Sentiment Classification of Fuel Price Rise in Economic Aspects Using Lexicon and SVM Method


  • Muhammad Fikri Alfauzan Faculty of Informatics Telkom University, Indonesia
  • Yuliant Sibaroni Faculty of Informatics Telkom University, Indonesia
  • Fitriyani Faculty of Informatics Telkom University, Indonesia




K-Fold, Lexicon, Sentiment Analysis, SVM, TF-IDF


After being hit by COVID-19 for a long time around the world which resulted in the paralysis of all countries, especially the economic aspects of all countries that dropped dramatically, the world was again shocked by the conflict between Russia and Ukraine which resulted in an increase in world oil prices including in Indonesia, many people complained and opposed the government's policy of increasing fuel prices because fuel affects various aspects, including economic aspects. Based on these problems, researchers use sentiment analysis methods that aim to find out people's opinions on issues that are being discussed throughout Indonesia and this research focuses on comparing the SVM algorithm with TF-IDF feature extraction then using K-Fold Cross Validation after that it is compared with the Lexicon Inset dictionary, in this case the model with Lexicon Inset which contains weighting on each word. In this study, it was found that the dataset model using the SVM algorithm with TF-IDF feature extraction and then using K-Fold Cross Validation obtained an average accuracy of 0.85 using the SVM algorithm. While the model using the automatic labeling dataset using the Indonesian sentiment Lexicon (Lexicon Inset) obtained an average accuracy of 0.68. Classification using SVM with TF-IDF feature extraction is superior to using Lexicon Inset.

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Adiah, A. (2022). Bank Dunia: Harga Energi Naik, Ekonomi Melambat. Retrieved November 17, 2022, from website:

Ahuja, R., Chug, A., Kohli, S., Gupta, S., & Ahuja, P. (2019). The impact of features extraction on the sentiment analysis. Procedia Computer Science, 152, 341–348.

Alsaeedi, A., & Khan, M. Z. (2019). A study on sentiment analysis techniques of Twitter data. International Journal of Advanced Computer Science and Applications, 10(2), 361–374.

Anshuman, Rao, S., & Kakkar, M. (2017). A rating approach based on sentiment analysis. Proceedings of the 7th International Conference Confluence 2017 on Cloud Computing, Data Science and Engineering, (January), 557–562.

Basri, H. (2017). PERANMEDIA SOSIAL TWITTER DALAM INTERAKSI SOSIAL PELAJAR SEKOLAH MENENGAH PERTAMA DI KOTA PEKANBARU (studi kasus pelajar SMPN 1 kota Pekanbaru). Strategi Bertahan Hidup Petani Penggarap Di Jorong Sarilamak Nagari Sarilamak Kecamatan Harau Kabupaten Lima Puluh Kota, 4(1), 1–13. Retrieved from

Buntoro, G. A. (2017). Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter. INTEGER: Journal of Information Technology, 2(1), 32–41.

Deshwal, V., & Sharma, M. (2019). Breast Cancer Detection using SVM Classifier with Grid Search Technique. International Journal of Computer Applications, 178(31), 18–23.

Koto, F., & Rahmaningtyas, G. Y. (2018). Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs. Proceedings of the 2017 International Conference on Asian Language Processing, IALP 2017, 2018-Janua(December), 391–394.

Krouska, A., Troussas, C., & Virvou, M. (2016). The effect of preprocessing techniques on Twitter sentiment analysis. IISA 2016 - 7th International Conference on Information, Intelligence, Systems and Applications, (November 2017).

Mujahidin, S., Prasetio, B., & Utomo, M. C. C. (2022). Implementasi Analisis Sentimen Masyarakat Mengenai Kenaikan Harga BBM Pada Komentar Youtube Dengan Metode Gaussian naïve bayes. Voteteknika (Vocational Teknik Elektronika Dan Informatika), 10(3), 17.

Rahman Isnain, A., Indra Sakti, A., Alita, D., & Satya Marga, N. (2021). Sentimen Analisis Publik Terhadap Kebijakan Lockdown Pemerintah Jakarta Menggunakan Algoritma Svm. Jdmsi, 2(1), 31–37. Retrieved from

Sanjaya, G., & Lhaksmana, K. M. (2020). Lexicon Based ). 7(3), 9698–9710.

Saputra, R. A., & Waluyo, S. (2022). Penerapan Algoritma Naive Bayes Dalam Analisis Kenaikan Bahan Bakar Minyak Pada Twitter. In Seminar Nasional Mahasiswa Fakultas Teknologi Informasi (SENAFTI) Jakarta-Indonesia. Retrieved from

Shalehanny, S., Triayudi, A., & Handayani, E. T. E. (2021). Public’S Sentiment Analysis on Shopee-Food Service Using Lexicon-Based and Support Vector Machine. Jurnal Riset Informatika, 4(1), 1–8.

Sunori, S. K., Singh, D. K., Mittal, A., Maurya, S., Mamodiya, U., & Juneja, P. K. (2021). Rainfall Classification using Support Vector Machine. Proceedings of the 5th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2021, 433–437.


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How to Cite

Alfauzan, M. F., Sibaroni, Y., & Fitriyani. (2023). Sentiment Classification of Fuel Price Rise in Economic Aspects Using Lexicon and SVM Method. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2526-2536.