Analisis Sentimen Pengguna X (Twitter) Terhadap Kebijakan Tapera Di Indonesia Menggunakan Metode CNN Dan BERT
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Abstract
The government’s Housing Savings Program (TAPERA) has sparked various public reactions, particularly on social media platform X (Twitter). This study aims to analyze user sentiment toward the TAPERA policy using the Convolutional Neural Network (CNN) and Bidirectional Encoder Representations from Transformers (BERT) methods. The dataset was collected using a crawling technique on X (Twitter), comprising a total of 1,790 tweets. These data were processed through preprocessing stages, including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. The CNN and BERT models were then trained and tested to classify sentiments as positive or negative. The findings indicate that the BERT model outperformed CNN, achieving a higher accuracy of 86% compared to CNN’s 85%, along with superior recall, precision, and F1-score values. These results suggest that the BERT method is more effective in comprehensively understanding sentiment context.
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N. Adawiyah, J. A. Sihombing, S. Mar’ah, and P. Kemala, “Analisis kebijakan perumahan subsidi di indonesia policy analysis 0f subsidized housing in indonesia,” El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam, vol. 5, no. 6, pp. 3064–3075, 2024.
Y. M. De, “Imam dan dialog sosialnya (analisis kritis program tapera “tabungan perumahan rakyat” bagi kehidupan umat di paroki riam batang kalimantan tengah),” Jurnal Pendidikan Agama dan Teologi, vol. 2, no. 3, pp. 57–73, 2024.
A. H. Alfrida, E. M. Sasea, and A. Suryana, “Legal challenges of tapera implementation in indonesia: Toward an equitable and effective housing financing scheme,” Legalis: Journal of Law Review, vol. 2, no. 2, pp. 62–71, 2024.
L. D. Mahbubah and E. Zuliarso, “Analisa sentimen twitter pada pilpres 2019 menggunakan algoritma naive bayes,” 2019.
G. Y. Sitio, S. A. Rumapea, and P. Lumbanraja, “Analisis sentimen pemindahan ibu kota negara di media sosial twitter menggunakan metode convolutional neural network (cnn),” METHOTIKA: Jurnal Ilmiah Teknik Informatika, vol. 3, no. 2, pp. 97–104, 2023.
R. M. R. W. P. Kusuma, W. Yustanti, et al., “Analisis sentimen customer review aplikasi ruang guru dengan metode bert (bidirectional encoder representations from transformers),” Journal of Emerging Information System and Business Intelligence (JEISBI), vol. 2, no. 3, 2021.
B. Liu, Sentiment analysis and opinion mining. Springer Nature, 2022.
P. W. Ratiasasadara, S. Sudarno, and T. Tarno, “Analisis sentimen penerapan ppkm pada twitter menggunakan naive bayes classifier dengan seleksi fitur chi-square,” Jurnal Gaussian, vol. 11, no. 4, pp. 580–590, 2023.
M. N. Humam, “Perbandingan kinerja cnn dan naïve bayes pada analisis sentimen performa manchester united di twitter,” JIEET (Journal of Information Engineering and Educational Technology), vol. 7, no. 2, pp. 83–91, 2023.
C. Ariningdyah, D. Lasonda, and F. R. D. Miarsa, “Analisis yuridis penerapan tabungan perumahan rakyat (tapera) dalam perspektif asas keadilan,” Innovative: Journal Of Social Science Research, vol. 4, no. 3, pp. 18410–18424, 2024.
O. Oueslati, E. Cambria, M. B. HajHmida, and H. Ounelli, “A review of sentiment analysis research in arabic language,” Future Generation Computer Systems, vol. 112, pp. 408–430, 2020.
S. I. Nurhafida and F. Sembiring, “Analisis text clustering masyarakat di twiter mengenai mcdonald’sxbts menggunakan orange data mining,” in Prosiding Seminar Nasional Sistem Informasi Dan Manajemen Informatika Universitas Nusa Putra, vol. 1, pp. 28–35, 2021.
S. Imron, E. I. Setiawan, J. Santoso, M. H. Purnomo, et al., “Aspect based sentiment analysis marketplace product reviews using bert, lstm, and cnn,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 3, pp. 586–591, 2023.
H. Tsaniya, R. Rosadi, and A. Abdullah, “Sentiment analysis towards jokowis government using twitter data with convolutional neural network method,” in Journal of Physics: Conference Series, vol. 1722, p. 012017, IOP Publishing, 2021.
S. Setyani and Y. Sibaroni, “Multi aspect sentiment analysis of mutual funds investment app bibit using bert method,” International Journal on Information and Communication Technology (IJoICT), vol. 9, no. 1, pp. 44–56, 2023.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
D. Putra and A. Wibowo, “Prediksi keputusan minat penjurusan siswa sma yadika 5 menggunakan algoritma naïve bayes,” in Prosiding Seminar Nasional Riset Information Science (SENARIS), vol. 2, pp. 84–92, 2020.