Analisis Sentimen Komentar Netizen Terhadap Pembubaran Konser NCT 127 Menggunakan Metode Naive Bayes


  • Nisa Qonita Rizkina Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, Indonesia
  • Firman Noor Hasan * Mail Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, Indonesia
  • (*) Corresponding Author
Keywords: Sentiment Analysis; NCT 127; Twitter; Rapidminer; Naïve Bayes

Abstract

The present rate of technological advancement has resulted in the rapid spread of information, which is easily available through social media platforms such as Twitter. Users of Twitter can send and read content in the form of text or videos using the facilities that Twitter itself offers. Numerous Twitter users have commented on the NCT 127 concert's recent dissolution, which has drawn both supportive and critical remarks. A dataset of 2451 tweets was created by gathering information from Twitter using the keyword "nct" between November 4 and November 6, 2022. The data was subsequently cleaned, yielding a total of 2451 useable data points. Labeling and the Naive Bayes algorithm were then applied to the data. The goal of this study was to count the number of favorable and unfavorable tweets and evaluate how well the Naive Bayes algorithm was applied. According to the trials done, there were 559 favorable remarks and 1,892 negative ones. The accuracy of the evaluation tests was 82.01%. Additionally, the analysis of negative sentiment produced a f1-score of 79.21%, a recall of 68.52%, and precision of 93.84%. Contrarily, the evaluation of positive attitude produced a f1-score of 84.15%, a recall of 95.50%, and a precision of 75.21%. The Naive Bayes method, it may be inferred, can categorize and process with a very consistent accuracy that approaches near-perfect outcomes.

Downloads

Download data is not yet available.

Author Biographies

Nisa Qonita Rizkina, Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta

Program Studi Teknik Informatika

Firman Noor Hasan, Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta

Program Studi Teknik Informatika

References

N. Magfirah Syahmar, I. I. Idrus, M. Ridwan, and S. Ahmad, “BUDAYA K-POP DAN KEHIDUPAN SOSIAL REMAJA (STUDI KOMUNITAS K-POP MAKASSAR: NCT-Zen MAKASSAR),” Agustus, vol. 3, no. 1, 2022.

D. Duei Putri, G. F. Nama, and W. E. Sulistiono, “Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 10, no. 1, Jan. 2022, doi: 10.23960/jitet.v10i1.2262.

M. A. Djamaludin, A. Triayudi, and E. Mardiani, “Analisis Sentimen Tweet KRI Nanggala 402 di Twitter menggunakan Metode Naïve Bayes Classifier,” Jurnal Teknologi Informasi dan Komunikasi), vol. 6, no. 2, p. 2022, 2022, doi: 10.35870/jti.

F. Rizali Rakhman, R. Wulan Ramadhani, and Y. Ari Kuncoroyakti, “ANALISIS SENTIMEN DAN OPINI DIGITAL KAMPANYE 3M DI MASA COVID-19 MELALUI MEDIA SOSIAL TWITTER,” Maret, vol. 18, no. 8, 2021, doi: https://doi.org/10.47007/jkomu.v18i01.301.

D. A. Hidayati, S. Dini, R. Fitriani, and S. Habibah, “Realitas Sosial Remaja Penggemar Budaya Korea (K-POP) di Bandar Lampung,” RESIPROKAL, vol. 4, no. 2, pp. 212–232, 2022.

D. Guna Memenuhi Persyaratan et al., “BUDAYA POPULER KOREA SELATAN (K-POP) DAN PERILAKU KONSUMTIF PENGGEMAR GRUP MUSIK KOREA SELATAN: STUDI KASUS EXO-L MARKAS LOTTO,” Universitas Islam Negeri Syarif Hidayatullah, Jakarta, 2020.

M. Ananda, N. Hadi, and N. H. P. Meiji, “Di balik perilaku konsumtif NCTZEN dalam pembelian merchaindise NCT (studi kasus komunitas NCTzen Malang),” Jurnal Integrasi dan Harmoni Inovatif Ilmu-Ilmu Sosial, vol. 1, no. 9, pp. 1011–1026, 2021, doi: 10.17977/um063v1i92021p1011-1026.

M. Kantardics, Data Mining: Concept, models, methods, and algorithms, Third Edition., vol. 03. Piscataway, NJ: John Wiley & Son, Inc, 2020.

R. Amelia, N. S. Prastiwi, and M. E. Purbaya, “Impementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Masyarakat Indonesia Mengenai Drama Korea Pada Twitter,” Jurnal Riset Komputer, vol. 9, no. 2, pp. 338–343, 2022, doi: 10.30865/jurikom.v9i2.3895.

P. Nurmawati, E. Supriyati, and T. Listyorini, “ANALISIS SENTIMEN TERHADAP PENGGEMAR K-POP DI MEDIA SOSIAL TWITTER MENGGUNAKAN NAIVE BAYES (STUDI KASUS PENGGEMAR GRUP BTS),” JIEET (Journal Information Engineering and Educational Technology), vol. 04, no. 02, 2020, doi: https://doi.org/10.26740/jieet.v4n2.p86-89.

N. M. A. J. Astari, Dewa Gede Hendra Divayana, and Gede Indrawan, “Analisis Sentimen Dokumen Twitter Mengenai Dampak Virus Corona Menggunakan Metode Naive Bayes Classifier,” Jurnal Sistem dan Informatika (JSI), vol. 15, no. 1, pp. 27–29, Nov. 2020, doi: 10.30864/jsi.v15i1.332.

L. Aji Andika and P. Amalia Nur Azizah, “Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier,” Indonesian Journal of Applied Statistics, vol. 2, no. 1, 2019, doi: https://doi.org/10.13057/ijas.v2i1.29998.

A. Wandani, “Sentimen Analisis Pengguna Twitter pada Event Flash Sale Menggunakan Algoritma K-NN, Random Forest, dan Naive Bayes,” Jurnal Sains Komputer & Informatika (J-SAKTI, vol. 5, no. 2, pp. 651–665, Sep. 2021, doi: http://dx.doi.org/10.30645/j-sakti.v5i2.365.

A. Wibowo, F. Noor Hasan, R. Nurhayati, and dan Arief Wibowo, “Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi Analisis Sentimen Opini Masyarakat Terhadap Keefektifan Pembelajaran Daring Selama Pandemi COVID-19 Menggunakan Naïve Bayes Classifier,” Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa dan Inovasi, vol. 4, no. 2, pp. 239–248, Jul. 2022, doi: https://doi.org/10.35814/asiimetrik.v4i1.3577.

A. I. Tanggraeni and M. N. N. Sitokdana, “Analisis Sentimen Aplikasi E-Government Pada Google Play Menggunakan Algoritma Naïve Bayes,” vol. 9, no. 2, pp. 785–795, 2022, doi: https://doi.org/10.35957/jatisi.v9i2.1835.

A. Muzaki and A. Witanti, “SENTIMENT ANALYSIS OF THE COMMUNITY IN THE TWITTER TO THE 2020 ELECTION IN PANDEMIC COVID-19 BY METHOD NAIVE BAYES CLASSIFIER,” Jurnal Teknik Informatika (Jutif), vol. 2, no. 2, pp. 101–107, Mar. 2021, doi: 10.20884/1.jutif.2021.2.2.51.

A. Bagus Sasmita, B. Rahayudi, and L. Muflikhah, “Analisis Sentimen Komentar pada Media Sosial Twitter tentang PPKM Covid-19 di Indonesia dengan Metode Naïve Bayes,” vol. 6, no. 3, pp. 1208–1214, 2022, [Online]. Available: http://j-ptiik.ub.ac.id

S. H. Ramadhani and M. I. Wahyudin, “Analisis Sentimen Terhadap Vaksinasi Astra Zeneca pada Twitter Menggunakan Metode Naïve Bayes dan K-NN,” Jurnal Teknologi Informasi dan Komunikasi), vol. 6, no. 4, p. 2022, 2022, doi: 10.35870/jti.

I. Mulya and C. M. Karyati, “Analisis Sentimen Terhadap Universitas Gunadarma Berdasarkan Opini Pengguna Twitter Menggunakan Metode Naive Bayes Classifier,” Jurnal Ilmiah Komputasi, vol. 19, no. 4, Dec. 2020, doi: 10.32409/jikstik.19.4.354.

R. Slamet, W. Gata, A. Novtariany, K. Hilyati, and F. A. Jariyah, “Analisis Sentimen Twitter Terhadap Penggunaan Artis Korea Selatan Sebagai Brand Ambassador Produk Kecantikan Lokal,” INTECOMS: Journal of Information Technology and Computer Science, vol. 5, no. 1, pp. 145–153, 2022, doi: 10.31539/intecoms.v5i1.3933.

A. Kusuma and A. Nugroho, “Analisa Sentimen Pada Twitter Terhadap Kenaikan Tarif Dasar Listrik Dengan Metode Naïve Bayes,” Jurnal Ilmiah Teknologi Informasi Asia, vol. 15, no. 2, Dec. 2021, doi: https://doi.org/10.32815/jitika.v15i2.557.

D. Anjas Ramadhan and E. Budi Setiawan SSi, “ANALISIS SENTIMEN PROGRAM ACARA DI SCTV PADA TWITTER MENGGUNAKAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE,” vol. 06, p. 9736, Aug. 2019.

D. Rusdiaman and D. Rosiyadi, “ANALISA SENTIMEN TERHADAP TOKOH PUBLIK MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE,” CESS (Journal of Computer Engineering System and Science), vol. 4, no. 2, pp. 2502–7131, 2019.

A. Azis Adjie Sumanjaya and A. Ridok, “Analisis Sentimen Data Tweets terhadap Penanganan Covid-19 di Indonesia menggunakan Metode Naïve Bayes dan Pemilihan Kata Bersentimen menggunakan Lexicon Based,” 2022. [Online]. Available: http://j-ptiik.ub.ac.id

N. Hardi et al., “SISTEMASI: Jurnal Sistem Informasi Analisis Sentimen Physical Distancing pada Twitter Menggunakan Text Mining dengan Algoritma Naive Bayes Classifier,” SISTEMASI: Jurnal Sistem Informasi, vol. 10, no. 1, pp. 131–138, Jan. 2021, doi: https://doi.org/10.32520/stmsi.v10i1.1118.

F. Noor, H. #1, S. #2, and P. Afikah, “Sentiment Analysis of Community Response on Cooking Oil Price Increase Policy with Naïve Bayes Classifier Algorithm,” JLK, vol. 5, no. 2, Sep. 2022, doi: https://doi.org/10.26418/jlk.v5i2.99.

I. R. Afandi, F. Noor, H. #2, A. A. Rizki, N. Pratiwi, and Z. Halim, “Analisis Sentimen Opini Masyarakat Terkait Pelayanan Jasa Ekspedisi Anteraja Dengan Metode Naive Bayes,” JLK, vol. 5, no. 2, 2022, doi: https://doi.org/10.26418/jlk.v5i2.107.

G. Shmueli, P. C. Bruce, A. V Deokar, and N. R. Patel, Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner, First Edition., vol. 1. Hoboken, USA: John Wiley & Son, Inc, 2023.

I. R. Afandi, I. F. Hanif, F. N. Hasan, E. Sinduningrum, Z. Halim, and N. Pratiwi, “Analisis Sentimen Opini Masyarakat Terkait Penyelenggaraan Sistem Elektronik Menggunakan Metode Logistic Regression,” Jurnal Linguistik Komputasional, vol. Vol 5, No 2, Sep. 2022, doi: https://doi.org/10.26418/jlk.v5i2.103.


Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Analisis Sentimen Komentar Netizen Terhadap Pembubaran Konser NCT 127 Menggunakan Metode Naive Bayes

Dimensions Badge
Article History
Submitted: 2023-07-07
Published: 2023-07-26
Abstract View: 2093 times
PDF Download: 1315 times
How to Cite
Rizkina, N., & Hasan, F. N. (2023). Analisis Sentimen Komentar Netizen Terhadap Pembubaran Konser NCT 127 Menggunakan Metode Naive Bayes. Journal of Information System Research (JOSH), 4(4), 1136-1144. https://doi.org/10.47065/josh.v4i4.3803
Issue
Section
Articles