Analisa Sentimen Rencana Pemindahan Ibu Kota Nusantara dari Jakarta ke Kalimantan Timur Menggunakan Algoritma Naïve Bayes
Article Sidebar
Main Article Content
Abstract
Article Summary
The relocation of Indonesia's capital city has been a highly debated topic in recent years. This decision has sparked various opinions and views from the public, especially as reflected on social media platforms like Twitter. This research aims to understand the perspectives and attitudes of Indonesian citizens regarding the plan to move the capital to Nusantara, as expressed on Twitter. Sentiment analysis using the Naïve Bayes algorithm is employed to classify tweets into positive and negative sentiment categories. By leveraging data from Twitter, this study aims to provide deeper insights into public perception and attitudes towards the relocation of the government center. The research used a dataset of 375 tweets. The results, analyzed using RapidMiner, show that the precision for positive sentiment is 94.89% and for negative sentiment is 65.00%. The recall for negative sentiment is 48.15%, while the recall for positive sentiment reaches 97.38%. The model's accuracy is 92.82%, indicating that the Naïve Bayes algorithm can effectively classify sentiments using the available data. This study offers insights into public support and opposition, as well as the factors influencing their attitudes towards the capital relocation plan.
Keywords
Article Keywords
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 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 a Creative Commons Attribution License (CC-BY 4.0) that allows others to share the work with an acknowledgement 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 acknowledgement 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.
License: All articles are licensed under 
Ananda, D., & Suryono, R. R. (2024). Analisis Sentimen Publik Terhadap Pengungsi Rohingya di Indonesia dengan Metode Support Vector Machine dan Naï ve Bayes. JURNAL MEDIA INFORMATIKA BUDIDARMA, 8(2), 748-757. DOI: http://dx.doi.org/10.30865/mib.v8i2.7517.
Darwis, D., Siskawati, N., & Abidin, Z. (2021). Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Review Data Twitter Bmkg Nasional. Jurnal Tekno Kompak, 15(1), 131-145.
Hakim, Z. R., & Sugiyono, S. (2024). Analisa Sentimen Terhadap Kereta Cepat Jakarta–Bandung Menggunakan Algoritma Naïve Bayes Dan K-Nearest Neighbor. Jurnal Sains dan Teknologi, 5(3), 939-945. DOI: https://doi.org/10.55338/saintek.v5i3.1423.
Hasri, C. F., & Alita, D. (2022). Penerapan Metode Naïve Bayes Classifier Dan Support Vector Machine Pada Analisis Sentimen Terhadap Dampak Virus Corona Di Twitter. Jurnal informatika dan rekayasa perangkat lunak, 3(2), 145-160. DOI: https://doi.org/10.33365/jtk.v15i1.744.
Juanita, S. (2020). Analisis sentimen persepsi masyarakat terhadap pemilu 2019 pada media sosial twitter menggunakan naive bayes. Jurnal Media Informatika Budidarma, 4(3), 552-558. DOI: http://dx.doi.org/10.30865/mib.v4i3.2140.
Kusuma, A., & Nugroho, A. (2021). Analisa Sentimen Pada Twitter Terhadap Kenaikan Tarif Dasar Listrik Dengan Metode Naïve Bayes. Jurnal Ilmiah Teknologi Informasi Asia, 15(2), 137-146. DOI: https://doi.org/10.32815/jitika.v15i2.557.
Naraswati, N. P. G., Nooraeni, R., Rosmilda, D. C., Desinta, D., Khairi, F., & Damaiyanti, R. (2021). Analisis Sentimen Publik dari Twitter Tentang Kebijakan Penanganan Covid-19 di Indonesia dengan Naive Bayes Classification. Sistemasi: Jurnal Sistem Informasi, 10(1), 222-238. DOI: https://doi.org/10.32520/stmsi.v10i1.1179.
Permadi, V. A. (2020). Analisis sentimen menggunakan algoritma Naïve Bayes terhadap review restoran di Singapura. Jurnal Buana Informatika, 11(2), 141-151. DOI: https://doi.org/10.24002/jbi.v11i2.3769.
Safira, A., & Hasan, F. N. (2023). Analisis Sentimen Masyarakat Terhadap Paylater Menggunakan Metode Naive Bayes Classifier. ZONAsi: Jurnal Sistem Informasi, 5(1), 59-70. DOI: https://doi.org/10.31849/zn.v5i1.12856.