Aplikasi Analisa Sentimen Bilingual dan Emoji pada Komentar Media Sosial Instagram Menggunakan Metode Support Vector Machine

Satria Adi Nugraha, Henry Novianus Palit, Hans Juwiantho


Indonesia is ranked 4th as the most Instagram user in the world. This makes business people triggered to promote their products and services to content creators to make reviews and upload them on Instagram. Business people need to evaluate uploads to assess whether the promotions carried out get a positive or negative response from netizens. Evaluation can be done by checking the comments column. Instagram comments not only contain comments in Indonesian but in English along with emojis. However, checking manually will certainly take a lot of time. Therefore, it is necessary to build an application system that can detect bilingual sentiments and emojis in Instagram comments. This system was built using the Support Vector Machine method to classify language, Indonesian sentiment, and English sentiment and then evaluated using the accuracy value. The data used is a sample of uploaded comments in the form of posts, reels, and IGTV. The combination of preprocessing cleansing, normalization, stopwords removal, and stemming as well as parameter tuning using GridSearchCV was also tested to find the best model. The model is divided into language classification models with Indonesia, Inggris, and Campuran labels, Indonesian sentiment classifications, and English sentiment classifications with positive, neutral, and negative labels. The best accuracy obtained by the model for language classification, Indonesian sentiment, and English sentiment is 88.77%, 73.10%, and 71.56%, respectively. In addition, emojis need to be analyzed because the model that analyzes emojis has 3.875% better accuracy than the model that ignores emoji.


sentiment analysis; Support Vector Machine; Instagram comments; bilingual sentiment analysis

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Ayvaz, S., & Shiha, M. O. 2017. The Effects of Emoji in

Sentiment Analysis. International Journal of Computer and

Electrical Engineering, 9(1), 360–369. DOI=


Badan Pusat Statistik. Badan Pusat Statistik. URI=


Baeldug. 2021. Multiclass Classification Using Support

Vector Machines. URI= https://www.baeldung.com/cs/svmmulticlass-classification.

Bird, S., Klein, E., & Loper, E. 2009. Natural Language

Processing with Python (J. Steele (ed.)). O’Reilly Media, Inc.

Christianto, M., Andjarwirawan, J., & Tjondrowiguno, A.

(2020). Aplikasi analisa sentimen pada komentar berbahasa

Indonesia dalam objek video di website YouTube

menggunakan metode Naïve Bayes classifier. Jurnal Infra,

1, 255–259.

Deshwal, V., & Sharma, M. 2019. Breast Cancer Detection

using SVM Classifier with Grid Search Technique.

International Journal of Computer Applications, 178(31),

–23. DOI= https://doi.org/10.5120/ijca2019919157.

Joachims, T. 2001. Learning To Classify Text Using Support

Vector Machine. Library of Congress Cataloging-inPublication Data. DOI= https://doi.org/10.1007/978-1-4615-


Kumari, U., Sharma, D. A. K. S., & Soni, D. 2017. Sentiment

Analysis of Smart Phone Product Review using SVM

Classification Technique. International Conference on

Energy, Comunication, Data Analytics and Soft Computing

(ICECDS), 1469–1474. DOI=


Naf’an, M. Z., Bimantara, A. A., Larasati, A., Risondang, E.

M., & Nugraha, N. A. S. 2019. Sentiment Analysis of

Cyberbullying on Instagram User Comments. Journal of

Data Science and Its Applications, 2(1), 88–98. DOI=


Rahat, A. M., Kahir, A., & Masum, A. K. M. 2020.

Comparison of Naive Bayes and SVM Algorithm based on

Sentiment Analysis Using Review Dataset. Proceedings of

the 2019 8th International Conference on System Modeling

and Advancement in Research Trends, SMART 2019, June

, 266–270. DOI=


Rakhmanov, O. 2020. A Comparative Study on Vectorization

and Classification Techniques in Sentiment Analysis to

Classify Student-Lecturer Comments. Procedia Computer

Science, 178, 194–204. DOI=


Rana, S., & Singh, A. 2017. Comparative analysis of

sentiment orientation using SVM and Naive Bayes

techniques. Proceedings on 2016 2nd International

Conference on Next Generation Computing Technologies,

NGCT 2016, October, 106–111. DOI=


Ross, S. (019. Being Real on Fake Instagram: Likes, Images,

and Media Ideologies of Value. Journal of Linguistic

Anthropology, 29(3), 359–374. DOI=


Sastrawi. 2015. sastrawi: High quality stemmer library for

Indonesian Language (Bahasa). URI=


Statista. 2021. Instagram: users by country. URI=


Syarif, I., Prugel-Bennett, A., & Wills, G. 2016. SVM

Parameter Optimization using Grid Search and Genetic

Algorithm to Improve Classification Performance.

TELKOMNIKA (Telecommunication Computing Electronics

and Control), 14(4), 1502. DOI=


Tane, O. Z. A., Lhaksmana, K. M., & Nhita, F. 2019. Analisis

Sentimen pada Twitter Tentang Calon Presiden 2019

Menggunakan Metode SVM (Support Vector Machine).

Seminar Nasional Teknologi Fakultas Teknik Universitas

Krisnadwipayana, 1(1), 739–742.


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