Implementasi Metode Naive Bayes Classifier Terhadap Klasifikasi Topik Kemacetan Lalu Lintas Indonesia Melalui Tweet

 Romindo Romindo (Universitas Pelita Harapan, Jakarta, Indonesia)
 (*)Okky Putra Barus Mail (Universitas Pelita Harapan, Jakarta, Indonesia)
 Jefri Junifer Pangaribuan (Universitas Pelita Harapan, Jakarta, Indonesia)

(*) Corresponding Author

Submitted: February 7, 2024; Published: April 30, 2024

Abstract

The causes of traffic congestion in Indonesia include traffic accidents, poor road infrastructure, and the increasing number of motor vehicles. In 2023, the number of vehicles reached 152.6 million, exceeding half of Indonesia's population of 276 million, according to the Indonesian Traffic Police Corps data. Twitter has a user base of approximately 4.23% of the total global population, which amounts to 436 million user and Indonesia is one of the countries with the largest number of Twitter users. Twitter data will be used to determine the sentiment level of traffic congestion in Indonesia using the Naïve Bayes Classifier method to evaluate overall accuracy performance, precision, recall, and f1-score. The research classified two groups, negative and positive. Classification is carried out through several stages, including data pre-processing, data training, data testing, and evaluation. After evaluating the Naive Bayes algorithm, the highest results achieved an overall accuracy of 77%, precision of 86%, recall of 82%, and f1-score of 84%.

Keywords


Traffic Congestion; Data Mining; Social Media; Tweet; Naive Bayes Classifier

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References

W. Yulita, E. Dwi Nugroho, and M. Habib Algifari, “Analisis Sentimen Terhadap Opini Masyarakat Tentang Vaksin Covid-19 Menggunakan Algoritma Naïve Bayes Classifier,” JDMSI, vol. 2, no. 2, pp. 1–9, 2021.

Akbar Nugroho, “Jumlah Kendaraan Januari 2023: Lebih Setengah Populasi Warga Indonesia Baca artikel CNN Indonesia,” CNN Indonesia.

I. Iwandini, A. Triayudi, and G. Soepriyono, “Analisa Sentimen Pengguna Transportasi Jakarta Terhadap Transjakarta Menggunakan Metode Naives Bayes dan K-Nearest Neighbor,” Journal of Information System Research (JOSH), vol. 4, no. 2, pp. 543–550, Jan. 2023, doi: 10.47065/josh.v4i2.2937.

Monavia Ayu Rizaty, “Pengguna Twitter di Indonesia Capai 18,45 Juta pada 2022,” https://dataindonesia.id/internet/detail/pengguna-twitter-di-indonesia-capai-1845-juta-pada-2022.

J. J. Pangaribuan and F. Ferawaty, “Prediction analysis of student interest in design learning using Naïve Bayes method,” SinkrOn, vol. 5, no. 2, pp. 208–212, Apr. 2021, doi: 10.33395/sinkron.v5i2.10726.

S. Chen, G. I. Webb, L. Liu, and X. Ma, “A novel selective naïve Bayes algorithm,” Knowl Based Syst, vol. 192, p. 105361, Mar. 2020, doi: 10.1016/j.knosys.2019.105361.

L. M. Sinaga, S. Sawaluddin, and S. Suwilo, “Analysis of classification and Naïve Bayes algorithm k-nearest neighbor in data mining,” IOP Conf Ser Mater Sci Eng, vol. 725, no. 1, p. 012106, Jan. 2020, doi: 10.1088/1757-899X/725/1/012106.

I. Aminudin and D. Anggraini, “ANALISIS PERINGKAT TOP BRAND OJEK ONLINE MENGGUNAKAN JEJARING SOSIAL PERCAKAPAN TWITTER,” Jurnal Ilmiah Informatika Komputer, vol. 24, no. 2, pp. 88–104, Aug. 2019, doi: 10.35760/ik.2019.v24i2.2365.

Divisi Redaksi, “Dampak Kemacetan Lalu Lintas Meresahkan Masyarakat,” http://biner.fti.unand.ac.id/dampak-kemacetan-lalu-lintas-meresahkan-masyarakat/.

A. Syakir and F. N. Hasan, “Analisis Sentimen Masyarakat Terhadap Perilaku Korupsi Pejabat Pemerintah Berdasarkan Tweet Menggunakan Naive Bayes Classifier,” vol. 7, pp. 1796–1805, 2023, doi: 10.30865/mib.v7i4.6648.

G. K. Pati and E. Umar, “Analisis Sentimen Komentar Pengunjung Terhadap Tempat Wisata Danau Weekuri Menggunakan Metode Naive Bayes Classifier Dan K-Nearest Neighbor,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 6, no. 4, p. 2309, Oct. 2022, doi: 10.30865/mib.v6i4.4635.

O. P. Barus, Romindo Romindo, and Jefri Junifer Pangaribuan, “Classification of Hearing Loss Degrees with Naive Bayes Algorithm,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 4, pp. 751–757, Aug. 2023, doi: 10.29207/resti.v7i4.4683.

O. P. Barus, K. Lauwren, J. J. Pangaribuan, and R. Romindo, “Implementation of the Naive Bayes Algorithm to Predict the Safety of Heart Failure Patients,” Conference Series, vol. 4, no. 1, pp. 172–177, Dec. 2023, doi: 10.34306/conferenceseries.v4i1.651.

Y. I. Kurniawan, T. Cahyono, Nofiyati, E. Maryanto, A. Fadli, and N. R. Indraswari, “Preprocessing Using Correlation Based Features Selection on Naive Bayes Classification,” in IOP Conference Series: Materials Science and Engineering, Dec. 2020, p. 012012. doi: 10.1088/1757-899X/982/1/012012.

Y. Huang and L. Li, “Naive Bayes classification algorithm based on small sample set,” in 2011 IEEE International Conference on Cloud Computing and Intelligence Systems, IEEE, Sep. 2011, pp. 34–39. doi: 10.1109/CCIS.2011.6045027.

Riza Adrianti Supono and Muhammad Azis Suprayogi, “Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks Helpdesk Menggunakan K-Nearest Neighbor,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 5, pp. 911–918, Oct. 2021, doi: 10.29207/resti.v5i5.3403.

S. Rabbani, D. Safitri, N. Rahmadhani, A. A. F. Sani, and M. K. Anam, “Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 2, pp. 153–160, Oct. 2023, doi: 10.57152/malcom.v3i2.897.

S. Sintia, S. Defit, and G. W. Nurcahyo, “Product Codefication Accuracy With Cosine Similarity And Weighted Term Frequency And Inverse Document Frequency (TF-IDF),” Journal of Applied Engineering and Technological Science (JAETS), vol. 2, no. 2, pp. 62–69, May 2021, doi: 10.37385/jaets.v2i2.210.

M. I. Alfarizi, L. Syafaah, and M. Lestandy, “Emotional Text Classification Using TF-IDF (Term Frequency-Inverse Document Frequency) And LSTM (Long Short-Term Memory),” JUITA : Jurnal Informatika, vol. 10, no. 2, p. 225, Nov. 2022, doi: 10.30595/juita.v10i2.13262.

D. Soria, J. M. Garibaldi, F. Ambrogi, E. M. Biganzoli, and I. O. Ellis, “A ‘non-parametric’ version of the naive Bayes classifier,” Knowl Based Syst, vol. 24, no. 6, pp. 775–784, Aug. 2011, doi: 10.1016/j.knosys.2011.02.014.

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