Analisis Sentimen Twitter Terhadap Perpindahan Ibu Kota Negara Ke IKN Nusantara Menggunakan Orange Data Mining
DOI:
https://doi.org/10.30865/klik.v4i1.957Keywords:
IKNNusantara; IKN; Twitter; Data Mining; OrangeAbstract
This study uses text mining which involves changing unstructured text to be structured and can be processed by a computer. In order to recognize important new patterns and ideas, several analytical techniques are used, including the text clustering method, Naive Bayes, and Support Vector Machines (SVM). Text Clustering analysis technique, which involves cluster analysis of text-based documents, can assist in categorizing and understanding unstructured text data using machine learning technology and Natural Language Processing (NLP) used in this process. This study aims to evaluate the community's response to the relocation of the national capital to Kalimantan. after going through the cleansing process, namely cleaning punctuation and characters, Transform Case, namely changing letters to lowercase, Tokenization is the process of dividing text sentences or paragraphs into certain parts, Stopwords Reducing the index in the text by removing some verbs, adjectives and other adverbs . The results of the analysis will be displayed in the form of a word cloud with words dominated by Indonesian and then Indonesian and distribution tables. The researcher collects 100 data via Twitter to become a dataset. The results of sentiment analysis with the Naive Bayes Classifier algorithm obtained results, namely 6 forms of emotion which were dominated by surprise (80%) and joy (50%), sadness (15% Sadness), fear (Fear) 10%, disgust (Disgust). ) 0% , angry (Anger) 0%.
Downloads
References
A. Liken Anggoro, L. Viola Prameswari Ken, and M. Gigih Setiawan, “Analisis Media Text Clustering pada Twitter Akan Kasus Selebriti Menggunakan Orange Data Mining,” Remik Ris. dan E-Jurnal Manaj. Inform. Komput., vol. 7, no. 1, pp. 189–195, 2023, doi: https://doi.org/10.33395/remik.v7i1.12001.
F. Sidik, I. Suhada, A. H. Anwar, and F. N. Hasan, “Analisis Sentimen Terhadap Pembelajaran Daring Dengan Algoritma Naive Bayes Classifier,” J. Linguist. Komputasional, vol. 5, no. 1, p. 34, 2022, doi: 10.26418/jlk.v5i1.79.
T. Ridwansyah, “Implementasi Text Mining Terhadap Analisis Sentimen Masyarakat Dunia Di Twitter Terhadap Kota Medan Menggunakan K-Fold Cross Validation Dan Naïve Bayes Classifier,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 2, no. 5, pp. 178–185, 2022, doi: 10.30865/klik.v2i5.362.
A. Wibowo, Firman Noor Hasan, Rika Nurhayati, and Arief Wibowo, “Analisis Sentimen Opini Masyarakat Terhadap Keefektifan Pembelajaran Daring Selama Pandemi COVID-19 Menggunakan Naïve Bayes Classifier,” J. Asiimetrik J. Ilm. Rekayasa Inov., vol. 4, pp. 239–248, 2022, doi: 10.35814/asiimetrik.v4i1.3577.
I. R. Afandi, F. N. Hasan, A. A. Rizki, N. Pratiwi, and Z. Halim, “Analisis Sentimen Opini Masyarakat Terkait Pelayanan Jasa Ekspedisi Anteraja Dengan Metode Naive Bayes,” J. Linguist. Komputasional, vol. 5, no. 2, pp. 63–70, 2022, doi: https://doi.org/10.26418/jlk.v5i2.107.
A. Yahyadi and F. Latifah, “Analisis Sentimen Twitter Terhadap Kebijakan PPKM di Tengah Pandemi COVID-19 Menggunakan Mode LSTM,” J. Inf. Syst. Applied, Manag. Account. Res., vol. 6, no. 2, pp. 464–471, 2022, doi: 10.52362/jisamar.v6i2.791.
R. K. Septiani, S. Anggraeni, and S. D. Saraswati, “Klasifikasi Sentimen Terhadap Ibu Kota Nusantara (IKN) pada Media Sosial Menggunakan Naive Bayes,” Teknika, vol. 16, no. 02, pp. 1–5, 2022, doi: https://doi.org/10.5281/zenodo.7535887.
H. Hairunnisa and W. A. Syaka, “Analysis of Political Communication in the Acceleration of Capital City Development Nusantara (Ikn) Towards a Sustainable City,” J. Gov. Polit., vol. 4, no. 1, p. 1, 2022, doi: https://doi.org/10.31764/jgop.v4i1.8193.
N. Yuliana and M. Atikurrahman, “Ibu Kota Negara yang (tak) Dirindukan: Kendi Nusantara, Jawaisme Jokowi, dan Semiologi Barthesian,” urnal Ilm. FONEMA J. Edukasi Bhs. Dan Sastra Indones., vol. 5, no. 2, pp. 104–128, 2022, doi: https://doi.org/10.25139/fn.v5i2.5044.
A. Ernawati, J. Wahyudi, A. D. Astuti, and S. Q. Aini, “Analisis Kelayakan Pendirian Usaha Pengolahan Limbah Medis untuk Meningkatkan Pendapatan Asli Daerah,” J. Ekon. dan Kebijak. Publik, vol. 13, no. 1, pp. 57–70, 2022, doi: 10.22212/jekp.v13i1.2155.
Syahril Dwi Prasetyo, Shofa Shofiah Hilabi, and Fitri Nurapriani, “Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN,” J. KomtekInfo, vol. 10, no. 1, pp. 1–7, 2023, doi: 10.35134/komtekinfo.v10i1.330.
A. Perdana, A. Hermawan, and D. Avianto, “Analisis Sentimen Terhadap Isu Penundaan Pemilu di Twitter Menggunakan Naive Bayes Clasifier,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 11, no. 2, pp. 195–200, 2022, doi: 10.32736/sisfokom.v11i2.1412.
R. Naquitasia, D. H. Fudholi, and L. Iswari, “Analisis Sentimen Berbasis Aspek pada Wisata Halal dengan Metode Deep Learning,” J. Teknoinfo, vol. 16, no. 2, p. 156, 2022, doi: 10.33365/jti.v16i2.1516.
N. Syahputri, “Penerapan Data Mining Asosiasi pada Pola Transaksi dengan Metode Apriori,” J. Sains Komput. Inform. (J-SAKTI, vol. 4, no. 2, pp. 728–736, 2020, doi: http://dx.doi.org/10.30645/j-sakti.v4i2.345.
M. Nizam Fadli, I. Sudahri Damanik, E. Irawan, S. Tunas Bangsa, and S. Utara, “Penerapan Metode Naive Bayes Dalam Menentukan Tingkat Kenyamanan Pada Rumah Sakit Terhadap Pasien,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 2, no. 3, pp. 117–122, 2021, [Online]. Available: https://djournals.com/klik
L. Irawan, L. H. Hasibuan, and F. Fauzi, “Analisa Prediksi Efek Kerusakan Gempa Dari Magnitudo (Skala Richter) Dengan Metode Algoritma Id3 Menggunakan Aplikasi Data Mining Orange,” J. Teknol. Inf. J. Keilmuan dan Apl. Bid. Tek. Inform., vol. 14, no. 2, pp. 189–201, 2020, doi: 10.47111/jti.v14i2.1079.
K. Sussolaikah, “Pemanfaatan Packages Pada R Programming Untuk Crawling Data Pada Social Media,” Build. Informatics, Technol. Sci., vol. 3, no. 3, pp. 203–206, 2021, doi: 10.47065/bits.v3i3.1035.
M. Z. Lubis, “Perancangan Aplikasi String Matching Dalam Pencarian Makanan Pantanagn Untuk Penderita Penyakit Kanker Dengan Algoritma Berry Ravindran Berbasis Android,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 1, no. 2, pp. 57–64, 2020, doi: https://doi.org/10.30865/klik.v1i2.49.
M. M. Mala Olhang, S. Achmadi, and F. . A. Wibisono, “Analisis Sentimen Pengguna Twitter Terhadap Covid-19 Di Indonesia Menggunakan Metode Naive Bayes Classifier (Nbc),” JATI (Jurnal Mhs. Tek. Inform., vol. 4, no. 2, pp. 214–221, 2020, doi: 10.36040/jati.v4i2.2695.
A. P. Giovani, A. Ardiansyah, T. Haryanti, L. Kurniawati, and W. Gata, “Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi,” J. Teknoinfo, vol. 14, no. 2, p. 115, 2020, doi: 10.33365/jti.v14i2.679.
A. N. Ulfah and M. K. Anam, “Analisis Sentimen Hate Speech Pada Portal Berita Online Menggunakan Support Vector Machine (SVM),” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 1, pp. 1–10, 2020, doi: 10.35957/jatisi.v7i1.196.
R. Sari and R. Y. Hayuningtyas, “Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Pada Wisata TMII Berbasis Website,” Indones. J. Softw. Eng., vol. 5, no. 2, pp. 51–60, 2019, doi: 10.31294/ijse.v5i2.6957.
M. Hudha, E. Supriyati, and T. Listyorini, “Analisis Sentimen Pengguna Youtube Terhadap Tayangan #Matanajwamenantiterawan Dengan Metode Naïve Bayes Classifier,” JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 1, pp. 1–6, 2022, doi: 10.33387/jiko.v5i1.3376.
E. Ramadanti and M. Muslih, “Penerapan Data Mining Algoritma K-Means Clustering Pada Populasi Ayam Petelur Di Indonesia,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 7, no. 1, pp. 1–7, 2022, doi: 10.36341/rabit.v7i1.2155.
H. Andriana, S. S. Hilab, and ..., “Penerapan Metode K-Nearest Neighbor pada Sentimen Analisis Pengguna Twitter terhadap KTT G20 di Indonesia,” JURIKOM (Jurnal …, vol. 10, no. 1, pp. 60–67, 2023, doi: 10.30865/jurikom.v10i1.5427.
A. N. Rahmad and F. S. Pribadi, “Analisis Tren Pertemuan Tatap Muka Terbatas dari Persepsi Warganet pada Twitter Menggunakan Topic Modeling,” Edu Komputika J., vol. 5, no. 1, pp. 33–43, 2018, doi: https://doi.org/10.15294/edukomputika.v9i2.62076.
R. A. raffaidy Wiguna and A. I. Rifai, “Analisis Text Clustering Masyarakat Di Twitter Mengenai Omnibus Law Menggunakan Orange Data Mining,” J. Inf. Syst. Informatics, vol. 3, no. 1, pp. 1–12, 2021, doi: 10.33557/journalisi.v3i1.78.
A. D. Akmal, I. Permana, H. Fajri, and Y. Yuliarti, “Opini Masyarakat Twitter terhadap Kandidat Bakal Calon Presiden Republik Indonesia Tahun 2024,” J. Manaj. dan Ilmu Adm. Publik, vol. 4, no. 4, pp. 292–300, 2022, doi: 10.24036/jmiap.v4i4.160.
H. Irsyad and M. R. Pribadi, “Klasifikasi Opini Masyarakat Terhadap Pertanian di Indonesia dengan Naive Bayes pada Twitter,” J. Tek. Inform. Unika St. Thomas, vol. 4, no. 1, pp. 89–98, 2019, doi: https://doi.org/10.17605/jti.v4i1.515.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Sentimen Twitter Terhadap Perpindahan Ibu Kota Negara Ke IKN Nusantara Menggunakan Orange Data Mining
ARTICLE HISTORY
Issue
Section
Copyright (c) 2023 Hafizh Dhery Al Assyam, Firman Noor Hasan
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).
