Sentiment Analysis Sentiment Analysis on Twitter of the Directorate General of Customs and Excise Using a comparison of the Naïve Bayes Algorithm and Support Vector Machine
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Published
Dec 21, 2024
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Abstract
Direktorat Jenderal Bea & Cukai (DJBC) is a government agency in charge of guarding and serving export and import activities in Indonesia since its establishment in 1946 which is expected by the community as the front guard in protecting the community in this field, but in recent times there have been many cases involving the institution of the Directorate General of Customs & Excise which make this institution can affect the view of the performance of this institution. With the description of the problem above, it is very interesting to conduct research on public views using tweets from twitter @bravobeacukai and @beacukaiRI which are owned and processed by Direktorat Jenderal Bea & Cukai as a place to channel public opinions and views on this institution. This research uses the Smote method with Naïve Bayes and compared with Support vector machine methods for these results to compare the level of accuracy. The results of this study found that using the Smote method with Naïve Bayes obtained an Accuracy value of 78.95%, Precision 74.01%, Recall 89.41%, and AUC 0.650 while for the Smote method with Support vector machine is worth 73.35% Accuracy, Precision 67.88%, Recall 88.95%, and AUC 0.853. Based on the research results, the smote method with Naïve Bayes has the greatest results and is effective with the dataset studied.
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References
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Al-Qurishi, M., Rana, O. , & Rajarajan, M. (2019). Real-time Spam Detection in Online Social Media Using Naive Bayes and Decision Tree. International Journal of Machine Learning and Cybernetics, 10, 2211–2221.
Bagus Panuluh Yudha Perkasa, K., & Eka Purwiantono, F. (n.d.). Sistem Rekomendasi Jurusan Menggunakan Algoritma Naïve Bayes Gaussian Berbasis Web. Jl. Raya Tidar, 65146(100). Retrieved from https://jurnal.stiki.ac.id/J-INTECH/article/view/1090
Cambria, E., & White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational Intelligence Magazine, 9(2), 48–57. https://doi.org/10.1109/MCI.2014.2307227
Devita, R. N., Herwanto, H. W., & Wibawa, A. P. (2018). Perbandingan Kinerja Metode Naive Bayes dan K-Nearest Neighbor untuk Klasifikasi Artikel Berbahasa indonesia. Jurnal Teknologi Informasi Dan Ilmu Komputer, 5(4), 427–434. https://doi.org/10.25126/jtiik.201854773
Dharmawan, A., Raharjo, S. T., & Kusumawardhani, A. (2019). Analisis Kinerja Bea Cukai Indonesia Yang Dipengaruhi Oleh Budaya Organisasi, Partisipasi Stakeholder, Teknologi Informasi, Dan Transfer of Knowledge. Jurnal Bisnis Strategi, 27(2), 110. https://doi.org/10.14710/jbs.27.2.110-122
Firmansyah, S., Gaol, J., & Susilo, S. B. (2019). Comparison of SVM and Decision Tree Classifier with Object Based Approach for Mangrove Mapping to Sentinel-2B Data on Gili Sulat, Lombok Timur. Jurnal Pengelolaan Sumberdaya Alam Dan Lingkungan, 9(3), 746–757. https://doi.org/10.29244/jpsl.9.3.746-757
Haqiqi, F., Santika, M., & Yusmalina. (2021). Analisis Pengaruh Realisasi Penerimaan Bea Masuk Terhadap Target Penerimaan Bea Masuk (Pada Kantor Pengawasan Dan Pelayanan Bea Cukai Tipe Madya B Tanjung Balai Karimun Periode 2017-2019). Jurnal Cafetaria, 2(1), 48–61. https://doi.org/10.51742/akuntansi.v2i1.282
Hardiantoro, A., & Firdaus, F. (n.d.). Ramai soal Barang Impor Gratis Masih Dikenai Pajak Bea Masuk, Ini Kata Ditjen Bea Cukai - Kompas.com. Retrieved June 6, 2024, from kompas.com website: https://www.kompas.com/tren/read/2023/03/18/103000465/ramai-soal-barang-impor-gratis-masih-dikenai-pajak-bea-masuk-ini-kata?page=all#google_vignette
Jain, A. , & Gupta, B. (2022). An Enhanced Naive Bayes Model for Sentiment Analysis of Tweets. Applied Computing and Informatics.
Jiang et al. (2009). text classification with Naïve Bayes.
Khan, M. , B. B., & Kamaruddin, H. (2020). Efficient Classification of Short Text with Deep Learning and Feature Reduction Techniques. IEEE, 8, 134479–134491.
Lumbanraja, F. R., Gaol, E. C. L., Shofiana, D. A., & Junaidi, A. (2024). Implementasi SMOTE dan Support Vector Machine Pada Klasifikasi Data Tidak Seimbang Metilasi Arginin. Jurnal Pepadun, 5(1), 27–37. https://doi.org/10.23960/pepadun.v5i1.209
Pravina, A. M., Cholissodin, I., & Adikara, P. P. (2019). Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine (SVM). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(3), 2789–2797.
Qorry Meidianingsih, Wardani, D. E., Salsabila, E., Nafisah, L., & Mutia, A. N. (2023). Perbandingan Performa Metode Berbasis Support Vector Machine untuk Penanganan Klasifikasi Multi Kelas Tidak Seimbang. STATISTIKA Journal of Theoretical Statistics and Its Applications, 23(1), 8–18. https://doi.org/10.29313/statistika.v23i1.1660
Ristoski, P., Bizer, C., & Paulheim, H. (2015). Mining the Web of Linked Data with RapidMiner. Journal of Web Semantics, 35, 142–151. https://doi.org/10.1016/J.WEBSEM.2015.06.004
Sangeetha, S. , & Vijayakumar, V. ,. (2018). Performance Analysis of Naive Bayes Algorithm in Classifying Twitter Sentiment with Different Feature Extraction Methods. International Journal of Engineering and Technology (IJET), 7, 580–584.
Singh, M. (2016). Sentiment Analysis and Similarity Evaluation for Heterogeneous-Domain Product Reviews. 144(2), 16–19.
Supriyatna, A., & Mustika, W. P. (2018). Komparasi Algoritma Naive bayes dan SVM Untuk Memprediksi Keberhasilan Imunoterapi Pada Penyakit Kutil. J-SAKTI (Jurnal Sains Komputer Dan Informatika), 2(2), 152. https://doi.org/10.30645/j-sakti.v2i2.78
Syah, F., Fajrin, H., Afif, A. N., Saeputra, M. R., Mirranty, D., & Saputra, D. D. (2023). Analisa Sentimen Terhadap Twitter IndihomeCare Menggunakan Perbandingan Algoritma Smote, Support Vector Machine, AdaBoost dan Particle Swarm Optimization. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(1), 53–58. https://doi.org/10.35870/jtik.v7i1.686
Toy, K. V. S., Sari, Y. A., & Cholissodin, I. (2021). Analisis Sentimen Twitter menggunakan Metode Naive Bayes dengan Relevance Frequency Feature Selection (Studi Kasus: Opini Masyarakat mengenai Kebijakan New Normal). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(11), 5068–5074. Retrieved from http://j-ptiik.ub.ac.id
Yudistira, A., & Wicaksana, S. T. (2022). Studi Kasus Implementasi SNI ISO 37001:2016 Dalam Pencegahan Korupsi Pada KPU BC Tipe A Tanjung Priok. Jurnal Perspektif Bea Dan Cukai, 6(2), 273–283.
Yulita, W. (2021a). Analisis Sentimen Terhadap Opini Masyarakat Tentang Vaksin Covid-19 Menggunakan Algoritma Naïve Bayes Classifier. Jurnal Data Mining Dan Sistem Informasi, 2(2), 1. https://doi.org/10.33365/jdmsi.v2i2.1344
Yulita, W. (2021b). Analisis Sentimen Terhadap Opini Masyarakat Tentang Vaksin Covid-19 Menggunakan Algoritma Naïve Bayes Classifier. Jurnal Data Mining Dan Sistem Informasi, 2(2), 1. https://doi.org/10.33365/jdmsi.v2i2.1344