Analisis Sentimen terhadap RSUD Salatiga Menggunakan SVM dan TF-IDF
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Jan 10, 2025
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The Salatiga Regional General Hospital (RSUD) plays an important role in providing healthcare services. This research analyzes public sentiment towards RSUD Salatiga using the SVM method with a linear kernel for sentiment analysis and TF-IDF for feature extraction. The dataset consists of 414 processed reviews, including case folding, data cleaning, tokenization, normalization, stopword removal, and stemming. Evaluation shows that the model achieved an accuracy of 84.00%; precision of 84.00%; recall of 83.25%; and an F1-score of 83.53%. A total of 55.8% of reviews indicated positive sentiment and 44.2% negative sentiment, highlighting the need for improvements in the queue system, waiting times, and parking facilities. The SVM and TF-IDF methods were chosen for their ability to handle large text data with high accuracy. This research provides practical contributions in the form of recommendations such as the implementation of a technology-based queue system. Limitations include the limited amount of data and platform bias, so exploring other algorithms, such as Naive Bayes and Random Forest, is recommended.
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Azzahra, W. L., & Mailoa, E. (2025). Analisis Sentimen terhadap RSUD Salatiga Menggunakan SVM dan TF-IDF. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 6(1), 478-489. https://doi.org/10.35870/jimik.v6i1.1208
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References
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Septiani, D., & Isabela, I. (2022). Analisis term frequency inverse document frequency (tf-idf) dalam temu kembali informasi pada dokumen teks. Sistem dan Teknologi Informasi Indonesia (SINTESIA), 1(2), 81-88.
Astuti, Y., Ruldeviyani, Y., Salbari, F., & Prayogi, A. (2023). Sentiment Analysis of Electricity Company Service Quality Using Naïve Bayes. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 7(2), 389-396. https://doi.org/10.29207/resti.v7i2.4627.
Atika, D., Styawati, S., & Aldino, A. A. (2022). Term Frequency-Inverse Document Frequency Support Vector Machine untuk Analisis Sentimen Opini Masyarakat terhadap Tekanan Mental pada Media Sosial Twitter. Jurnal Teknologi dan Sistem Informasi, 3(4), 86-97. https://doi.org/10.33365/jtsi.v3i4.2054.
Batubara, A. S. R. T., & Maharani, W. (2020). Kecendrungan Sentimen Dengann Pendekatan Support Vector Machine Pada Komunitas Yang Berpengaruh Di Twitter. eProceedings of Engineering, 7(3).
Cristianini, N. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press.
Ditami, G. R., Ripanti, E. F., & Sujaini, H. (2022). Implementasi Support Vector Machine untuk Analisis Sentimen Terhadap Pengaruh Program Promosi Event Belanja pada Marketplace. JEPIN (Jurnal Edukasi dan Penelitian Informatika), 8(3), 508-516. https://doi.org/10.26418/jp.v8i3.56478.
Dzukaidah, M. F., & Prasvita, D. S. (2022, August). ANALISIS SENTIMEN PROGRAM BANTUAN SOSIAL TUNAI PADA SOSIAL MEDIA TWITTER MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE. In Prosiding Seminar Nasional Mahasiswa Bidang Ilmu Komputer dan Aplikasinya (Vol. 3, No. 2, pp. 569-580).
Fahlevvi, M. R. (2022). Analisis Sentimen Terhadap Ulasan Aplikasi Pejabat Pengelola Informasi dan Dokumentasi Kementerian Dalam Negeri Republik Indonesia di Google Playstore Menggunakan Metode Support Vector Machine. Jurnal Teknologi dan Komunikasi Pemerintahan, 4(1), 1-13. https://doi.org/10.33701/jtkp.v4i1.2701.
Gifari, O. I., Adha, M., Hendrawan, I. R., & Durrand, F. F. S. (2022). Analisis Sentimen Review Film Menggunakan TF-IDF dan Support Vector Machine. Journal of Information Technology, 2(1), 36-40. https://doi.org/10.46229/jifotech.v2i1.330.
Karimah, N. (2024). Multi-Aspect Sentiment Analysis Pada Review Film Menggunakan Metode Bidirectional Encoder Representations From Transformers (BERT). Komputika: Jurnal Sistem Komputer, 13(1), 63-72. https://doi.org/10.34010/komputika.v13i1.11098.
Khan, M., & Srivastava, A. (2024). Sentiment analysis of Twitter data using machine learning techniques. International Journal of Engineering and Management Research, 14(1), 196-203. https://doi.org/10.5281/zenodo.10791485.
Kristiawan, K., & Widjaja, A. (2021). Perbandingan algoritma machine learning dalam menilai sebuah lokasi toko ritel. Jurnal Teknik Informatika dan Sistem Informasi, 7(1). https://doi.org/10.28932/jutisi.v7i1.3182.
Liu, B. (2022). Sentiment analysis and opinion mining. Springer Nature.
Muttaqien, D. D., Tibyani, T., & Hartono, P. P. (2022). Implementasi Support Vector Machine pada Analisis Sentimen mengenai Bantuan Sosial di Era Pandemi Covid-19 pada Pengguna Twitter. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 6(1), 163-171.
Prabowo, W. A., & Azizah, F. (2020). Sentiment analysis for detecting cyberbullying using TF-IDF and SVM. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 4(6), 1142–1148. https://doi.org/10.29207/resti.v4i6.2753.
Pranata, R. A., Rudiman, R., & Verdikha, N. A. (2024). Metode pembobotan TF-IDF untuk klasifikasi teks quick count pemilihan wakil presiden Indonesia 2024 pada X Twitter dengan metode SVM. Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika, 18(2), 127–138. https://doi.org/10.54914/jit.v10i2.1279.
Raehanun, M. (2021). Analisis Support Vector Machine (SVM) Dalam Prediksi Permintaan Emas Perhiasan (Studi Kasus: Permintaan Emas Perhiasan dari Beberapa Negara Tertentu Periode Tahun 2000-2021).
Septiani, D., & Isabela, I. (2022). Analisis term frequency inverse document frequency (tf-idf) dalam temu kembali informasi pada dokumen teks. Sistem dan Teknologi Informasi Indonesia (SINTESIA), 1(2), 81-88.