Analisis Sentimen Masyarakat Terhadap Rencana Kenaikan PPN 12% Di Indonesia Pada Media Sosial X Menggunakan Metode Decision Tree
- Intan Diah Hardyatman Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, Indonesia
-
Firman Noor Hasan
*
Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta,
Indonesia
(*) Corresponding Author
Abstract
This study analyzes public sentiment towards the planned increase of Value Added Tax (VAT) to 12% in Indonesia using data from X social media. The VAT hike could trigger an increase in overseas spending and higher prices for products and services in Indonesia, potentially reducing sales and weakening industries. This proposal also received widespread attention on social media X. The VAT increase plan has pros and cons, triggering many discussions on social media. The Decision Tree classification method was used to process the data obtained through crawling and text preprocessing. This research compares 80% training data and 20% test data consisting of 1000 data, with details of 285 negative sentiments and 715 positive sentiments in the dataset. In this case, it can be described that X social media users towards the plan to increase VAT by 12% in Indonesia tend to be positive. This research aims to analyze people's sentiment towards the plan to increase VAT by 12% in Indonesia using Decision Tree and identify factors that influence the sentiment. The results of the analysis show that Decision Tree succeeded in increasing the accuracy by 81.34% of sentiment classification compared to previous methods, such as Naïve Bayes with an accuracy rate of 63.1%. The results of this study are expected to help the government in a more responsive fiscal policy.
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References
P. J. Siagian, “Analisis Sentimen Masyarakat Indonesia Terhadap Rencana Kenaikan PPN Menjadi 12% di Media Sosial X Dengan Metode Naïve Bayes,” Semin. Nas. Mhs. Fak. Teknol. Inf., vol. 3, no. 2, pp. 779–786, 2024, [Online]. Available: https://senafti.budiluhur.ac.id/index.php/senafti/index
Novi, M. Alkautsar, Y. Suryaman, and F. F. Roji, “Pelabelan VADER Dalam Menganalisis Persepsi Masyarakat Terhadap Kenaikan Tarif PPN di Indonesia,” J. Akunt. DAN Keuang., vol. 12, no. 2, pp. 228–238, 2024, [Online]. Available: https://ojs.unimal.ac.id/index.php/jak/index%0APelabelan
M. C. Kwan and B. Sarjono, “Dampak Kenaikan Tarif Pajak Pertambahan Nilai Pada Perilaku Konsumen di Indonesia,” J. Adijaya Multidisplin, vol. 2, no. 03, pp. 338–348, 2024, [Online]. Available: https://e-journal.naureendigition.com/index.php/mj
N. Istiqomah and F. Novika, “Sentiment Analysis Penyedia layanan Asuransi dari Media Sosial Twitter,” J. Tekno Kompak, vol. 18, no. 1, p. 77, 2024, doi: 10.33365/jtk.v18i1.3465.
M. Zania Maharani, “Analisis Sentimen Positif Terhadap Avoskin sebagai Eco Friendly Brand di Media Sosial X dan TikTok,” Filos. Publ. Ilmu Komunikasi, Desain, Seni Budaya, vol. 1, no. 3, pp. 125–140, 2024, [Online]. Available: https://doi.org/10.62383/filosofi.v1i3.169
L. A. Pramesti and N. Pratiwi, “Analisis Sentimen Twitter Terhadap Program MBKM Menggunakan Decision Tree dan Support Vector Machine,” J. Inf. Syst. Res., vol. 4, no. 4, pp. 1145–1154, 2023, doi: 10.47065/josh.v4i4.3807.
R. Harahap, Eva Darwisah Kurniawan, “Analisis Sentimen Komentar Terhadap Kebijakan Pemerintah Mengenai Tabungan Perumahan Rakyat (TAPERA) Pada Aplikasi X Menggunakan Metode Naïve Bayes,” J. Tek. Inform. Unika ST. Thomas, vol. 9, no. 1, pp. 2657–1501, 2024, [Online]. Available: https://ejournal.ust.ac.id/index.php/JTIUST/article/view/3911
A. Adi et al., “Analisis Sentimen Ulasan pada Maskapai Penerbangan Virgin America Menggunakan Metode Decision Tree,” in SEMINAR NASIONAL AMIKOM SURAKARTA (SEMNASA) 2024, 2024, pp. 392–401.
A. Fatkhudin, F. A. Artanto, N. A. Safli, and ..., “Decision Tree Berbasis SMOTE Dalam Analisis Sentimen Penggunaan Artificial Intelligence Untuk Skripsi,” REMIK Ris. dan E …, vol. 8, no. 2, pp. 494–505, 2024, [Online]. Available: https://www.jurnal.polgan.ac.id/index.php/remik/article/view/13531%0Ahttps://www.jurnal.polgan.ac.id/index.php/remik/article/download/13531/2453
I. Zulfahmi, J. Williem Iskandar, P. V Medan, S. Tuan, and D. Serdang, “Analisis Sentimen Aplikasi PLN Mobile Menggunakan Metode Decission Tree,” J. Penelit. Rumpun Ilmu Tek., vol. 3, no. 1, pp. 11–21, 2024, [Online]. Available: https://doi.org/10.55606/juprit.v3i1.3096
A. R. I. Waldy Romadhona, “Analisis Sentimen Pengguna Media Sosial Terhadap Identitas Kependudukan Digital Menggunakan Metode Support Vector Machine (SVM),” J. Inf. Syst. …, vol. 9, no. 4, pp. 2185–2195, 2024, [Online]. Available: http://ejurnal.seminar-id.com/index.php/josh/article/view/5614%0Ahttps://ejurnal.seminar-id.com/index.php/josh/article/download/5614/2906
J. S. Gea and H. Budiati, “Analisis Sentimen Masyarakat Terhadap Direktorat Jenderal Pajak,” J. Sains Dan Komput., vol. 8, no. 01, pp. 30–36, 2024, doi: 10.61179/jurnalinfact.v8i01.466.
F. N. Hasan and M. Dwijayanti, “Analisis Sentimen Ulasan Pelanggan Terhadap Layanan Grab Indonesia Menggunakan Multinominal Naïve Bayes Classifier,” J. Linguist. Komputasional, vol. 4, no. 2, pp. 52–58, 2021, doi: https://doi.org/10.26418/jlk.v4i2.61.
T. Setiawan, S. Liem, and D. M. R. Pribadi, “Perbandingan Algoritma SVM dan Naïve Bayes dalam Analisis Sentimen Komentar Tiktok pada Produk Skincare,” Appl. Inf. Technol. Comput. Sci., vol. 3, no. 2, pp. 28–32, 2024, [Online]. Available: https://jurnal.politap.ac.id/index.php/aicoms
Maharani and Fathoni, “Analisis Sentimen Pengguna Terhadap Faktor Penggunaan PayPal Menggunakan Metode Decision Tree,” J. Ilm. Teknol. Inf. Asia, vol. 18, no. 1, pp. 71–83, 2024, doi: 10.32815/jitika.v18i1.1002.
R. Cantika Larasati, C. Dewi, and C. H. Juli, “Analisis sentimen produk kecantikan jenis moisturizer di twitter menggunakan algoritma super vector machine,” Tekinkom, vol. 7, no. 1, pp. 124–134, 2024, doi: 10.37600/tekinkom.v7i1.1243.
A. Sentimen, “Perbandingan Pelabelan Data dalam Analisis Sentimen Kurikulum Proyek di platform TikTok : Pendekatan Naïve Bayes,” J. EKSPLORA Inform., vol. 14, no. 1, pp. 96–107, 2024, doi: 10.30864/eksplora.v14i1.1093.
A. M. Hilda et al., “Metode Klasifikasi Support Vector Machine ( SVM ) Untuk Analisis Sentimen Aplikasi Bing : Chat with AI & GPT-4 Di Google Play Store,” J. Pengkaj. dan Penerapan Tek. Inform., vol. 17, no. 1, pp. 68–76, 2024, [Online]. Available: https://jurnal.itpln.ac.id/petir/article/download/2283/1226/10752
H. Z. Muflih, A. R. Abdillah, and F. N. Hasan, “Analisis Sentimen Ulasan Pengguna Aplikasi Ajaib Menggunakan Metode Naïve Bayes,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 4, no. 3, pp. 1613–1621, 2023, doi: 10.30865/klik.v4i3.1303.
R. A. Pranata and N. A. Verdikha, “Metode Pembobotan TF-IDF untuk Klasifikasi Teks Quick Count Pemilihan Wakil Presiden Indonesia 2024 pada X Twitter dengan Metode SVM,” J. Teknol. Inf., vol. 18, no. 2, pp. 126–138, 2024, [Online]. Available: https://e-journal.upr.ac.id/index.php/JTI%0A126
A. H. Nasrullah, “Implementasi Algoritma Decision Tree Untuk Klasifikasi Data Peserta Didik,” J. Pilar Nusa Mandiri, vol. 7, no. 2, p. 217, 2021, [Online]. Available: http://ejournal.fikom-unasman.ac.id
A. Kartika Sari, Akhmad Irsyad, Dinda Nur Aini, Islamiyah, and Stephanie Elfriede Ginting, “Analisis Sentimen Twitter Menggunakan Machine Learning untuk Identifikasi Konten Negatif,” Adopsi Teknol. dan Sist. Inf., vol. 3, no. 1, pp. 64–73, 2024, doi: 10.30872/atasi.v3i1.1373.
K. Nurfebia, “Sentiment Analysis of Skincare Products Using the Naive Bayes Method,” vol. 6, no. 3, pp. 1663–1676, 2024, doi: 10.51519/journalisi.v6i3.817.
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