Analisis Sentimen Publik Terhadap Kenaikan Pajak PPN di Indonesia Tahun 2024 Menggunakan Algoritma Machine Learning: Analisis Sentimen

Mhd Arief  Hasan; Novia Bimby

doi:10.37859/jf.v15i1.8556

Authors

Mhd Arief Hasan Universitas Lancang Kuning
Novia Bimby Universitas Lancang Kuning

DOI:

https://doi.org/10.37859/jf.v15i1.8556

Keywords: analisis sentimen, tweet, logistic regression, naive bayes, support vector machine

Abstract

Kebijakan kenaikan Pajak Pertambahan Nilai (PPN) sebesar 12% di Indonesia telah memicu beragam reaksi masyarakat, terutama di media sosial seperti Twitter. Permasalahan dalam penelitian ini terletak pada pentingnya memahami persepsi publik terhadap kebijakan tersebut secara otomatis dan akurat, yang menjadi tantangan tersendiri dalam pengolahan data teks. Penelitian ini bertujuan untuk mengevaluasi dan membandingkan kinerja tiga algoritma klasifikasi machine learning populer, yaitu Logistic Regression, Naive Bayes, dan Support Vector Machine (SVM), dalam menganalisis sentimen publik dari data tweet terkait kebijakan kenaikan PPN. Proses analisis melibatkan tahapan preprocessing, transformasi teks menggunakan TF-IDF, serta evaluasi menggunakan metrik akurasi dan f1-score. Hasil penelitian menunjukkan bahwa algoritma SVM memberikan performa terbaik dengan akurasi sebesar 82,24%, diikuti oleh Logistic Regression (81,15%) dan Naive Bayes (74,63%). Temuan ini menunjukkan bahwa SVM lebih efektif dalam membedakan sentimen positif, negatif, dan netral pada teks media sosial, serta dapat dijadikan algoritma yang direkomendasikan untuk tugas analisis sentimen serupa di masa mendatang.

Downloads

Download data is not yet available.

References

I. Syahrohim, S. D. Saputra, R. W. Saputra, V. H. Pranatawijaya, and R. Priskila, “Perbandingan Analisis Sentimen Setelah Pilpres 2024 Di Twitter Menggunakan Algoritma Machine Learning,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 2, 2024, doi: 10.23960/jitet.v12i2.4249.

R. Bhaskaran et al., “Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification,” Computer Systems Science and Engineering, vol. 44, no. 1, pp. 235–247, 2022, doi: 10.32604/csse.2023.024399.

A. P. Giovani, A. Ardiansyah, T. Haryanti, L. Kurniawati, and W. Gata, “Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi,” Jurnal Teknoinfo, vol. 14, no. 2, p. 115, 2020, doi: 10.33365/jti.v14i2.679.

R. Rita and P. Astuty, “Dampak Kenaikan Tarif Kenaikan Ppn Terhadap Inflasi,” Remittance: Jurnal Akuntansi Keuangan Dan Perbankan, vol. 4, no. 1, pp. 38–43, 2023, doi: 10.56486/remittance.vol4no1.279.

S. Agustian et al., “New Directions in Text Classification Research: Maximizing The Performance of Sentiment Classification from Limited Data Arah Baru Penelitian Klasifikasi Teks: Memaksimalkan Kinerja Klasifikasi Sentimen dari Data Terbatas,” Malcom, pp. 1–10, 2024.

M. Qorib, T. Oladunni, M. Denis, E. Ososanya, and P. Cotae, “Covid-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination Twitter dataset,” Expert Systems with Applications, vol. 212, no. January 2022, p. 118715, 2023, doi: 10.1016/j.eswa.2022.118715.

Ferdian Maulana Akbar, Robby Hermansyah, Sofian Lusa, Dana Indra Sensuse, Nadya Safitri, and Damayanti Elisabeth, “Analisis Sentimen untuk Evaluasi Reputasi Merek Motor XYZ Berkaitan dengan Isu Rangka Motor di Twitter Menggunakan Pendekatan Machine Learning,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 3, pp. 647–654, 2024, doi: 10.25126/jtiik.938663.

W. Zhang, Y. Chang, Y. Ding, Y. Zhu, Y. Zhao, and R. Shi, “To Establish an Early Prediction Model for Acute Respiratory Distress Syndrome in Severe Acute Pancreatitis Using Machine Learning Algorithm,” Journal of Clinical Medicine, vol. 12, no. 5, 2023, doi: 10.3390/jcm12051718.

L. Wu et al., “Development of benchmark datasets for text mining and sentiment analysis to accelerate regulatory literature review,” Regulatory Toxicology and Pharmacology, vol. 137, no. October 2022, p. 105287, 2023, doi: 10.1016/j.yrtph.2022.105287.

T. Anderson, S. Sarkar, and R. Kelley, “Analyzing public sentiment on sustainability: A comprehensive review and application of sentiment analysis techniques,” Natural Language Processing Journal, vol. 8, no. June, p. 100097, 2024, doi: 10.1016/j.nlp.2024.100097.

H. T. Phan, V. C. Tran, N. T. Nguyen, and D. Hwang, “Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Model,” IEEE Access, vol. 8, pp. 14630–14641, 2020, doi: 10.1109/ACCESS.2019.2963702.

S. Mansour, “Social media analysis of user’s responses to terrorism using sentiment analysis and text mining,” Procedia Computer Science, vol. 140, pp. 95–103, 2018, doi: 10.1016/j.procs.2018.10.297.

Y. Yang and N. Li, “Research on Residents’ Travel Behavior Based on Multiple Logistic Regression Model,” IEEE Access, vol. 11, no. July, pp. 74759–74767, 2023, doi: 10.1109/ACCESS.2023.3297497.

Imamah and F. H. Rachman, “Twitter sentiment analysis of Covid-19 using term weighting TF-IDF and logistic regresion,” Proceeding - 6th Information Technology International Seminar, ITIS 2020, pp. 238–242, 2020, doi: 10.1109/ITIS50118.2020.9320958.

R. Sanjaya, E. Tohidi, E. Wahyudi, and K. Kaslani, “Analisis Sentimen Terhadap Berhentinya Tiktokshop Pada Media Sosial Twitter Menggunakan Algoritma Naïve Bayes,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 1, pp. 507–514, 2024, doi: 10.36040/jati.v8i1.8443.

F. Meila, A. Sofyan, N. Sulistiyowati, and A. Voutama, “ANALISIS SENTIMEN TERHADAP RESPON PERUBAHAN NAMA TWITTER MENJADI ‘ X ’ MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER,” vol. 8, no. 5, pp. 10987–10994, 2024.

F. V. Sari and A. Wibowo, “Analisis Sentimen Pelanggan Toko Online Jd.Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi,” Jurnal SIMETRIS, vol. 10, no. 2, pp. 681–686, 2019.

L. Gunawan, M. S. Anggreainy, L. Wihan, Santy, G. Y. Lesmana, and S. Yusuf, “Support vector machine based emotional analysis of restaurant reviews,” Procedia Computer Science, vol. 216, no. 2022, pp. 479–484, 2022, doi: 10.1016/j.procs.2022.12.160.

M. Alfreihat, O. S. Almousa, Y. Tashtoush, A. Alsobeh, K. Mansour, and H. Migdady, “Emo-SL Framework: Emoji Sentiment Lexicon Using Text-Based Features and Machine Learning for Sentiment Analysis,” IEEE Access, vol. 12, no. April, pp. 81793–81812, 2024, doi: 10.1109/ACCESS.2024.3382836.

C. A. Nurhaliza Agustina, R. Novita, Mustakim, and N. E. Rozanda, “The Implementation of TF-IDF and Word2Vec on Booster Vaccine Sentiment Analysis Using Support Vector Machine Algorithm,” Procedia Computer Science, vol. 234, pp. 156–163, 2024, doi: 10.1016/j.procs.2024.02.162.

Analisis Sentimen Publik Terhadap Kenaikan Pajak PPN di Indonesia Tahun 2024 Menggunakan Algoritma Machine Learning