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Abstract

Cooking oil is a basic need for Indonesian people. Indonesia experienced a shortage of oil in March 2022. This was a hot topic of discussion on social media Twitter last March, many people thought positively or negatively. However behind it all, there are differences in the assessment of parties who feel the pros and cons, various parties have different points of view. In this article, we conduct a sentiment analysis of public responses regarding the scarcity of cooking oil using a dataset obtained from the Twitter digital platform. This article aims to classify tweets related to the scarcity of cooking oil into positive and negative sentiments using a machine learning strategy with the Naive  Bayes and lexicon based methods. This algorithm was chosen to make it easier for interested users to compare methods and find out how accurate it is, which is where the level of accuracy obtained from the lexicon method is 42% and the method using the naïve Bayes classifier is 72%. Shows the results of the analysis related to the scarcity of cooking oil for the highest level of accuracy, namely the method that uses the naïve Bayes classifier compared to the method that uses lexicon based

Keywords

minyak goreng naive bayes classifier lexicon based cooking oil, naïve bayes classifier, lexicon based

Article Details

Author Biographies

Faldy Irwiensyah, Universitas Muhammadiyah Prof. Dr. Hamka

Program Studi Teknik Informatika

Firman Noor Hasan, Universitas Muhammadiyah Prof. Dr. Hamka

Universitas Muhammadiyah Prof. Dr. Hamka

How to Cite
Irwiensyah, F., & Hasan, F. N. (2023). Perbandingan Akurasi Metode Naïve Bayes Classifier dan Lexicon Based pada Analisis Sentimen Respon Masyarakat Tentang Kebijakan Kenaikan Harga Minyak Goreng. Jurnal Teknik Informatika Dan Komputer, 2(1), 18–23. https://doi.org/10.22236/jutikom.v2i1.11500
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References

  1. Amrustian, M. A., Widayat, W., & Wirawan, A. M. (2022). Analisis Sentimen Evaluasi Terhadap Pengajaran Dosen di Perguruan Tinggi Menggunakan Metode LSTM. 6, 535–541. https://doi.org/10.30865/mib.v6i1.3527
  2. Badryah, & Rasmito, A. (2018). Pemanfaatan Kunyit Untuk Meningkatkan Kualitas Minyak Goreng Curah. Jurnal Teknik Industri Dan Kimia, 1(1), 7–15. https://doi.org/https://doi.org/10.54980/jtik.v1i1.59
  3. Dewi, S. M., Syahputri, A. N., Deni, K., Sari, A. P., & Damanik, I. S. (2021). Sistem Pendukung Keputusan Penentuan Produk Minyak Goreng Kemasan dikalangan Masyarakat dengan Metode MFEP (Multi Factor Evaluation Process). Seminar Nasional Sains Dan Teknologi Informasi (SENSASI), 2021, 577–580.
  4. Firly, S. . R., & Nurrahmah, A. (2020). Analisis perbandingan penggunaan minyak curah dan minyak kemasan menggunakan uji hipotesis dua proporsi. Bulletin of Applied Industrial Engineering Theory, 2(1), 65–66.
  5. Franko, G., Tarigan, J., & Pinem, L. J. (2021). Agriprimatech Vol. 4 No. 2, April 2021. 4(2), 57–64.
  6. Ftiri, A., Margasaty, F., Kusmaria, Desfaryani, R., & Dewi, V. U. (2020). Peramalan Harga Minyak Goreng Di Tengah Pandemi Covid-19 Kota Bandar Lampung. Jurnal DwijenAGRO, 10(1), 21–26. https://doi.org/https://doi.org/10.46650/dwijenagro.10.1.859.21-26
  7. Hasan, F. N., Aziz, A. S., & Nofendri, Y. (2023). Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu Recommendations. MATRIK: Jurnal Manajemen, Teknik Informatika, Dan Rekayasa Komputer, 22(2), 261–270. https://doi.org/10.30812/matrik.v22i2.2166
  8. Hasan, F. N., & Dwijayanti, M. (2021). Analisis Sentimen Ulasan Pelanggan Terhadap Layanan Grab Indonesia Menggunakan Multinominal Naïve Bayes Classifier. Jurnal Linguistik Komputasional, 4(2), 52–58. https://doi.org/https://doi.org/10.26418/jlk.v4i2.61
  9. Hasan, F. N., Sidik, F., & Afikah, P. (2022). Sentiment Analysis of Community Response on Cooking Oil Price Increase Policy with Naïve Bayes Classifier Algorithm. Jurnal Linguistik Komputasional, 5(2), 71–76. https://doi.org/https://doi.org/10.26418/jlk.v5i2.99
  10. Hernikawati. (2021). Kecenderungan Tanggapan Masyarakat Terhadap Vaksin Sinovac Berdasarkan Lexicon Based Sentiment Analysis The Trend of Public Response to Sinovac Vaccine Based on Lexicon Based Sentiment Analysis. Jurnal Ilmu Pengetahuan Dan Teknologi Komunikasi, 23(1), 21–31.
  11. Prabowo, W. A., & Wiguna, C. (2021). Sistem Informasi UMKM Bengkel Berbasis Web Menggunakan Metode SCRUM. Jurnal Media Informatika Budidarma, 5(1), 149. https://doi.org/10.30865/mib.v5i1.2604
  12. Putra, N. A., & Azara, R. (2021). Comparative of the Quality of Cooking Oil With Four Times Frying on Packaged and Bulk Cooking Oil. Journal of Tropical Food and Agroindustrial Technology, 2(1), 9–14. https://doi.org/https://doi.org/10.21070/jtfat.v2i01.1576
  13. Safira, A., & Hasan, F. N. (2023). Analisis Sentimen Masyarakat Terhadap Paylater Menggunakan Metode Naive Bayes Classifier. ZONAsi: Jurnal Sistem Informasi, 5(1), 59–70. https://doi.org/https://doi.org/10.31849/zn.v5i1.12856
  14. Sidik, F., Suhada, I., Anwar, A. H., & Hasan, F. N. (2022). Analisis Sentimen Terhadap Pembelajaran Daring Dengan Algoritma Naive Bayes Classifier. Jurnal Linguistik Komputasional, 5(1), 34–43. https://doi.org/https://doi.org/10.26418/jlk.v5i1.79
  15. Sinaga, D. M., Rusdina R, Alfah, R., Perdana Windarto, A., & Wanto, A. (2019). Analisis Metode ELECTRE Pada Pemilihan Produk Minyak Goreng Kemasan Terbaik Berdasarkan Konsumen. Science and Informatic V5.I2, 5(2), 129–135.
  16. Sudiantoro, A. V., Zuliarso, E., Studi, P., Informatika, T., Informasi, F. T., Stikubank, U., & Mining, T. (2018). Analisis Sentimen Twitter Menggunakan Text Mining Dengan. 10(2), 398–401.
  17. Widjaja, G. (2022). HILANGNYA MINYAK GORENG DARI PASAR. File:///C:/Users/Fajar Sidik/Downloads/675-729-1-PB.Pdf, 2(2), 1–11.