Visualisasi Data Analisa Sentimen RUU Omnibus Law Kesehatan Menggunakan KNN dengan Software RapidMiner

Tupari Tupari, Syaukani Abdullah, Chairani Chairani


The government's decision to discuss the RUU Omnibus law on health has become a controversial topic in society, especially among users of the Twitter social media platform. Users express their opinions regarding their stance on the RUU Omnibus law through tweets on Twitter. With diverse comments from users, it is essential to classify and visualize them into useful information about the positive and negative sentiments towards the RUU on health. This is crucial to understand the public's response to this policy. A total of 2406 sentiment data from Twitter users were collected using the RapidMiner software. Before analyzing the data using the K-Nearest Neighbors (KNN) algorithm, data preprocessing was carried out. After preprocessing, 2.406 data points were obtained, which were then divided into 1.684 tweets for testing and 722 tweets for training. The data was then processed using the KNN algorithm model executed in the RapidMiner software. The results of the data processing were presented in the form of tables, graphs, and word clouds, aligning with the research objective of providing clear and easily understandable visualizations about the RUU on health. This facilitates understanding for stakeholders without technical backgrounds to grasp the meaning and sentiments expressed. The research results indicate that the testing of K-Nearest Neighbors (KNN) yielded a high accuracy value, making it well-visualized at 84.58%. This indicates that the KNN model is highly successful in analyzing Twitter users' opinions on the Health Omnibus Law based on the data used and its ability to visualize effectively


visualisasi data, analisa sentimen, RUU omnibuslaw Kesehatan, KNN, RapidMiner

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DPR-RI, “Paripurna DPR Tetapkan UU Kesehatan, Regulasi Komprehensif di Bidang Kesehatan,” 2023. DPR Tetapkan UU Kesehatan, Regulasi Komprehensif di Bidang Kesehatan#:~:text=Rapat Paripurna DPR RI mengesahkan,11%2F7%2F2023). (diakses Sep 17, 2023).

DPR-RI, “Dua Fraksi di DPR Tolak Pengesahan UU Kesehatan,” 2023. Fraksi di DPR Tolak Pengesahan UU Kesehatan (diakses Sep 17, 2023).

Z. Abidin, “RUU ‘Omnibus law’ Kesehatan Versus Uniknya Profesi Dokter,” 2023. (diakses Sep 17, 2023).

N. KATINGKA, “Perlu Transformasi, Koalisi Organisasi Nakes Dukung RUU Kesehatan,” 2023. (diakses Sep 17, 2023).

Z. Alhaq, A. Mustopa, S. Mulyatun, dan J. D. Santoso, “Penerapan Metode Support Vector Machine Untuk Analisis Sentimen Pengguna Twitter,” J. Inf. Syst. Manag., vol. 3, no. 2, hal. 44–49, 2021, doi: 10.24076/joism.2021v3i2.558.

H. Sujadi, “Analisis Sentimen Pengguna Media Sosial Twitter Terhadap Wabah Covid-19 Dengan Metode Naive Bayes Classifier Dan Support Vector Machine,” Infotech J., vol. 8, no. 1, hal. 22–27, 2022, doi: 10.31949/infotech.v8i1.1883.

A. Wandani, Fauziah, dan Andrianingsih, “Sentimen Analisis Pengguna Twitter pada Event Flash Sale Menggunakan Algoritma K-NN, Random Forest, dan Naive Bayes,” J-Sakti, vol. 5, no. 2, hal. 651–665, 2021.

B. P. Zen, D. Wicaksana, dan H. Alfidzar, “Analisis Sentimen Tweet Vaksin Covid 19 Sinovac Menggunakan Metode Support Vector Machine,” Jdmsi, vol. 3, no. 2, hal. 21–27, 2022.

F. Hashfi, D. Sugiarto, dan I. Mardianto, “Sentiment Analysis of An Internet Provider Company Based on Twitter Using Support Vector Machine and Naïve Bayes Method,” J. Tek. Inform., vol. 14, no. 1, hal. 1–6, 2022.

A. P. Giovani, A. Ardiansyah, T. Haryanti, L. Kurniawati, dan W. Gata, “Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi,” J. Teknoinfo, vol. 14, no. 2, hal. 115, 2020, doi: 10.33365/jti.v14i2.679.

V. Kevin, S. Que, A. Iriani, dan H. D. Purnomo, “Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization (Online Transportation Sentiment Analysis Using Support Vector Machine Based on Particle Swarm Optimization),” vol. 9, no. 2, hal. 162–170, 2020.

G. Guslendra, S. Defit, dan R. Bastola, “K-Means and K-NN Methods For Determining Student Interest,” Int. J. Artif. Intell. Res., vol. 6, no. 1, 2022, doi: 10.29099/ijair.v6i1.222.

V. Dwi Antonio, S. Efendi, dan H. Mawengkang, “Sentiment analysis for covid-19 in Indonesia on Twitter with TF-IDF featured extraction and stochastic gradient descent,” Int. J. Nonlinear Anal. Appl, vol. 13, no. 1, hal. 2008–6822, 2022, [Daring]. Tersedia pada:

Y. Setiawan, “Data Mining berbasis Nearest Neighbor dan Seleksi Fitur untuk Deteksi Kanker Payudara,” J. Pengemb. IT, vol. 8, no. 2, hal. 89–96, 2023, [Daring]. Tersedia pada:

S. Sahara dan R. A. Permana, “Metode KNN Pada Sentiment Analisis Review Produk Game Android,” Indones. J. Netw. Secur., vol. 11, no. 2, hal. 123–128, 2022.

E. Undamayanti, T. I. Hermanto, dan I. Kaniawulan, “Analisis Sentimen Menggunakan Metode Naive Bayes Berbasis Particle Swarm Optimization Terhadap Pelaksanaan Program Merdeka Belajar Kampus Merdeka,” J. Sains Komput. Inform., vol. 6, no. 2, hal. 916–930, 2022.

A. P. J. Dwitama, “Deteksi Ujaran Kebencian Pada Twitter Bahasa Indonesia Menggunakan Machine Learning: Reviu Literatur,” J. SNATi, vol. 1, no. 1, hal. 31–39, 2021.



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