Penerapan Metode Support Vector Machine Dalam Menganalisis Sentimen Pengguna Aplikasi Sirekap 2024 Di Google Playstore
DOI:
https://doi.org/10.47747/jpsii.v6i1.2565Keywords:
Analysis Sentiment, Support Vector Machine, SirekapAbstract
Sirekap is a mobile application that was built to help the public monitor and oversee the development of the 2024 elections held in Indonesia. The research aims to apply the Support Vector Machine algorithm in analyzing sentiment about the use of the Sirekap application in 2024. The Support Vector Machine method is used to classify user sentiment into classes, namely positive, negative, and very negative. The amount of data used is 15,000 data sourced from Sirekap application reviews on Google PlayStore, with more detailed research stages including data collection, data preprocessing, data labeling, visualization, word weighting, and testing and analysis. The results show that the Support Vector Machine algorithm provides an accuracy of 88% for the Sirekap 2024 application. These results are expected to help developers to develop further the Sirekap 2024 application in improving the quality of the application and providing better user comfort Based on the results of the sentiment analysis of Sirekap 2024 application users on the Google Play store using the Support Vector Machine (SVM) method, an accuracy rate of 88% was obtained in classifying the sentiment of reviews into positive, negative, and very negative. This shows that the Support Vector Machine method is quite accurate for sentiment analysis of Indonesian text data. Overall, most reviews are very negative with a percentage reaching 76.9%, followed by negative reviews at 12.6%, and the least are positive reviews at 11%.
References
Yusrin, Y. and Salpina, S. (2023) ‘Partisipasi Generasi Millenial dalam
Mengawasi Tahapan Pemilu 2024’, Journal on Education, 5(3), pp. 9646– 9653. Available at: https://doi.org/10.31004/joe.v5i3.1842.
Marzellina, H. et al. (2022) ‘Urgensi Sistem E-Voting dan Sirekap Dalam Penyelenggaraan Pemilu 2024’, 7(2).
VF. Anindya, A.W. (2023) ‘Analisis Sentimen Ulasan Aplikasi Capcut Menggunakan Metode Naive Bayes’, Statistika, 23(2), pp. 164–175. Available at: https://doi.org/10.29313/statistika.v23i2.2353.
Styawati, S., Hendrastuty, N. and Isnain, A.R. (2021) ‘Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine’, Jurnal Informatika: Jurnal Pengembangan IT, 6(3), pp. 150–155. Available at: https://doi.org/10.30591/jpit.v6i3.2870.
Hasibuan, S.S. and Saputra, E. (2024) ‘Sentimen Analisis Terhadap Fitur Tiktok Shop Menggunakan Naïve Bayes dan K-Nearest Neighbor’, 8, pp. 303–311. Available at: https://doi.org/10.30865/mib.v8i1.7238.
Mufidah, U. and Siahaan, M. (2021) ‘Perancangan Aplikasi Perbandingan Harga Produk (Historical Data) Menggunakan Teknik Web Scraping’, Pusdansi.org, 1(1), pp. 1–14.
Rahman, A. (2019) ‘Statistics-based data preprocessing methods and machine learning algorithms for big data analysis’, International Journal of Artificial Intelligence, 17(2), pp. 44–65.
Wang, Q. et al. (2022) ‘A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization’, IEEE Transactions on Visualization and Computer Graphics, 28(12), pp. 5134–5153. Available at:
Sintia, Defit, S. and Nurcahyo, G.W. (2021) ‘Product Codefication Accuracy With Cosine Similarity and Weighted Term Frequency and Inverse Document Frequency (Tf-Idf)’, Journal of Applied Engineering and Technological Science, 2(2), pp. 14–21. Available at: https://doi.org/10.37385/jaets.v2i2.210.
Kosasih, R. (2021) ‘Penggunaan Metode Linear Discriminant Analysis Untuk Pengenalan Wajah Dengan Membandingkan Banyaknya Data Latih’, Jurnal Ilmiah Teknologi dan Rekayasa, 26(1), pp. 25–34. Available at: https://doi.org/10.35760/tr.2021.v26i1.3520.
Tineges, R., Triayudi, A. and Sholihati, I.D. (2020) ‘Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)’, Jurnal Media Informatika Budidarma, 4(3), p. 650. Available at: https://doi.org/10.30865/mib.v4i3.2181.
C. G. Indrayanto, D. E. Ratnawati, dan B. Rahayudi, “Analisis Sentimen Data Ulasan Pengguna Aplikasi MyPertamina di Indonesia pada Google Play Store menggunakan Metode Random Forest,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 3, hal. 1131–1139, 2023.
E. Indrayuni, “Komparasi Algoritma Naive Bayes Dan Support Vector Machine Untuk Analisa Sentimen Review Film,” J. Pilar Nusa Mandiri, vol. 14, no. 2, hal. 175, 2018, doi: 10.33480/pilar.v14i2.918.
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