Perbandingan Algoritma Klasifikasi Sentimen pada Ulasan Aplikasi Mobile JKN
DOI:
https://doi.org/10.35960/ikomti.v6i3.1891Keywords:
sentiment analysis, mobile JKN, naive bayes, svm classification, knn, google play storeAbstract
Sentiment classification plays an important role in evaluating public response to digital services such as BPJS Kesehatan's Mobile JKN application. This study aims to compare the performance of three machine learning algorithms-Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbor (KNN) for classifying user sentiment based on reviews in the Google Play Store. A total of 10,000 user reviews were collected using Python and processed on Google Colab. The research process includes text pre-processing, sentiment labeling based on ratings, data splitting, and model training. Evaluation was conducted using accuracy, precision, recall, F1 score, and confusion matrix metrics. The results show that the SVM algorithm provides the best accuracy of 90.9%, followed by Naive Bayes (90.3%) and KNN (86%). These findings prove that SVM is the most effective model for sentiment classification in the context of public services and provide important insights for government policy evaluation and digital service improvement.
References
[1] M. G. K. Lita, A. D. Mardhiyyah, I. G. A. N. S. Maharani, A. P. Mulia, and F. I. Maulana, “Sentiment Analysis of Tokopedia Product Reviews Using Naïve Bayes Algorithm,” Commun. Comput. Inf. Sci., vol. 2185 CCIS, no. 3, pp. 179–189, 2024.
[2] B. S. Belay, “EFEKTIVITAS PENGGUNAAN APLIKASI MOBILE JKN DALAM MENGURANGI ANTRIAN,” Syntax Lit. J. Ilm. Indones., vol. 5, no. 8.5.2017, pp. 2003–2005, 2022.
[3] P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, p. 147, 2021.
[4] Normah, B. Rifai, S. Vambudi, and R. Maulana, “Analisa Sentimen Perkembangan Vtuber Dengan Metode Support Vector Machine Berbasis SMOTE,” J. Tek. Komput. AMIK BSI, vol. 8, no. 2, pp. 174–180, 2022.
[5] K. Anwar, “Analisa sentimen Pengguna Instagram Di Indonesia Pada Review Smartphone Menggunakan Naive Bayes,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 2, no. 4, pp. 148–155, 2022.
[6] F. A. Larasati, D. E. Ratnawati, and B. T. Hanggara, “Analisis Sentimen Ulasan Aplikasi Dana dengan Metode Random Forest,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 9, pp. 4305–4313, 2022.
[7] I. H. Kusuma and N. Cahyono, “Analisis Sentimen Masyarakat Terhadap Penggunaan E-Commerce Menggunakan Algoritma K-Nearest Neighbor,” J. Inform. J. Pengemb. IT, vol. 8, no. 3, pp. 302–307, 2023.
[8] B. Ramadhani and R. R. Suryono, “Komparasi Algoritma Naïve Bayes dan Logistic Regression Untuk Analisis Sentimen Metaverse,” J. Media Inform. Budidarma, vol. 8, no. 2, p. 714, 2024.
[9] I. Cholissodin, Sutrisno, A. A. Soebroto, U. Hasanah, and Y. I. Febiola, “AI, Machine Learning & Deep Learning (Teori & Implementasi) ‘from Basic Science to High Scientific Solution for Any Problem’ Versi 1.01,” p. 317, 2020.
[10] I. Cholissodin and A. A. Soebroto, “AI , MACHINE LEARNING & DEEP LEARNING ( Teori & Implementasi ),” no. July 2019, 2021.
[11] U. Muhammadiyah, M. Aceh, and U. B. Nusantara, “Penggunaan Algoritma Support Vector Machine ( SVM ) Untuk Deteksi Penipuan pada Transaksi Online,” vol. 13, pp. 1627–1632, 2024.
[12] A. Mulyoto, NAÏVE BAYES PADA GOOGLE COLABS, 1st ed. EUREKA MEDIA AKSARA, 2024.
[13] E. V Rahcmadani, S. F. Pane, and N. H. Harani, Algoritma C4.5 dan K-Nearest Neighbors (KNN) untuk Memetakan Matakuliah dan Keterlambatan Kelulusan Mahasiswa. in Ebook. Kreatif.
[14] A. Surahman, A. F. Octaviansyah, and D. Darwis, “Ekstraksi Data Produk E-Marketplace Sebagai Strategi Pengolahan Segmentasi Pasar Menggunakan Web Crawler,” Sistemasi, vol. 9, no. 1, p. 73, 2020.
[15] C. Carudin et al., Buku Ajar Data Mining. PT. Sonpedia Publishing Indonesia, 2024.
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Copyright (c) 2025 Ramadhoni Gibran Maulana, Muhammad Shidiq Budiman, Tegar Hardiansyah Prasetyo, Muhammad Fadhlan Karimuddin, Rizqy Adriansyah, Yayan Hendrian, Shynde Limar Kinanti (Author)
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