Global Extension And Predominance Of Human Metapneumovirus A2 Genotype With Partial G Gene Duplication,” Viruses, Vol. 14, No. 5, May 2022, Doi: 10.3390/V14051058.
Y. Feng, T. He, B. Zhang, H. Yuan, And Y. Zhou, “Epidemiology And Diagnosis Technologies Of Human Metapneumovirus In China: A Mini Review,” Biomed Central Ltd., Access Date Dec. 01, 2024, Doi: 10.1186/S12985-024-02327-9.
Https://Databoks.Katadata.Co.Id/, “Ada 27 Juta Pengguna Twitter Di Indonesia, Terbanyak Ke-4 Global,” Https://Databoks.Katadata.Co.Id/. Accessed: Feb. 07, 2025. [Online]. Available: Https://Databoks.Katadata.Co.Id/Teknologi-Telekomunikasi/Statistik/75dd4b36866dc54/Ada-27-Juta-Pengguna-Twitter-Di-Indonesia-Terbanyak-Ke-4-Global
N. Hadi And D. Sugiarto, “Analisis Sentimen Pembangunan Ikn Pada Media Sosial X Menggunakan Algoritma Svm, Logistic Regression Dan Naïve Bayes,” Jurnal Informatika: Jurnal Pengembangan It, Vol. 10, No. 1, Pp. 37–49, Jan. 2025, Doi: 10.30591/Jpit.V10i1.7106.
Kelvin, J. Banjarnahor, E. Indra, And S. H. Sinurat, “Analisis Perbandingan Sentimen Corona Virus Disease-2019 (Covid-19) Pada Twitter Menggunakan Metode Logistic Regression Dan Support Vector Machine (Svm),” Jusikom Prima, Vol. 5, No. 2, Feb. 2022, Accessed: Feb. 27, 2025. [Online]. Available: Https://Jurnal.Unprimdn.Ac.Id/Index.Php/Jusikom/Article/View/2365
Y. Pratama, D. T. Murdiansyah, And K. M. Lhaksmana, “Analisis Sentimen Kendaraan Listrik Pada Media Sosial Twitter Menggunakan Algoritma Logistic Regression Dan Principal Component Analysis,” Jurnal Media Informatika Budidarma, Vol. 7, No. 1, P. 529, Feb. 2023, Doi: 10.30865/Mib.V7i1.5575.
Q. Philippot Et Al., “Human Metapneumovirus Infection Is Associated With A Substantial Morbidity And Mortality Burden In Adult Inpatients,” Heliyon, Vol. 10, No. 13, Jul. 2024, Doi: 10.1016/J.Heliyon.2024.E33231.
M. Marizal And A. Nurmita, “Mendeteksi Pengaruh Persepsi Mahasiswa Terhadap Vaksinasi Covid-19 Menggunakan Ordinal Logistic Regression,” Al-Ittizaan: Jurnal Bimbingan Konseling Islam, Vol. 4, No. 2, P. 43, Feb. 2022, Doi: 10.24014/Ittizaan.V4i2.16201.
E. R. Lidinillah, T. Rohana, And A. R. Juwita, “Analisis Sentimen Twitter Terhadap Steam Menggunakan Algoritma Logistic Regression Dan Support Vector Machine,” Teknosains : Jurnal Sains, Teknologi Dan Informatika, Vol. 10, No. 2, Pp. 154–164, Jul. 2023, Doi: 10.37373/Tekno.V10i2.440.
F. Fazrin, O. N. Pratiwi, And R. Andreswari, “Perbandingan Algoritma K-Nearest Neighbor Dan Logistic Regression Pada Analisis Sentimen Terhadap Vaksinasi Covid-19 Pada Media Sosial Twitter Dengan Pelabelan Vader Dan Textblob,” E-Proceeding Of Engineering, Vol. 10, No. 2, P. 1596, Apr. 2023, Accessed: Feb. 27, 2025. [Online]. Available: Https://Openlibrarypublications.Telkomuniversity.Ac.Id/Index.Php/Engineering/Article/View/19960
Kelvin, J. Banjarnahor, E. Indra, And S. H. Sinurat, “Analisis Perbandingan Sentimen Corona Virus Disease- 2019 (Covid19) Pada Twitter Menggunakan Metode Logistic Regression Dan Support Vector Machine (Svm),” Jusikom Prima, Vol. 5, No. 2, Pp. 2580–2879, Feb. 2022, Accessed: Feb. 27, 2025. [Online]. Available: Https://Jurnal.Unprimdn.Ac.Id/Index.Php/Jusikom/Article/View/2365
R. Prabowo, H. Sujaini, And T. Rismawan, “Analisis Sentimen Pengguna Twitter Terhadap Kasus Covid-19 Di Indonesia Menggunakan Metode Regresi Logistik Multinomial,” Jurnal Sistem Dan Teknologi Informasi (Justin), Vol. 11, No. 1, P. 85, Jan. 2023, Doi: 10.26418/Justin.V11i1.57450.
A. Shiddicky, “Analisis Sentimen Masyarakat Terhadap Kebijakan Vaksinasi Covid-19 Pada Media Sosial Twitter Menggunakan Metode Logistic Regression,” Coscitech, Vol. 3, No. 2, Pp. 99–106, Oct. 2022, Accessed: Feb. 27, 2025. [Online]. Available: Https://Doi.Org/10.37859/Coscitech.V3i2.3836
B. R. Talisman, I. Darmawan, And I, N. Pratiwi. Oktaria, “Perancangan Aplikasi Data Crawling Untuk Pencarian Buku Pada Toko Buku Online,” E-Proceeding Of Engineering, Vol. 11, No. 4, 2024, Accessed: Feb. 27, 2025. [Online]. Available: Https://Openlibrarypublications.Telkomuniversity.Ac.Id/Index.Php/Engineering/Article/View/24280
B. A. Yuniarossy Et Al., “Analisis Sentimen Terhadap Isu Feminisme Di Twitter Menggunakan Model Convolutional Neural Network (Cnn),” Jurnal Lebesgue, Vol. 5, No. 1, Apr. 2024, Doi: 10.46306/Lb.V5i1.
R. Darman, “Analisis Sentimen Respons Twitter Terhadap Persyaratan Badan Penyelenggara Jaminan Sosial (Bpjs) Di Kantor Pertanahan,” Widya Bhumi, Vol. 3, No. 2, Pp. 113–136, Oct. 2023, Doi: 10.31292/Wb.V3i2.61.
Abd. A. Syam, G. Hardy M, A. Salim, D. F. Surianto, And M. Fajar B, “Analisis Teknik Preprocessing Pada Sentimen Masyarakat Terkait Konflik Israel-Palestina Menggunakan Support Vector Machine,” Jipi (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), Vol. 9, No. 3, Pp. 1464–1472, Aug. 2024, Doi: 10.29100/Jipi.V9i3.5527.
E. R. Lidinillah, T. Rohana, And A. R. Juwita, “Analisis Sentimen Twitter Terhadap Steam Menggunakan Algoritma Logistic Regression Dan Support Vector Machine,” Teknosains : Jurnal Sains, Teknologi Dan Informatika, Vol. 10, No. 2, Pp. 154–164, Jul. 2023, Doi: 10.37373/Tekno.V10i2.440.
E. G. Radjah And A. C. Talakua, “Analisis Sentimen Komentar Terhadap Konten Tenun Ntt Di Youtube Menggunakan Metode Smote Dan Logistic Regression,” Transformatif, Vol. Xiii, No. 2, Pp. 84–94, 2024, Accessed: Feb. 27, 2025. [Online]. Available: Https://Ojs.Unkriswina.Ac.Id/Index.Php/Transformatif/Article/View/1005
R. Yusuf, K. Bahumatra, N. Komaria, E. A. Aqma, And L. Cahyani, “Analisis Sentimen Terhadap Aplikasi Google Meet Berdasarkan Komentar Pengguna Menggunakan Metode Logistic Regresion,” Jurnal Ilmiah Edutic : Pendidikan Dan Informatika, Vol. 11, No. 1, Pp. 53–64, Nov. 2024, Doi: 10.21107/Edutic.V11i1.28113.
J. Kajian Et Al., “Analisis Sentimen Di Twitter Terkait Tim Nasional Sepak Bola Indonesia Menggunakan Metode Support Vector Machine,” Jurnal Kajian Dan Terapan Matematika, Vol. 10, Pp. 96–104, Jul. 2024, Accessed: Feb. 27, 2025. [Online]. Available: Https://Journal.Student.Uny.Ac.Id/Jktm/Article/View/19561
M. I. Putri And I. Kharisudin, “Analisis Sentimen Pengguna Aplikasi Marketplace Tokopedia Pada Situs Google Play Menggunakan Metode Support Vector Machine (Svm), Naïve Bayes, Dan Logistic Regression,” Prisma, Prosiding Seminar Nasional Matematika, Vol. 5, Pp. 759–766, 2022, Accessed: Feb. 27, 2025. [Online]. Available: Https://Journal.Unnes.Ac.Id/Sju/Prisma/Article/View/54577
B. Setiawan, K. Ahmad Baihaqi, E. Nurlaelasari, And H. Hikmayanti Handayani, “Analisis Sentimen Ulasan Aplikasi Identitas Kependudukan Digital Menggunakan Algoritma Logistic Regression Dan K-Nearest Neighbor,” Technology And Science (Bits), Vol. 6, No. 1, Pp. 533–540, 2024, Doi: 10.47065/Bits.V6i1.5389.
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