Diterbitkan Oleh:
Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Creation is distributed below Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
SENTIMENT ANALYSIS ON THE TWITTER PSSI PERFORMANCE USING TEXT MINING WITH THE NAÏVE BAYES ALGORITHM
Keywords:
Sentiment Analysis, PSSI, Twitter, Naïve Bayes Classifier
Abstract
Social media has developed rapidly today, so social media is no longer just a place to interact and socialize but also to express opinions or criticize a particular party or institution. After the incident at the Malang Kanjuruhan stadium in October 2022, many netizens criticized the performance of PSSI as Indonesia's number one organization that oversees football competitions in Indonesia. For this reason, sentiment analysis was carried out on the official PSSI account on Twitter to assess the performance of PSSI by grouping them as Satisfied and Unsatisfied using the Naïve Bayes Classifier. Sentiment analysis took tweets from the official PSSI account and as many as 1000 comments to be used as a dataset. Then preprocessing is carried out in the GATA Framework using the Annotation Removal, Remove Hashtag, Transformation Remove URL, Regexp, Indonesian Steaming, and Indonesian Stopword Removal methods. The results obtained were 82.82% for accuracy, 78.69% for precision, 90.33% for recall, and 0.866 for AUC. With these results, the value obtained is at a good classification level.
Downloads
Download data is not yet available.
References
Fadilah, E. (2019). Implementasi Metode Profile Matching Terhadap Sistem Pendukung Keputusan Penerimaan Dana Zakat pada Badan Amil Zakat Pertamina (BAZMA). Matics, 10(2), 39. https://doi.org/10.18860/mat.v10i2.5745
Gunawan, B., Pratiwi, H. S., & Pratama, E. E. (2018). Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 4(2), 113. https://doi.org/10.26418/jp.v4i2.27526
Hermawan, D., Akhsanal, M., Wahyudi, Z., Ariyanto, A., & Dwi, D. (2022). Optimasi Analisis Sentimen Pada Twitter Olshop Tokopedia Menggunakan Textmining Dengan Algoritma Naïve Bayes & Adaboost. 6(September), 821–828.
Imandasari, T., Irawan, E., Windarto, A. P., & Wanto, A. (2019). Algoritma Naive Bayes Dalam Klasifikasi Lokasi Pembangunan Sumber Air. Prosiding Seminar Nasional Riset Information Science (SENARIS), 1(September), 750. https://doi.org/10.30645/senaris.v1i0.81
Lasepa, R., Riyadi, S., Ramadhan, S., & Saputra, D. D. (2021). Analisis Sentimen Terhadap Perspektif Warganet Atas Tragedi Kanjuruhan Malang di Twitter Menggunakan Naïve Bayes Classifier. 8(1), 1–8.
Mutawalli, L., Zaen, M. T. A., & Bagye, W. (2019). KLASIFIKASI TEKS SOSIAL MEDIA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE (Studi Kasus Penusukan Wiranto). Jurnal Informatika Dan Rekayasa Elektronik, 2(2), 43. https://doi.org/10.36595/jire.v2i2.117
Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter. J-SAKTI (Jurnal Sains Komputer Dan Informatika), 5(2), 697–711.
Prastyawan, E. B. (2018). Stereotip dan Konflik Antar Suporter Sepakbola Persibat dan Persip Pekalongan. Persepsi : Communication Journal, 1(1), 1–14. https://doi.org/10.30596/persepsi.v1i1.2440
Qadrini L, Sepperwali A, & Aina A. (2021). Decision Treedan Adaboostpada Klasifikasi Penerima Program Bantuan Sosial. Decision Tree Dan Adaboost Pada Klasifikasi Penerima Program Bantuan Sosial, 2(7), 1959–1966.
Sari, F. V., & Wibowo, A. (2019). Analisis Sentimen Pelanggan Toko Online Jd.Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi. Jurnal SIMETRIS, 10(2), 681–686.
Suryani, P. S. M., Linawati, L., & Saputra, K. O. (2019). Penggunaan Metode Naïve Bayes Classifier pada Analisis Sentimen Facebook Berbahasa Indonesia. Majalah Ilmiah Teknologi Elektro, 18(1), 145. https://doi.org/10.24843/mite.2019.v18i01.p22
Syarifuddinn, M. (2020). Analisis Sentimen Opini Publik Mengenai Covid-19 Pada Twitter Menggunakan Metode Naïve Bayes Dan Knn. INTI Nusa Mandiri, 15(1), 23–28. https://doi.org/10.33480/inti.v15i1.1347
Utomo, D. P., & Mesran, M. (2020). Analisis Komparasi Metode Klasifikasi Data Mining dan Reduksi Atribut Pada Data Set Penyakit Jantung. Jurnal Media Informatika Budidarma, 4(2), 437. https://doi.org/10.30865/mib.v4i2.2080
Yulita, W., Dwi Nugroho, E., Habib Algifari, M., Studi Teknik Informatika, P., Teknologi Sumatera, I., Terusan Ryacudu, J., Huwi, W., Agung, J., & Selatan, L. (2021). Analisis Sentimen Terhadap Opini Masyarakat Tentang Vaksin Covid-19 Menggunakan Algoritma Naïve Bayes Classifier. Jdmsi, 2(2), 1–9.
Zulhidayat, M. (2018). Kewenangan Dan Peran Pemerintah Dalam Penyelenggaraan Komepetisi Sepak Bola Di Indonesia (the Authority and Role of Government in the Organizing of Football Competition in Indonesia). Jurnal Hukum Replik, 6(2), 222. https://doi.org/10.31000/jhr.v6i2.1446
Published
2022-09-13
How to Cite
Maulana, F., Abdullah, M., Sari, J., Siddik, D., Agustinus, M., & Saputra, D. (2022). SENTIMENT ANALYSIS ON THE TWITTER PSSI PERFORMANCE USING TEXT MINING WITH THE NAÏVE BAYES ALGORITHM. Jurnal Pilar Nusa Mandiri, 18(2), 211-216. https://doi.org/10.33480/pilar.v18i2.3938
Article Metrics
Abstract viewed = 283 times
PDF downloaded = 145 times
Abstract viewed = 283 times
PDF downloaded = 145 times
Section
Articles
Copyright (c) 2022 Fajrullah Maulana, M Arief Abdullah, Juwita Sari, Dimas Zappar Siddik, Matius Agustinus, Dedi Dwi Saputra
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
- Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
- Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.
