Analisis Sentimen Komentar Pengguna Instagram Mengenai Pelaksanaan Pemilu 2024 dengan Naïve Bayes dan Lexicon-Based
- Cahyani Rahma Dewi Universitas Nasional, Jakarta, Indonesia
-
Agus Iskandar
*
Universitas Nasional, Jakarta,
Indonesia
(*) Corresponding Author
Abstract
The debate surrounding the implementation of the 2024 General Election has taken centre stage in Indonesia, especially on social media platforms that are favoured by the public. The change of leaders in Indonesia and the emotional differences that emerge in society are of significant concern. The search for leadership figures brings up various complex theoretical, conceptual, and cultural perspectives. This paper aims to analyse people's sentiment related to the 2024 general election by classifying sentiment as positive, negative, or neutral, aiding understanding of people's perceptions of candidates, relevant political issues, and voter behaviour patterns. The methodology involved collecting data using scrapping techniques from the social media platform Instagram using a combination of both Naïve Bayes Classifier and Lexicon-Based labelling algorithms. These two methods were used to conduct sentiment analysis towards the general election in this study. Sentiment analysis of the 2024 General Election using the Naive Bayes and InSet Lexicon models showed good results with an accuracy of 72% (precision negative 74%, neutral 54%, positive 70%; recall positive 62%, neutral 22%, negative 87%). This study successfully surpassed the accuracy of the previous model (72% accuracy, 70% precision, 72% recall) and revealed that negative sentiments were more prevalent in public opinion towards the 2024 General Election. This indicates that there is public dissatisfaction and doubt regarding the implementation of the election, which is thought to be triggered by technical problems and political uncertainty.
Downloads
References
Jimmy, E. H. Hermaliani, dan L. Kurniawati, “Analisis Klasifikasi Sentimen Pengguna Media Sosial Twitter Terhadap Penundaan Pemilu Presiden Tahun 2024,” J. Indones. Manaj. Inform. dan Komun., vol. 4, no. 2, hal. 570–579, 2023, doi: https://doi.org/10.35870/jimik.v4i2.243.
A. S. Afif dan A. R. Pratama, “Analisis Sentimen Kebijakan Pendidikan di Masa Pandemi COVID-19 dengan CrowdTangle di Instagram,” Automata, vol. 2, no. 2, hal., 2021, [Daring]. Tersedia pada: https://journal.uii.ac.id/AUTOMATA/article/view/19429
T. D. Putra, E. Utami, dan M. P. Kurniawan, “Analisis Sentimen Pemilu 2024 dengan Naive Bayes Berbasis Particle Swarm Optimization (PSO),” Explore, vol. 13, no. 1, hal. 1–5, 2023, [Daring]. Tersedia pada: https://www.journal.utmmataram.ac.id/index.php/explore/article/download/13/9
D. P. Kussanti dan F. Azizi, “Company Profile Komisi Pemilihan Umum Republik Indonesia Sebagai Media Informasi Kepada Publik Eksternal,” J. Public Relations, vol. 2, no. 1, hal. 67–71, 2021, doi: https://doi.org/10.31294/jpr.v2i1.513.
A. R. Alifvia dan U. Saprudin, “Analisis Sentimen Review Data Twitter Komisi Pemilihan Umum (KPU) Menggunakan Metode Naïve Bayes,” J. Inf. dan Komput., vol. 11, no. 01, hal. 81–84, 2023, doi: https://doi.org/10.35959/jik.v11i01.407.
R. Vindua dan A. U. Zailani, “Analisis Sentimen Pemilu Indonesia Tahun 2024 Dari Media Sosial Twitter Menggunakan Python,” JURIKOM (Jurnal Ris. Komputer), vol. 10, no. 2, hal. 479–487, 2024, doi: 10.30865/jurikom.v10i2.5945.
F. A. Artanto, “Support Vector Machine Berbasis Particle Swarm Optimization Pada Analisis Sentimen Anggota KPPS,” J. FASILKOM (teknologi Inf. dan ILmu KOMputer), vol. 14, no. 1, hal. 75–79, 2024, doi: https://doi.org/10.37859/jf.v14i1.6795.
D. S. Nugroho, I. F. Hanif, M. A. Hasbi, F. Fredianto, A. M. Saputra, dan R. Zildjian, “Analisis Sentimen Dugaan Pelanggaran Pemilu 2024 Berdasarkan Tweet Menggunakan Algoritma Naïve Bayes Classifier,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 3, hal. 1169–1176, 2024, doi: https://doi.org/10.57152/malcom.v4i3.1496.
E. Hasibuan dan E. A. Heriyanto, “Analisis Sentimen Pada Ulasan Aplikasi Amazon Shopping Di Google Play Store Menggunakan Naive Bayes Classifier,” J. Tek. Dan Sci., vol. 1, no. 3, hal. 13–24, 2022, doi: https://doi.org/10.56127/jts.v1i3.434.
O. Manullang, C. Prianto, dan N. H. Harani, “Analisis Sentimen Untuk Memprediksi Hasil Calon Pemilu Presiden Menggunakan Lexicon Based Dan Random Forest,” J. Ilm. Inform., vol. 11, no. 02, hal. 159–169, 2023, doi: https://doi.org/10.33884/jif.v11i02.7987.
S. Ratnaswari, N. C. Wibowo, dan D. S. Y. Kartika, “ANALISIS SENTIMEN MENGGUNAKAN METODE LEXICON-BASED DAN SUPPORT VECTOR MACHINE PADA PRESIDEN DAN WAKIL PRESIDEN INDONESIA PERIODE 2024–2029,” J. Inform. dan Tek. Elektro Terap., vol. 13, no. 1, hal. 362–368, 2025, doi: http://dx.doi.org/10.23960/jitet.v13i1.5604.
M. Saputra dan M. Iqbal, “Analisis Sentimen Masyarakat Terhadap PON XII Aceh-Sumut Menggunakan Algoritma Naïve Bayes,” J. Sist. Inf. dan Sist. Komput., vol. 10, no. 1, hal. 39–48, 2025, doi: https://doi.org/10.51717/simkom.v10i1.679.
V. F. Lestari, P. Arsi, dan P. Subarkah, “Sentimen Analisis Evaluasi Pengguna Aplikasi Orbit Telkomsel Pada Ulasan Playstore Menggunakan Algoritma Naïve Bayes,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 9, no. 4, hal. 2244–2255, 2024, doi: https://doi.org/10.29100/jipi.v9i4.5538.
D. P. Yani, “Klasifikasi Sentimen Transformasi dan Reformasi Sepak Bola Indonesia Pada Twitter Menggunakan Algoritma Bernoulli Naïve Bayes,” Univ. Islam Negeri Sultan Syarif Kasim Riau Repos., vol. 4, no. 3, hal. 452–458, 2023, [Daring]. Tersedia pada: http://repository.uin-suska.ac.id/id/eprint/71577
D. Abimanyu, “Analisis Sentimen Akun Twitter Apex Legends Menggunakan VADER,” Univ. Islam Negeri Sultan Syarif Kasim Riau Repos., vol. 5, no. 03, hal. 423–431, 2022, [Daring]. Tersedia pada: http://repository.uin-suska.ac.id/id/eprint/61480
J. Supriyanto, D. Alita, dan A. R. Isnain, “Penerapan Algoritma K-Nearest Neighbor (K-NN) Untuk Analisis Sentimen Publik Terhadap Pembelajaran Daring,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 4, no. 1, hal. 74–80, 2023, doi: https://doi.org/10.33365/jatika.v4i1.2468.
D. D. Putri, G. F. Nama, dan W. E. Sulistiono, “Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier,” J. Inform. dan Tek. Elektro Terap., vol. 10, no. 1, hal. 34–40, 2022, doi: http://dx.doi.org/10.23960/jitet.v10i1.2262.
M. I. Maulana, “Klasifikasi Sentiment Ulasan Aplikasi Sausage Man Menggunakan VADER Lexicon dan Naïve Bayes Classifier,” Univ. Islam Negeri Sultan Syarif Kasim Riau Repos., vol. 4, no. 3, hal. 485–492, 2023, [Daring]. Tersedia pada: http://repository.uin-suska.ac.id/id/eprint/71622
D. Darwis, N. Siskawati, dan Z. Abidin, “Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Review Data Twitter BMKG Nasional,” J. Tekno Kompak, vol. 15, no. 1, hal. 131–145, 2021, doi: 10.33365/jtk.v15i1.744.
H. Prasetyo, G. A. Buntoro, dan D. Mustikasari, “Analisis Sentimen Pada Channel Autonetmagz Terhadap Review Mobil Almaz 2019 Dengan Metode Naive Bayes Classifier Dan Lexicon Based,” Komputek J. Tek. Univ. Muhammadiyah Ponorogo, vol. 4, no. 1, hal. 58–70, 2020, [Daring]. Tersedia pada: https://studentjournal.umpo.ac.id/index.php/komputek
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Sentimen Komentar Pengguna Instagram Mengenai Pelaksanaan Pemilu 2024 dengan Naïve Bayes dan Lexicon-Based
Pages: 456-471
Copyright (c) 2024 Cahyani Rahma Dewi, Agus Iskandar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are 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 an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their 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 published work (Refer to The Effect of Open Access).
