Sentiment Analysis of Stakeholder Satisfaction Measurement

Ni Luh Ratniasih(1*), Ni Wayan Ninik Jayanti(2)

(1) Information System Study Program, ITB STIKOM Bali, Bali
(2) Information System Study Program, ITB STIKOM Bali, Bali
(*) Corresponding Author


Measuring the satisfaction of stakeholders is very impoirtant in order to get feedback and input for the purposes of developing and implementing the improvement strategies. ITB STIKOM Bali routinely measures student stakeholder satisfaction every semester. This study aims to analyze stakeholder comments to generate sentiment analysis on stakeholder satisfaction. The data used are comments on the results of the measurement of stakeholder satisfaction (students) for the Odd Semester of 2020/2021 which are filled out through questionnaire. The algorithm used in this research is the Naïve Bayes Classifier (NBC). The research method in this study consisted of several stages, namely problem identification and literature study, data collection on stakeholder satisfaction (students), data preprocessing, feature extraction in order to facilitate classification using the Naïve Bayes Classifier (NBC) algorithm. The training data used is 200 data while the training data is 2133 data. The results of this study can provide recommendations to ITB STIKOM Bali for the results of student comments as a whole where the percentage of sentiment generated is 58% positive sentiment and 42% negative sentiment.


Sentiment Analysis; Student Satisfaction; Naïve Bayes Classifier (NBC)

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[1] Samsir, A., Verawardina, U., Edi, F., Watrianthos, R., “Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes”, Jurnal Media Informatika Budidarma, Vol. 5, No. 1, pp. 157-163, Januari, 2020.

[2] Juanita, S., “Analisis Sentimen Persepsi Masyarakat Terhadap Pemilu 2019 Pada Media Sosial Twitter Menggunakan Naive Bayes”, Jurnal Media Informatika Budidarma, vol.4, no.3, pp. 552-558, Juli, 2020.

[3] Akbari, M. I. H. A. D., Astri Novianty S.T., M. & Casi Setianingsih S.T., M., 2012. Sentiment Analysis Using Learning Vector Quantization Method. Telkom University.

[4] Liu, B., 2012. Sentiment Analysis and Opinion Mining. In: Chicago: Morgan & Claypool Publishers.

[5] Junaedi, Hartanto., Herman, Budianto. 2011. Data Transformation Pada Data Mining. Prosiding Konferensi nasional “Inovasi dalam Desain dan Teknologi”. IDeaTech 2011.

[6] Kalpit G. Soni and Dr. Atul Patel, “Comparative Analysis of K-means and K-medoids Algorithm on IRIS Data”, International Journal of Computational Intelligence Research, ISSN 0973-1873 Vol. 13, No. 5, pp. 899-906, 2017.

[7] Prasetyo, P. 2012. Data Mining: Konsep dan Aplikasi menggunakan MATLAB. 1st ed. Yogyakarta: Andi.

[8] Rasenda, R., H. Lubis, and R. Ridwan, “Implementasi K-NN Dalam Analisis Sentimen Riba Terhadap Bunga Bank Berdasarkan Data Twitter”, Jurnal Media Informatika Budidarma, vol.4, no.2, pp. 369-376, April, 2020.

[9] Watrianthos R, S. Suryadi, D. Irmayani, M. Nasution, and E. F. S. Simanjorang, “Sentiment Analysis Of Traveloka App Using Naïve Bayes Classifier Method,” INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH., vol. 8, no. 07, pp. 786–788, July, 2019.


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