Analisis Sentimen Terhadap Kenaikan Harga Bahan Pokok Menggunakan Metode Naive Bayes Classifier

 (*)Muhammad Muslimin Mail (Universitas Stikubank, Semarang, Indonesia)
 Veronica Lusiana (Universitas Stikubank, Semarang, Indonesia)

(*) Corresponding Author

Submitted: June 21, 2023; Published: July 23, 2023

Abstract

Basic necessities are the main needs that are important for people’s lives. The increase in the price of basic necessities certainly has a huge impact on the operational costs of the community and has become a very crucial issue. This event gave rise to pro and con responses from the public expressed through social media Twitter. From this event, sentiment analysis research was conducted related to the increase in the price of basic commodities. The amount of data used for this research is 2070 tweet data. The analysis results show that negative sentiment appears more than positive sentiment, with a percentage of 2,8% positive sentiment and 97,2% for negative sentiment. Retrival of tweet data is done through the netlytic.org website with the keyword staples. The classification method uses the naïve Bayes Classifier method. Furthermore, data division is carried out on the dataset with a ratio of 6:4. The data is divided into 60% training data and 40% test data. The size of the test data as much as 40% of the overall data produces the best accuracy rate. From the results of testing the model with the Naïve Bayes Classifier method the evaluation value results are the highest accuracy score of 94,38%, precision of 59,67%, recall of 67,93%, and F-measure of 62,32%. It can be concluded that the results of sentiment analysis on the increase in the proce of basic commodities get a negative response from the public. This research proposes a method of sentiment analysis of rising prices of basic commodities by considering the level opinion sentiment on Twitter.

Keywords


Community; Sentiment Analysis; Twitter; Basic Materials; Naïve Bayes Classifier

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References

D. A. Wulandari, R. R. Saedudin, and R. Andreswari, “Analisis Sentimen Media Sosial Twitter Terhadap Reaksi Masyarakat Pada Ruu Cipta Kerja Menggunakan Metode Klasifikasi Algoritma Naive Bayes,” e-Procedding of Engineering, 2021, vol. 8, no. 5, pp. 9007-90016.

B. T. Buwono and N. Matondang, “Analisis Sentimen Pada Media Sosial Twitter Mengenai Kebijakan Kenaikan Harga Bahan Minyak Menggunakan Metode Naïve Bayes,” Senamika, 2022, vol. 3, no. 2, pp. 584-591.

U. Kurniasih and A. T. Suseno, “Analisis Sentimen Terhadap Bantuan Subsisi Upah (BSU) Pada Kenaikan Harga Bahan Bakar Minyak (BBM),” Jurnal Media Informatika Budidarma, 2022, vol. 6, no. 4, pp. 2335-2340, 2022, doi: 10.30865/mib.v6i4.4958.

R. Asrianto and M. Herwinanda, “Analisis Sentimen Kenaikan Harga Kebutuhan Pokok Dimedia Sosial Youtube Menggunakan Algoritma Support Vector Machine,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 3, no. 3, pp. 431–440, Dec. 2022, doi: 10.37859/coscitech.v3i3.4368.

M. Riski Qisthiano, I. Ruswita, and A. Prayesy, “Implementasi Metode SVM dalam Analisis Sentimen Mengenai Vaksin dengan Menggunakan Python 3,” Teknologi: Jurnal Ilmiah Sistem Informasi, vol. 13, no. 1, pp. 1–7, 2023, doi: 10.26594/teknologi.v13i1.3105.

S. L. M. Sitio, and R. Nadiyanti, “Analisis Sentimen Kenaikan Harga BBM Pertamax Pada Media Sosial Menggunakan Metode Naïve Bayes Classifier,” Technology and Science (BITS), vol. 4, no. 3, 2022, doi: 10.47065/bits.v4i3.2331.

S. Mujahidin, B. Prasetio, and M. C. C. Utomo, “Implementasi Analisis Sentimen Masyarakat Menegnai Kenaikan Harga BBM Pada Komentar Youtube Dengan Metode Gaussian Naïve Bayes,” Jurnal Vocational Teknik Elektronika Dan Informatika, 2022, Vol. 10. No. 3, pp. 17-24.

P. S. Zalukhu, T. Handhayani, and M. Sitorus, “Analisis Sentimen Terhadap Kenaikan BBM Di Indonesia Pada Media Sosial Twitter Menggunakan Metode Naïve Bayes,” Jurnal Sistem Informasi dan Teknik Informatika, 2023, vol. 8, no. 1, pp. 65-69.

P. Rohimi, “SNA Dengan Netlytic Pada Kolom Komentar Video Youtube Gus Miftah Ceramah Di Gereja,” Proceeding Of Conference On Strengthening Islamic Studies In The Digital Era, 2021, vol. 1, no. 1, pp. 360-377.

R. Azhar, A. Surahman, and C. Juliane, “Analisis Sentimen Terhadap Cryptocurrency Berbasis Python TextBlob Menggunakan Algoritma Naïve Bayes,” Jurnal Sains Komputer & Informatika (J-SAKTI), 2022, Vol. 6, No. 1, pp. 267-281.

H. Irsyad and A. Taqwiym, “Sentimen Analisis Masyarakat Terhadap Rakyat Palestina dengan Klasifikasi Naive Bayes,” Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem & Komputer (JTECS), 2021, Vol. 1, No. 2, pp. 167-176.

R. A. Raharjo, I. M. G. Sunarya, and D. G. H. Divayana, “Perbandingan Metode Naïve Bayes Classifier Dan Support Vector Machine Pada Kasus Analisis Sentimenerhadap Data Vaksin Covid-19 Di Twitter,” Jurnal Ilmiah Elektronika Dan Komputer, 2022, vol.15, no.2, pp. 456-464.

D. I. Mulyana, N. Lutfianti, “Analisis Sentimen Dengan Algoritma SVM Dalam Tanggapan Netizen Terhadap Berita Resesi 2023,” Jurnal SISFOTENIKA, vol. 13, no. 1, 2023, doi: 10.30700/jst.v13i1.1339.

M. Zaki Anbari, M. Zaki Anbari, and B. Sugiantoro, “Studi Komparasi Metode Analisis Sentimen Naïve Bayes, SVM, dan Logistic Regression Pada Piala Dunia 2022,” Jurnal Media Informatika Budidarma, 2023, Vol. 7, No. 2, pp. 688-695 doi: 10.30865/mib.v7i2.5383.

R. Asmara, M. F. Ardiansyah, and M. Ansori, “Analisa Sentiment Masyarakat Terhadap Pemilu 2019 Berdasarkan Opini Di Twitter Menggunakan Metode Naïve Bayes Classifier,” Jurnal Inovtek Polbeng – Seri Informatika, 2020, vol. 5, no. 2, pp. 193-204.

E. Salim and A. Solichin, “Analisis Sentimen Pada Media Sosial Twitter Terhadap Pelayanan Dinas Kependudukan Dan Pencatatan Sipil Menggunakan Algoritma Naïve Bayes,” Indonesia Journal Information System (IDEALIS), 2022, Vol. 5, No. 2, pp. 79-86.

A. Budiman, A. Suryadibrata, and J. C. Young, “Implementasi Algoritma Naïve Bayes untuk Klasifikasi Konten Twitter dengan Indikasi Depresi,” Jurnal Pengembangan IT, 2021, vol. 6, no. 2, pp. 133-138.

F. Rejeki and V. Ayumi, “Analisa Sentimen Mengenai Kenaikan Harga Bbm Menggunakan Metode Naïve Bayes Dan Support Vector Machine,” JSAI : Journal Scientific and Applied Informatics, 2023, vol. 6, no. 1, 2023, pp. 1-10 doi: 10.36085.

I. Najiyah, “Analisis Sentimen Tanggapan Masyarakat Indonesia Tentang Kenaikan Bbm Menggunakan Metode Artificial Neural Network,” Jurnal Responsif, 2023, vol. 5, no. 1, pp. 92–100.

M. R. Nurhusen, J. Indra, and K. A. Baihaqi, “Analisis Sentimen Pengguna Twitter Terhadap Kenaikan Harga Bahan Bakar Minyak (BBM) Menggunakan Metode Logistic Regression,” Jurnal Media Informatika Budidarma, 2023, vol. 7, no. 1, pp. 276-282, doi: 10.30865/mib.v7i1.5491.

F. Amaliah and K. D. Nuryana, “Perbandingan Akurasi Metode Lexicon Based Dan Naïve Bayes Classifier Pada Analisis Sentimen Pendapat Masyarakat Terhadap Aplikasi Investasi Pada Media Twitter,” Journal Of Informatics And Conputer Science, 2022, vol. 3, no. 4, pp. 384-393.

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