ANALISIS SENTIMEN DENGAN METODE NAÏVE BAYES, SMOTE DAN ADABOOST PADA TWITTER BANK BTN

• KURNIA

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ABSTRAK

ABSTRAK

Kurnia (11211155), Analisis Sentimen Dengan Metode Naïve Bayes, SMOTE Dan Adaboost Pada Twitter Bank BTN

Media sosial menjadi sarana untuk saling berbagi informasi, dari sebagai sarana promosi hingga digunakan untuk menyebarkan opini . Karena kemudahan teknologi, dimanapun orang dapat mengakses media sosial dan mengomentari isu yang sedang ramai dibicarakan.Branding image juga menjadi suatu hal yang penting, karena masyarakat dapat berinteraksi langsung lewat media sosial baik itu dukungan atau kritikan. Sehingga penelitian ini dilakukan untuk menganalisa sentiment yang ada pada media sosial twitter terhadap Bank BTN sebagai Bank yang sudah lama berfokus dalam bidang Kredit Pemilikan Rumah.Untuk menganalisa sentimen tersebut dilakukan percobaan kombinasi algoritma SMOTE, Naïve Bayes dan Adaboost. Sebelum dilakukan perhitungan algoritma, dilakukan tahapan stemming dan stopwords agar data yang digunakan tidak mengandung noise. Hasil penelitian menunjukan bahwa kombinasi SMOTE, Naïve Bayes, dan Adaboost menunjukan hasil pemodelan yang paling baik dengan akurasi sebesar 87.05%, presisi sebesar 90.63%, recall sebesar 83.00%, dan AUC sebesar 0.909.

Kata Kunci : Analisa Sentimen, Naïve Bayes, SMOTE, Adaboost, Bank BTN

KATA KUNCI

Analisis Sentimen

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DAFTAR PUSTAKA

Daftar Pustaka

[1] S. Kemp, "Digital 2022: Indonesia," 15 Februari 2022. [Online]. Available: https://datareportal.com/reports/digital-2022-indonesia. [Accessed 08 April 2022].

[2] M. Wongkar and A. Angdresey, "Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler : Twitter," 2019 Fourth International Conference on Informatics and Computing (ICIC), pp. 2-3, 2019.

[3] P. Kanimozhi and D. Elavarasi, "A Survey on Sentiment Analysis using Twitter Dataset," International Journal of Engineering Research & Technology (IJERT), vol. 6, no. 14, pp. 1-3, 2018.

[4] A. Alsaeedi and M. Z. Khan, "A Study on Sentiment Analysis Techniques of Twitter Data," (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 10, no. 2, pp. 1-2, 2019.

[5] B. B. K, A. P. Rodrigues and N. N. Chiplunkar, "Comparative study of machine learning techniques in sentimental analysis," 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 216-221, 2017.

[6] N. C. Dang, M. N. Moreno-Garcia and F. D. l. Prieta, "Sentiment Analysis Based on Deep Learning: A Comparative Study," Electronics 2020, vol. 9, no. 483, pp. 1-29, 2020.

[7] M. M. Saritas and A. Yasar, "Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification," Int J Intell Syst Appl Eng, vol. 7, no. 2, pp. 88-91, 2019.

[8] S. L. and S. S. , "ANALISIS SENTIMEN VAKSIN SINOVAC PADA TWITTER MENGGUNAKAN ALGORITMA NAIVE BAYES," SISMATIK (Seminar Nasional Sistem Informasi dan Manajemen Informatika), vol. 1, no. 1, pp. 163-170, 2021.

[9] E. Indrayuni, "Klasifikasi Text Mining Review Produk Kosmetik Untuk Teks Bahasa Indonesia Menggunakan Algoritma Naive Bayes," JURNAL KHATULISTIWA INFORMATIKA, vol. VII, no. 1, pp. 29-36, 2019.

[10] A. N. Rais and A. S. , "Integrasi SMOTE dan Ensemble AdaBoost Untuk Mengatasi Imbalance Class Pada Data Bank Direct Marketing," JURNAL INFORMATIKA, vol. 6, no. 2, pp. 278-285, 2019.

[11] P. Skryjomski and B. Krawczyk, "Infuence of minority class instance types on SMOTE imbalanced data oversampling," Proceedings of Machine Learning Research, no. 74, pp. 7-21, 2017..

[12] A. Byna and M. Basit, "Penerapan Metode Adaboost Untuk Mengoptimasi Prediksi Penyakit Stroke Dengan Algoritma Naïve Bayes," Jurnal SISFOKOM (Sistem Informasi dan Komputer), vol. 09, no. 03, pp. 407-411, 2020.

[13] I. M. Latief, A. Subekti and W. Gata, "PREDIKSI TINGKAT PELANGGAN CHURN PADA PERUSAHAAN TELEKOMUNIKASI DENGAN ALGOITMA ADABOOST," Jurnal Informatika, vol. 21, no. 1, pp. 34-43, 2021. 46

[14] B. Liu, Sentiment Analysis : Mining Opinions, Sentiments, and Emotions, New York: Cambridge University Press, 2015.

[15] B. Agarwal and N. Mittal, Prominent Feature Extraction for Sentiment Analysis, Switzerland: Springer International Publishing, 2016.

[16] M. A. Russel and M. Klassen, Mining the Social Web, California: O’Reilly Media, Inc., 2019.

[17] Wikipedia, "Wikipedia," [Online]. Available: https://id.wikipedia.org/wiki/Twitter. [Accessed 19 April 2022]. [18] Twitter Inc., "General Twitter API Support," [Online]. Available: https://developer.twitter.com/en/support/twitter-api/support-overview#faq-twitter-apisetup. [Accessed 20 April 2022].

[19] M. Awad and R. Khanna, Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, New York: Apress Media, LLC, 2015.

[20] A. C. Muller and S. Guido, Introduction to Machine Learning with Python, California: O’Reilly Media, Inc., 2017.

[21] M. Swamynathan, Mastering Machine Learning with  Python in Six Steps, New York: Apress Media, LLC, 2017.

[22] J. Zizka, F. Darena and A. Svoboda, Text Mining with Machine Learning Principles and Techniques, Florida: Taylor & Francis Group, LLC, 2020.

[23] V. Kotu and B. Deshpande, Predictive Analytics and Data Mining, Massachusetts: Elsevier Inc., 2015.

[24] G. Ignatow and R. Mihalcea, An Introduction to Text Mining, California: SAGE Publications, Inc., 2018.

[25] A. Fernandez, S. Garcia, F. Herrera and N. V. Chawla, "SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary," Journal of Artificial Intelligence Research, no. 61, pp. 863-905, 2018.

[26] V. Kotu and B. Deshpade, PhD, Predictive Analytics and Data Mining (Concepts and Practices with RapidMiner), Massachusetts: Elsevier Inc., 2015.

[27] D. W. Gata, H. Setiawan and S. Kurniawan, "GATAFRAMEWORK," [Online]. Available: http://www.gataframework.com/. [Accessed 27 Mei 2022].

[28] D. D. Saputra, W. Gata, N. K. Wardhani, K. S. Parthama, H. Setiawan, S. Budilaksono, D. Yogatama, A. Hadiyatna, E. P. Purnamasari, B. Pratama and D. Novianti, "Optimization Sentiments of Analysis from Tweets in myXLCare using Naive Bayes Algorithm and Synthetic Minority Oversampling Technique Method," Journal Of Physics: Conference Series, vol. 1471, pp. 1-7, 2019.