Sentiment Analysis Berbasis Algoritma Naïve Bayes Classsifier untuk Identifikasi Persepsi Masyarakat Terhadap Produk / Layanan Perusahaan

Affandy Affandy, Oktania Nandiyati


Twitter is the most popular microblogging service in Indonesia, with nearly 23 million users. In the era of big data such as the current tweets from customers, observers, potential consumers, or the community of users of products or services of a company will greatly help companies in knowing the industrial and consumer landscape, so as to determine strategic plans that will contribute to the company's growth. However, the use of data from social media such as Twitter is hampered by a number of technical difficulties in the process of collecting, processing, and analysing. Specifically, this research applies the Naïve Bayes Classifier algorithm in the process of sentiment analysis of tweets data into a prototype application that is intended to make it easier for companies / organizations to know people's perceptions of their products or services. The NBC algorithm was chosen because this algorithm is able to do a good classification even though it uses small training data, but has high accuracy and process speed for handling large training data. From the evaluation results found a prototype running well where the keywords entered will trigger the Twitter API to crawl the data then the mining process can be monitored at each stage and at the end of the process, the system will show the final sentiment level values and the representation of the calculation results log in a chart form over a certain period of time.

Full Text:



K., Simon, “Digital 2020: Indonesia” 2020. [Online]. Available: [Accessed 03 03 2020].

Similarweb LTD, “Top sites ranking for all categories in Indonesia” 2020. [Online]. Available: [Accessed 04 03 2020].

Obar, Jonathan A.; Wildman, Steve (2015). "Social media definition and the governance challenge: An introduction to the special issue". Telecommunications Policy. 39 (9): 745–750. doi:10.1016/j.telpol.2015.07.014. SSRN 2647377.

Statista, “Number of Twitter users in Indonesia from 2014 to 2019” 2020. [Online]. Available: [Accessed 04 03 2020]

Carley, Kathleen & Malik, Momin & Kowalchuck, Michael & Pfeffer, Juergen & Landwehr, Peter. (2015). Twitter Usage in Indonesia. 10.13140/RG.2.1.2163.9925.

A., Alamsyah, “(Big) Data Analytics for Economics, Business and Management: A Social Network Approach,” dalam In Workshop Big Data Puslitbang Aptika dan IKP, 2015.

J. Ipmawati, Kusrini dan E. T. Luthfi, “Komparasi Teknik Klasifikasi Teks Mining Pada Analisis Sentimen,” Indonesian Journal on Networking and Security, vol. VI, no. 1, pp. 28-36, 2017.

R. Sari, “Komparasi Algoritma Support Vector Machine, Naïve Bayes Dan C4.5 Untuk Klasifikasi SMS,” Indonesian Journal on Computer and Information Technology , vol. II, no. 2, pp. 7-13, 2017 .

Hermanto, S. J. Kuryanti dan S. N. Khasanah, “Comparison of Naïve Bayes Algorithm, C4.5 and Random Forest for Service Classification Ojek Online,” Journal Publications & Informatics Engineering Research, vol. III, no. 2, pp. 266-274, 2019.

Y. Aggraini, Sucipto dan R. Indriati, “Cyberbullying Detection Modelling at Twitter Social Networking,” Information System, vol. VI, no. 2, pp. 113-118, 2018.

S. K. Lidya, O. S. Sitompul dan S. Efendi, “Sentimen Analisis pada Teks Bahasa Indonesia Menggunakan Support Vector Machine dan K-Nearest Neighbor,” dalam Seminar Nasional Teknologi Informasi dan Komunikasi (SENTIKA), Yogyakarta , 2015.

R. N. Devita, H. W. Herwanto dan A. P. Wibawa , “Perbandingan Kinerja Metode Naive Bayes dan K-Nearest Neighbor untuk Klasifikasi Artikel Bahasa Indonesia,” Jurnal Teknologi Informasi dan Ilmu Komputer , vol. 5, no. 4, pp. 427-434, 2018 .

Wirth, R., & Hipp, J. (2000, April). CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining (pp. 29-39). London, UK: Springer-Verlag.

F. Z. Tala, “A Study of Stemming Effects on Information Retrieval in Bahasa Indonesia,” Institute for Logic, Language and Computation Universiteit Van Amsterdam The Netherlands, 2003.


Article Metrics

Abstract view : 3051 times
PDF - 345 times


  • There are currently no refbacks.

Indexed by:


JOINS (Journal Of Information System) licensed by Creative Commons Attribution 4.0 International License.

Creative Commons License