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SENTIMENT ANALYSIS OF TWITTER (X) USERS TOWARD THE KOPERASI DESA MERAH PUTIH PROGRAM USING SUPPORT VECTOR MACHINE AND NAIVE BAYES METHODS

Fauzi Fauzi(1), Bertha Andini(2), Muhamad Muslihudin(3),


(1) Prodi Sistem Informasi, Institut Bakti Nusantara, Lampung
(2) Prodi Sistem Informasi, Institut Bakti Nusantara, Lampung
(3) Prodi Sistem Informasi, Institut Bakti Nusantara, Lampung
Corresponding Author

Abstract


The emergence of social media as a digital public sphere has opened significant opportunities to capture public opinion directly, including on social programs such as the Koperasi Desa Merah Putih. This program has attracted considerable attention on Twitter; however, public perception remains mixed, ranging from support and skepticism to criticism. This study aims to understand public sentiment through text analysis using two classification algorithms: Naive Bayes and Support Vector Machine (SVM). The results show that Naive Bayes performs better in balancing predictions, achieving an AUC of 0.71 and an F1-score of 0.62. In contrast, SVM, despite a slightly higher accuracy (0.69), was only effective in identifying the neutral class. This tendency indicates that Naive Bayes is more capable of capturing opinion variations in short and unstructured texts such as tweets. Furthermore, the sentiment distribution reveals a dominance of neutral opinions, suggesting the public's inclination to share factual information without explicit emotional expression.

Keywords


Public Sentiment; Social Media Analysis; Text Classification; Sentiment Distribution; Short Text Analytics.

References


A. Sholehah and A. S. Adinda, “Peran Media dalam Publisitas Politik Prabowo Subianto di TikTok,” --, vol. 5, no. 1, pp. 1426–1433, 2025.

T. Safitri, Y. Umaidah, and I. Maulana, “Analisis Sentimen Pengguna Twitter Terhadap Grup Musik BTS Menggunakan Algoritma Support Vector Machine,” J. Appl. Informatics Comput., vol. 7, no. 1, pp. 28–35, 2023.

A. Clairine, E. I. D. Lestari, E. N. Wiyono, and M. Wildan R, “Ekspresi Keresahan Pemuda melalui Media Sosial: Studi Kritik terhadap Narasi Indonesia Gelap,” Moderasi J. Stud. Ilmu Pengetah. Sos., vol. 6, no. 1, pp. 36–51, 2025.

O. Sembiring, E. Rulfin Tiara Kiu, and K. Nisa Meiah Ngafidin, “Analisis Sentimen Twitter Menggunakan Algoritma Naive Bayes Pada Kasus Koperasi Simpan Pinjam Indosurya,” Appl. Inf. Technol. Comput. Sci., vol. 2, no. 1, pp. 9–18, 2025.

A. Safira and F. N. Hasan, “Analisis Sentimen Masyarakat Terhadap Paylater Menggunakan Metode Naive Bayes Classifier,” Zo. J. Sist. Inf., vol. 5, no. 1, pp. 59–70, 2023.

M. I. Maulana, E. Budianita, M. Fikry, and F. Yanto, “Klasifikasi Sentiment Ulasan Aplikasi Sausage Man Menggunakan VADER Lexicon dan Naïve Bayes Classifier,” J. Sist. Komput. dan Inform., vol. 4, no. 3, pp. 485–492, 2023.

I. S. K. Idris, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine ( SVM ),” Jambura J. Electr. Electron. Eng., vol. 5, no. 1, pp. 32–35, 2023.

dan S. I. Rulin Swastika, Siti Mukodimah, Ferry Susanto, Muhamad Muslihudin, Implementasi Data Mining (Clastering, Association, Prediction, Estimation, Classification), 1st ed. Indramayu: CV. Adanu Abimata, 2023.

K. Rismayanti, Fera Damayanti, “Penerapan Data Mining Algoritma C4.55 Dalam Menentukan Rekam Jejak Kinerja Dosen STT Harapan Medan,” J. Penelit. Tek. Inform., vol. 3, no. 1, pp. 99–104, 2018.

M. Muslihudin, “Analisis Prediksi Mahasiswa Tidak Tepat Waktu Menyelesaikan Studi Dengan Menggunakan Metode Algoritma C 4.5 (Studi Kasus: STMIK Pringsewu),” IBI Darmajaya, 2015.

D. F. Rahman, “Analisis ChatGPT tweet menggunakan EDA dan sentiment analysis: Data pengguna Twitter di Indonesia.” 2023.

A. Suharsimi, Prosedur Penelitian Suatu Pensekatan Praktek. Jakarta: Rineka Cipta, 1998.

S. Arikunto, PROSEDUR PENELITIAN Suatu Pendekatan Praktik Edisi Revisi 2010. Jakarta: Rineka Cipta, 2010.

S. B. Imandoust and M. Bolandraftar, “Application of K-Nearest Neighbor ( KNN ) Approach for Predicting Economic Events : Theoretical Background,” Int. J. Eng. Res. Appl., vol. 3, no. 5, pp. 605–610, 2013.

R. Syahputra, G. J. Yanris, and D. Irmayani, “SVM and Naïve Bayes Algorithm Comparison for User Sentiment Analysis on Twitter,” Sinkron, vol. 7, no. 2, pp. 671–678, 2022.

W. E. N. Fenghua, X. Jihong, H. E. Zhifang, and G. Xu, “Stock Price Prediction Based on SSA and SVM,” Procedia - Procedia Comput. Sci., vol. 31, pp. 625–631, 2014.

H. Yu, R. Chen, and G. Zhang, “A SVM Stock Selection Model within PCA,” Procedia - Procedia Comput. Sci., vol. 31, pp. 406–412, 2014.

D. Eko, H. Pramono, and F. K. Ikhsan, “Pemodelan Enterprise Architecture Untuk Menentukan Sistem Informasi Di Perguruan Tinggi Menggunakan Metode Eap ( Studi Kasus : Perguruan Tinggi Dharma Wacana Metro ),” vol. XIX, no. April, pp. 434–438, 2020.

B. A. Maulana, M. J. Fahmi, A. M. Imran, and N. Hidayati, “Analisis Sentimen Terhadap Aplikasi Pluang Menggunakan Algoritma Naive Bayes dan Support Vector Machine (SVM),” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 2, pp. 375–384, 2024.

T. Mustaqim, “Sentiment Analysis Opini Pelantikan Kabinet Pemerintah Indonesia Tahun 2019 Menggunakan Vader Dan Random Forest,” Universitas Negeri Semaran, 2020.


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DOI: 10.56327/jurnaltam.v16i1.1834

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