Analisis Sentimen Opini Masyarakat terhadap Film Horor Indonesia Menggunakan Metode SVM dan TF-IDF
Abstract
In the digital era, public opinions regarding Indonesian horror films often receive significant attention on social media platforms. The objective of this study is to assess the perspectives and sentiments expressed by the public concerning Indonesian horror films. The Support Vector Machine (SVM) method along with Term Frequency-Inverse Document Frequency (TF-IDF) is utilized as the primary analytical tool in this research. Issues and public viewpoints regarding horror films are extracted from social media platforms to identify both pros and cons. Employing the SVM method in conjunction with text representation using TF-IDF is expected to provide comprehensive insights into the emotional responses of the public toward specific film genres. The analyzed dataset comprises 2281 entries. Subsequently, the application of the Support Vector Machine (SVM) algorithm is employed for text classification, coupled with word weighting using TF-IDF. The outcomes of this analysis exhibit an accuracy of 82.51%, a precision of 5.28%, a recall of 7.26%, and an F1 Score of 6.12%. This study demonstrates the efficacy of the SVM and TF-IDF methods in classifying public sentiment toward Indonesian horror films and has the potential to provide insights into the social impact and reception of film works within the entertainment industry.
References
Razaq. Thaariq, “Analisis Sentimen Review Film Menggunakan Naive Bayes Classifier Dengan Fitur TF-IDF,” Bandung, Apr. 2023.
Y. Nurtikasari, Syariful Alam, and Teguh Iman Hermanto, “Analisis Sentimen Opini Masyarakat Terhadap Film Pada Platform Twitter Menggunakan Algoritma Naive Bayes,” INSOLOGI: Jurnal Sains dan Teknologi, vol. 1, no. 4, pp. 411–423, Aug. 2022, doi: 10.55123/insologi.v1i4.770.
M. Alfi, R. Reynaldhi, and Y. Sibaroni, “Analisis Sentimen Review Film pada Twitter menggunakan Metode Klasifikasi Hybrid SVM, Naïve Bayes, dan Decision Tree.”
M. Asjad Adna Jihad and W. Astuti, “Analisis Sentimen Terhadap Ulasan Film Menggunakan Algoritma Random Forest.”
A. Z. Amrullah, A. Sofyan Anas, M. Adrian, and J. Hidayat, “Analisis Sentimen Movie Review Menggunakan Naive Bayes Classifier Dengan Seleksi Fitur Chi Square,” Jurnal, vol. 2, no. 1, 2020, doi: 10.30812/bite.v2i1.804.
N. Fitriyah, B. Warsito, D. Asih, and I. Maruddani, “ANALISIS SENTIMEN GOJEK PADA MEDIA SOSIAL TWITTER DENGAN KLASIFIKASI SUPPORT VECTOR MACHINE (SVM),” JURNAL GAUSSIAN, vol. 9, no. 3, pp. 376–390, 2020, [Online]. Available: https://ejournal3.undip.ac.id/index.php/gaussian/
V. W. D. Thomas and F. Rumaisa, “Analisis Sentimen Ulasan Hotel Bahasa Indonesia Menggunakan Support Vector Machine dan TF-IDF,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 6, no. 3, p. 1767, Jul. 2022, doi: 10.30865/mib.v6i3.4218.
O. I. Gifari, M. Adha, I. Rifky Hendrawan, F. Freddy, and S. Durrand, “Analisis Sentimen Review Film Menggunakan TF-IDF dan Support Vector Machine,” JIFOTECH (JOURNAL OF INFORMATION TECHNOLOGY, vol. 2, no. 1, 2022.
D. R. Alghifari, M. Edi, and L. Firmansyah, “Implementasi Bidirectional LSTM untuk Analisis Sentimen Terhadap Layanan Grab Indonesia,” Jurnal Manajemen Informatika (JAMIKA), vol. 12, no. 2, pp. 89–99, Sep. 2022, doi: 10.34010/jamika.v12i2.7764.
R. Wati, S. Ernawati, and H. Rachmi, “Pembobotan TF-IDF Menggunakan Naïve Bayes pada Sentimen Masyarakat Mengenai Isu Kenaikan BIPIH,” Jurnal Manajemen Informatika (JAMIKA), vol. 13, no. 1, pp. 84–93, Apr. 2023, doi: 10.34010/jamika.v13i1.9424.
A. Brijith, “Data Preprocessing for Machine Learning.” [Online]. Available: https://www.researchgate.net/publication/375003512
K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.
F. Fridom Mailo et al., “Analisis Sentimen Data Twitter Menggunakan Metode Text Mining Tentang Masalah Obesitas di Indonesia,” 2019.
D. Hazarika, G. Konwar, S. Deb, and D. J. Bora, “Sentiment Analysis on Twitter by Using TextBlob for Natural Language Processing,” in Proceedings of the International Conference on Research in Management & Technovation 2020, PTI, Jan. 2020, pp. 63–67. doi: 10.15439/2020km20.
S. A. Helmayanti, F. Hamami, and R. Y. Fa’rifah, “PENERAPAN ALGORITMA TF-IDF DAN NAÏVE BAYES UNTUK ANALISIS SENTIMEN BERBASIS ASPEK ULASAN APLIKASI FLIP PADA GOOGLE PLAY STORE,” Jurnal Indonesia : Manajemen Informatika dan Komunikasi, vol. 4, no. 3, pp. 1822–1834, Sep. 2023, doi: 10.35870/jimik.v4i3.415.
S. Khairunnisa, A. Adiwijaya, and S. Al Faraby, “Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19),” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 2, p. 406, Apr. 2021, doi: 10.30865/mib.v5i2.2835.
“Penggunaan_Algoritma_Support_Vector_Machine_SVM_Un”.
E. Hokijuliandy, H. Napitupulu, and F. Firdaniza, “Analisis Sentimen Menggunakan Metode Klasifikasi Support Vector Machine (SVM) dan Seleksi Fitur Chi-Square”.
M. Fadli and R. A. Saputra, “KLASIFIKASI DAN EVALUASI PERFORMA MODEL RANDOM FOREST UNTUK PREDIKSI STROKE Classification And Evaluation Of Performance Models Random Forest For Stroke Prediction,” vol. 12, [Online]. Available: http://jurnal.umt.ac.id/index.php/jt/index
M. Azhari, Z. Situmorang, and R. Rosnelly, “Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 2, p. 640, Apr. 2021, doi: 10.30865/mib.v5i2.2937.
