ANALISIS PROGRAM MAKAN BERGIZI GRATIS DENGAN SUPPORT VECTOR MACHINE (SVM) PADA APLIKASI X

Authors

  • Sekar Cinta Amaria Universitas Semarang
  • Nurtriana Hidayati Universitas Semarang

DOI:

https://doi.org/10.30656/jsii.v12i2.10708

Abstract

The Free Nutritious Meal Program (MBG), which represents a priority program of President Prabowo Subianto, has garnered widespread attention from Indonesian society. This program has received sympathy from various groups, including students and informal workers, and has been extensively discussed through social media, particularly on platform X. This research aims to analyze public response in the form of positive and negative sentiment toward the MBG Program based on data from platform X. A total of 1,378 tweets were collected using crawling methods, followed by preprocessing, sentiment labeling using the InSet lexicon dictionary, and feature extraction using three techniques: Term Presence, Bag of Words (BoW), and Term Frequency-Inverse Document Frequency (TF-IDF). Subsequently, sentiment classification was performed using the Support Vector Machine (SVM) algorithm for each feature extraction technique. Classification results demonstrate that the TF-IDF technique achieved the highest accuracy of 77.5%, compared to Term Presence (76.2%) and BoW (75.3%). Validation using K-Fold Cross Validation with five iterations was conducted with imbalanced data handling through the Synthetic Minority Over-sampling Technique (SMOTE) method. In this validation, TF-IDF consistently demonstrated superior performance with an average accuracy of 75.54%, precision of 74.31%, recall of 73.86%, and f1-score of 73.98%. Despite a slight decrease in accuracy following data synthesis, the TF-IDF technique proved to be stable and effective in handling data variation. The superiority of the TF-IDF feature extraction technique is suitable for combination with the SVM algorithm.

Keywords: Free Nutritious Meal Program, SVM algorithm, TF-IDF, Platform X, SMOTE Method

References

1] Tundo and D. N. Rachmawati, “Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Terhadap Program Makan Siang Gratis,” J. Indones. Manaj. Inform. dan Komun., vol. 5, no. 3, pp. 2925–2939, 2024.

[2] M. Ilham and B. Priambodo, “Analisis Sentimen Publik Terhadap Program Makan Siang Gratis Menggunakan BERT Neural Network Pada Platform X,” JEMSI J. Ekon. Manahemen Sist. Inf., vol. 6, no. 2, pp. 1039–1047, 2024.

[3] E. Y. Hidayat, R. W. Hardiansyah, and Affandy, “Analisis Sentimen Twitter untuk Menilai Opini Terhadap Perusahaan Publik Menggunakan Algoritma Deep Neural Network,” J. Nas. Teknol. dan Sist. Inf., vol. 02, no. 2, pp. 108–118, 2021.

[4] J. Anggraini and D. Alita, “Implementasi Metode SVM Pada Sentimen Analisis Terhadap Pemilihan Presiden ( Pilpres ) 2024 Di Twitter,” J. Inform. J. Pengemb. IT, vol. 9, no. 2, pp. 102–111, 2024, doi: 10.30591/jpit.v9i2.6560.

[5] Z. Jianqiang, G. U. I. Xiaolin, and Z. Xuejun, “Deep Convolution Neural Networks for Twitter Sentiment Analysis,” IEEE Access, vol. 6, pp. 23253–23260, 2018, doi: 10.1109/ACCESS.2017.2776930.

[6] D. T. Attaulah and D. Soyusiawaty, “Analisis Sentimen Program Makan Siang Gratis di Twitter / X menggunakan Metode BI-LSTM,” Edumatic J. Pendidik. Inform., vol. 9, no. 1, pp. 294–303, 2025, doi: 10.29408/edumatic.v9i1.29725.

[7] R. N. Muhammad, L. W. S, and B. Tanggahma, “Pengaruh Media Sosial Pada Persepsi Publik Terhadap Sistem Peradilan : Analisis Sentimen di Twitter,” UNES LAW Rev., vol. 7, no. 1, pp. 507–516, 2024, doi: https://doi.org/10.31933/unesrev.v7i1.

[8] T. R. Gatla, “A Groundbreaking Research in Breaking Language Barriers : NLP And Linguistics Development,” Int. J. Adv. Res. Interdiscip. Sci. Endeav., vol. 1, no. 1, pp. 1–7, 2024, doi: 10.61359/11.2206-2401.

[9] A. Monika, L. Toruan, B. M. Panjaitan, E. Malum, and K. Tumangger, “Penggunaan NLP dalam Analisis Sentimen untuk Meningkatkan Kepuasan Pelanggan pada Pengguna E-commerce : Lazada,” SAINTEK J. Sains, Teknol. Komput., vol. 1, no. 1, pp. 18–20, 2024.

[10] A. Ramadhani, I. Permana, M. Afdal, and M. Fronita, “Analisis Sentimen Tanggapan Publik di Twitter Terkait Program Kerja Makan Siang Gratis Prabowo – Gibran Menggunakan Algoritma Naïve Bayes Classifier dan Support Vector Machine,” Build. Informatics, Technol. Sci., vol. 6, no. 3, pp. 1509–1516, 2024, doi: 10.47065/bits.v6i3.6188.

[11] P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, pp. 147–156, 2021, doi: 10.25126/jtiik.202183944.

[12] O. Manullang, C. Prianto, and N. H. Harani, “Analisis Sentimen Untuk Memprediksi Hasil Calon Pemilu Presiden Menggunakan Lexicon Based dan Random Forest,” no. 54, 2023.

[13] K. Taha, “Semi-supervised and un-supervised clustering: A review and experimental evaluation,” Inf. Syst., vol. 114, p. 102178, 2023, doi: https://doi.org/10.1016/j.is.2023.102178.

[14] M. Daffa, A. Fahreza, A. Luthfiarta, M. Rafid, M. Indrawan, and A. Nugraha, “Analisis Sentimen : Pengaruh Jam Kerja Terhadap Kesehatan Mental Generasi Z,” J. Appl. Comput. Sci. Technol. ( JACOST ), vol. 5, no. 1, pp. 16–25, 2024.

[15] I. K. N. Ananda, N. Putu, N. Puspa, N. W. Marti, L. Joni, and E. Dewi, “Klasifikasi Multilabel Pada Gaya Belajar Siswa Sekolah Dasar Menggunakan Algoritma Machine Learning,” J. Appl. Comput. Sci. Technol. ( JACOST ), vol. 5, no. 2, pp. 144–154, 2024.

[16] P. H. Prastyo, A. S. Sumi, A. W. Dian, and A. E. Permanasari, “Tweets Responding to the Indonesian Government ’ s Handling of COVID-19 : Sentiment Analysis Using SVM with Normalized Poly Kernel,” J. Inf. Syst. Eng. Bus. Intell., vol. 6, no. 2, pp. 112–122, 2020.

[17] S. A. Nugraha, “Penerapan Lexicon Based Untuk Analisis Sentimen Masyarakat Indonesia Terhadap Danantara,” JATI (Jurnal Mhs. Tek. Inform., vol. 9, no. 3, pp. 4949–4957, 2025.

[18] M. F. Firdaus, D. E. Ratnawati, and N. Y. Setiawan, “Analisis Sentimen Berbasis Aspek Pada Ulasan Pelanggan Restoran Menggunakan Algoritma Support Vector Machine (Studi Kasus : Depot Bamara),” J. Teknol. Inf. dan Ilmu Komput., vol. 11, no. 6, pp. 1265–1272, 2024, doi: 10.25126/jtiik.2024117564.

[19] W. F. Abdillah, “Analisis Sentimen Penanganan Covid-19 Dengan Support Vector Machine : Evaluasi Leksikon Dan Metode Ekstraksi Fitur,” J. Ilm. Intech Inf. Technol. J. UMUS, vol. 3, no. 2, pp. 160–170, 2021.

[20] N. L. P. R. Dewi, I. N. S. W. Wijaya, I. K. Purnamawan, and N. W. Marti, “Model Classifier Judul Berita Pariwisata Indonesia,” J. Teknol. Inf. dan Ilmu Komput., vol. 11, no. 1, pp. 117–124, 2024, doi: 10.25126/jtiik.20241117617.

[21] R. A. Husen, S. Erlinda, and T. Perumal, “Sentiment Analysis of Societal Attitudes Toward the Childfree Lifestyle Using Latent Dirichlet Allocation ( LDA ) and Support Vector Machines ( SVM ),” Innov. Res. Informatics ( Innov. ), vol. 1, pp. 1–8, 2025.

[22] F. M. Lubis and M. Ikhsan, “Analisis Sentimen Terhadap Program Kampanye Tabrak Prof Pada Media Sosial X Dengan Menggunakan Metode Support Vector Machine,” J. Sist. Inf., vol. 12, no. 1, pp. 86–92, 2025, doi: 10.30656/jsii.v11i2.9065.

[23] Suryanto and W. Andriyani, “Sentiment Analysis of X Platform on Viral ’ Fufufafa ’ Account Issue in Indonesia Using SVM,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 19, no. 1, pp. 95–104, 2025.

[24] K. Bagaskara, H. Perdana, and S. Aprizkiyandari, “Penerapan Support Vector Regression Kernel Linier,” Equator J. Math. Stat. Sci., vol. 3, no. 2, pp. 74–83, 2024.

[25] D. R. Nurqotimah, A. N. Khudori, and R. S. Pradini, “Implementasi Algoritma Support Vector Machine ( SVM ) Untuk Klasifikasi Penyakit Stroke,” J. Appl. Comput. Sci. Technol. ( JACOST ), vol. 5, no. 2, pp. 179–185, 2024.

[26] N. Fajriyah, N. T. Lapatta, D. W. Nugraha, and R. Laila, “Implementasi svm dan smote pada analisis sentimen media sosial x terhadap pelantikan agus harimurti yudhoyono,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 10, no. 2, pp. 1359–1370, 2025.

[27] R. R. R. Arisandi, B. Warsito, and A. R. Hakim, “Aplikasi naïve bayes classifier (nbc) pada klasifikasi status gizi balita stunting dengan pengujian k-fold cross validation,” J. GAUSSIAN, vol. 11, pp. 130–139, 2022.

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Published

2025-09-07

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