Pemodelan Text Mining untuk Analisis Sentimen Terhadap Program Makan Siang Gratis di Media Sosial X Menggunakan Algoritma Support Vector Machine (SVM)
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Sep 20, 2024
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The free lunch program is an initiative that provides free lunches and milk in schools and Islamic boarding schools, as well as providing nutritional support for children under five and pregnant women. Even though efforts to overcome stunting with this program are considered too late, providing free milk to pregnant women is considered more relevant for dealing with the stunting problem. Based on experience from various countries, the free lunch program shows various positive impacts, including increasing children's nutritional intake, increasing school participation, developing student character, and supporting the family and national economy. Discussions about this program received various responses from netizens, including platform X users. Online platforms have an important role in reaching and disseminating information about various social programs. In this context, the support vector machine method can be used to classify netizen responses to the free lunch program into positive and negative categories. It is hoped that this analysis will provide a deeper understanding of how this program is received by the general public, as well as provide suggestions for policy makers in designing social programs in the future.
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Purwanti, Z., & Sugiyono. (2024). Pemodelan Text Mining untuk Analisis Sentimen Terhadap Program Makan Siang Gratis di Media Sosial X Menggunakan Algoritma Support Vector Machine (SVM). Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 3065-3079. https://doi.org/10.35870/jimik.v5i3.1001
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Ayumi, V. (2023). Analisa Sentimen Mengenai Kenaikan Harga Bbm Menggunakan Metode Naïve Bayes Dan Support Vector Machine. JSAI (Journal Scientific and Applied Informatics), 6(1), 1-10. DOI: https://doi.org/10.36085/jsai.v6i1.4628.
Buana, R. S., Gata, W., Widodo, A. Z. P., Setiawan, H., & Hilyati, K. (2023). Analisis Sentimen pada Komen Twitter Pawang Hujan Mandalika dengan Support Vector Machine (SVM) dan Naïve Bayes. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 7(2), 194-200. DOI: https://doi.org/10.35870/jtik.v7i2.705.
Effendi, L., & Syukmana, F. (2022). Analisa sentimen vaksinasi covid-19 dengan metode support vector machine dan naïve bayes berbasis teknik SMOTE.
Fikri, M. I., Sabrila, T. S., & Azhar, Y. (2020). Perbandingan metode naïve bayes dan support vector machine pada analisis sentimen twitter. SMATIKA Jurnal: STIKI Informatika Jurnal, 10(02), 71-76. DOI: https://doi.org/10.32664/smatika.v10i02.455.
Handayanto, R. T., Herlawati, H., Atika, P. D., Khasanah, F. N., Yusuf, A. Y. P., & Septia, D. Y. (2021). Analisis Sentimen Pada Situs Google Review dengan Naïve Bayes dan Support Vector Machine. Jurnal Komtika (Komputasi dan Informatika), 5(2), 153-163.
Indriyani, F. A., Fauzi, A., & Faisal, S. (2023). Analisis sentimen aplikasi tiktok menggunakan algoritma naïve bayes dan support vector machine. TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika, 10(2), 176-184. DOI: https://doi.org/10.37373/tekno.v10i2.419.
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Mufidah, F. S., Winarno, S., Alzami, F., Udayanti, E. D., & Sani, R. R. (2022). Analisis Sentimen Masyarakat terhadap Layanan Shopeefood Melalui Media Sosial Twitter dengan Algoritma Naïve Bayes Classifier. vol, 7, 14-25.
Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter. J-SAKTI (Jurnal Sains Komputer dan Informatika), 5(2), 697-711. DOI: http://dx.doi.org/10.30645/j-sakti.v5i2.369.
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Oktavia, D., Ramadahan, Y. R., & Minarto, M. (2023). Analisis Sentimen Terhadap Penerapan Sistem E-Tilang Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (SVM). KLIK: Kajian Ilmiah Informatika dan Komputer, 4(1), 407-417. DOI: https://doi.org/10.30865/klik.v4i1.1040.
Qoiriah, A., Harimurti, R., & Nurhidayat, A. I. (2020, November). Application of k-means algorithm for clustering student’s computer programming performance in automatic programming assessment tool. In International Joint Conference on Science and Engineering (IJCSE 2020) (pp. 421-425). Atlantis Press. DOI: 10.2991/aer.k.201124.075.
Raharjo, R. A., Sunarya, I. M. G., & Divayana, D. G. H. (2022). Perbandingan Metode Naïve Bayes Classifier Dan Support Vector Machine Pada Kasus Analisis Sentimen Terhadap Data Vaksin Covid-19 Di Twitter. Elkom: Jurnal Elektronika dan Komputer, 15(2), 456-464. DOI: https://doi.org/10.51903/elkom.v15i2.918.
Rahman, H. A., Santoso, R., & Widiharih, T. (2023). Analisis Sentimen Pada Perusahaan Penyedia Jasa Logistik J&T Menggunakan Algoritma Multinomial Naive Bayes dan Support Vector Machine. Jurnal Gaussian, 12(2), 242-253. DOI: https://doi.org/10.14710/j.gauss.12.2.242-253.
Sari, B. W., & Haranto, F. F. (2019). Implementasi Support Vector Machine Untuk Analisis Sentimen Pengguna Twitter Terhadap Pelayanan Telkom Dan Biznet. Jurnal Pilar Nusa Mandiri, 15(2), 171-176. DOI: https://doi.org/10.33480/pilar.v15i2.699.
Sarimole, F. M., & Ihsan, A. N. (2023). Analisis Sentimen Twitter Terhadap Uu Cipta Kerja Dengan Menggunakan Algoritma Naïve Bayes Dan Support Vector Machine. INTECOMS: Journal of Information Technology and Computer Science, 6(2), 822-829. DOI: https://doi.org/10.31539/intecoms.v6i2.7671.
Setiawan, H., Utami, E., & Sudarmawan, S. (2021). Analisis Sentimen Twitter Kuliah Online Pasca Covid-19 Menggunakan Algoritma Support Vector Machine dan Naive Bayes. Jurnal Komtika (Komputasi dan Informatika), 5(1), 43-51. DOI: https://doi.org/10.31603/komtika.v5i1.5189.
Sujadi, H. (2022). Analisis Sentimen Pengguna Media Sosial Twitter Terhadap Wabah Covid-19 Dengan Metode Naive Bayes Classifier Dan Support Vector Machine. INFOTECH journal, 8(1), 22-27. DOI: https://doi.org/10.31949/infotech.v8i1.1883.
Widyanto, T., Ristiana, I., & Wibowo, A. (2023). Komparasi Naïve Bayes dan SVM Analisis Sentimen RUU Kesehatan di Twitter. SINTECH (Science and Information Technology) Journal, 6(3), 147-161. DOI: https://doi.org/10.31598/sintechjournal.v6i3.1433.
Wulandari, S., & Hasan, F. N. (2024). Analisis Sentimen Masyarakat Indonesia Terhadap Pengalaman Belanja Thrifting Pada Media Sosial Twitter Menggunakan Algoritma Naïve Bayes. Jurnal Media Informatika Budidarma, 8(2), 768-776. DOI: http://dx.doi.org/10.30865/mib.v8i2.7520.
Zaman, F. N., Fadhilah, M. A., Ulinuha, M. A., & Umam, K. (2024). MENGANALISIS RESPONS NETIZEN TWITTER TERHADAP PROGRAM MAKAN SIANG GRATIS MENERAPKAN NLP METODE NAIVE BAYES. Just IT: Jurnal Sistem Informasi, Teknologi Informasi dan Komputer, 14(3), 201-208. DOI: https://doi.org/10.24853/justit.14.3.201-208.