Sentiment Analysis of Sirekap Application Users Using the Support Vector Machine Algorithm
Abstract
In the current era of digitalization, various activities are conducted using technology to aid their execution, including the democratic process scheduled for February 2024. The Komisi Pemilihan Umum (KPU) is utilizing a mobile-based application called Sirekap. During the previous presidential and vice-presidential elections, there were many pros and cons regarding the Sirekap application. A significant number of negative reviews were expressed by the public towards this application. This study employs the SVM algorithm to perform sentiment analysis of Sirekap application users. Before building the model, several steps were undertaken, including data labeling, data preprocessing, splitting the dataset into training and testing data, and performing transformations using Count Vectorizer. Evaluation of the SVM model results shows quite good performance with an accuracy of 81%. For the negative class, the precision and recall values are 87% and 85%, respectively, while for the positive class, the precision and recall values only reach 66% and 70%, indicating a need for improvement in the model's identification of the positive class. Five-fold cross-validation was performed with an average cross-validation score of 79.6% and a standard deviation of 2.14%, indicating the model's consistency across various training data subsets. These findings suggest that the SVM model can effectively perform text classification tasks. Based on the negative word cloud, it can be concluded that the Sirekap application still has many shortcomings affecting the democratic process in February 2024.
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References
H. KPU, “KPU-Bawaslu Rapat Persiapan Implementasi Sirekap,” Komisi Pemilihan Umum. Accessed: Jun. 06, 2024. [Online]. Available: https://www.kpu.go.id/berita/baca/8224/Guna-meningkatkan-transparansi-serta-akuntabilitas-pelakasanaan-pemilihan-maupun-pemilihan-umum--Komisi-Pemilihan-Umum--KPU--RI-bersama-Badan-Pengawas-Pemilu--Bawaslu-
D. Handita and D. L. Anggraini, “Penerapan Pemilu Online Berbasis Aplikasi Smartphone di Era Pandemi Covid-19,” Pros. Semin. Nas. Desain Sos., pp. 848–851, 2021.
H. KPU, “Semua TPS Gunakan Sirekap Sebagai Alat Bantu dan Publikasi,” Komisi Pemilihan Umum. Accessed: Jun. 06, 2024. [Online]. Available: https://www.kpu.go.id/berita/baca/9133/Sistem-Informasi-Rekapitulasi--Sirekap--tetap-akan-dipergunakan-Komisi-Pemilihan-Umum--KPU--pada-proses-rekapitulasi-hasil-perolehan-suara-di-Tempat-Pemungutan-Suara--TPS-
D. D. Purnamasari, “Sirekap, Alat Bantu Pemilu yang Justru Timbulkan Kegaduhan,” kompas.id. Accessed: Jun. 06, 2024. [Online]. Available: https://www.kompas.id/baca/polhuk/2024/02/17/sirekap-alat-bantu-pemilu-yang-justru-timbulkan-kegaduhan
M. F. Y. Herjanto and C. Carudin, “Analisis Sentimen Ulasan Pengguna Aplikasi Sirekap Pada Play Store Menggunakan Algoritma Random Forest Classifer,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 2, pp. 1204–1210, 2024, doi: 10.23960/jitet.v12i2.4192.
K. Solecha and O. Irnawati, “Komparasi Algoritma Support Vector Machine Dan Naïve Bayes Berbasis Particle Swarm Optimization Pada Analisis Sentimen Ulasan Aplikasi Flip,” JIEET (Journal Inf. Eng. Educ. Technol., vol. 07, no. 1, pp. 10–15, 2023.
M. N. Muttaqin and I. Kharisudin, “Analisis Sentimen Pada Ulasan Aplikasi Gojek Menggunakan Metode Support Vector Machine dan K Nearest Neighbor,” UNNES J. Math., vol. 10, no. 2, pp. 22–27, 2021, [Online]. Available: http://journal.unnes.ac.id/sju/index.php/ujm
A. Salma and W. Silfianti, “Sentiment Analysis of User Review on COVID-19 Information Applications Using Naïve Bayes Classifier, Support Vector Machine, and K-Nearest Neighbors,” Int. Res. J. Adv. Eng. Sci., vol. 6, no. 4, pp. 158–162, 2021.
K. P. Umum, “SIREKAP 2024,” Google Play Store. [Online]. Available: https://play.google.com/store/apps/details?id=id.go.kpu.sirekap2024&hl=id
T. Safitri, Y. Umaidah, and I. Maulana, “Analisis Sentimen Pengguna Twitter Terhadap Grup Musik BTS Menggunakan Algoritma Support Vector Machine,” J. Appl. Informatics Comput., vol. 7, no. 1, pp. 28–35, 2023, doi: 10.30871/jaic.v7i1.5039.
H. P. Doloksaribu and Yusran Timur Samuel, “Komparasi Algoritma Data Mining Untuk Analisis Sentimen Aplikasi Pedulilindungi,” J. Teknol. Inf. J. Keilmuan dan Apl. Bid. Tek. Inform., vol. 16, no. 1, pp. 1–11, 2022, doi: 10.47111/jti.v16i1.3747.
R. Maulana, A. Voutama, and T. Ridwan, “Analisis Sentimen Ulasan Aplikasi MyPertamina pada Google Play Store menggunakan Algoritma NBC,” J. Teknol. Terpadu, vol. 9, no. 1, pp. 42–48, 2023, doi: 10.54914/jtt.v9i1.609.
JoMingyu, “google-play-scraper.” [Online]. Available: https://pypi.org/project/google-play-scraper/
R. Merdiansah, S. Siska, and A. Ali Ridha, “Analisis Sentimen Pengguna X Indonesia Terkait Kendaraan Listrik Menggunakan IndoBERT,” J. Ilmu Komput. dan Sist. Inf., vol. 7, no. 1, pp. 221–228, 2024, doi: 10.55338/jikomsi.v7i1.2895.
A. Hermawan, I. Jowensen, J. Junaedi, and Edy, “Implementasi Text-Mining untuk Analisis Sentimen pada Twitter dengan Algoritma Support Vector Machine,” JST (Jurnal Sains dan Teknol., vol. 12, no. 1, pp. 129–137, 2023, doi: 10.23887/jstundiksha.v12i1.52358.
G. R. Ditami, E. F. Ripanti, and H. Sujaini, “Implementasi Support Vector Machine untuk Analisis Sentimen Terhadap Pengaruh Program Promosi Event Belanja pada Marketplace,” J. Edukasi dan Penelit. Inform., vol. 8, no. 3, p. 508, 2022, doi: 10.26418/jp.v8i3.56478.
M. N. B. Balit and F. S. Utomo, “Sentiment Analysis of pegipegi.com Review on Google Play Store with Naïve Bayes,” Sistemasi, vol. 13, no. 3, pp. 1044–1053, 2024, doi: 10.32520/stmsi.v13i2.3887.
D. Safryda Putri and T. Ridwan, “Analisis Sentimen Ulasan Aplikasi Pospay Dengan Algoritma Support Vector Machine,” J. Ilm. Inform., vol. 11, no. 01, pp. 32–40, 2023, doi: 10.33884/jif.v11i01.6611.
B. Gaye, D. Zhang, and A. Wulamu, “Improvement of Support Vector Machine Algorithm in Big Data Background,” Math. Probl. Eng., vol. 2021, 2021, doi: 10.1155/2021/5594899.
R. Tineges, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM),” J. Media Inform. Budidarma, vol. 4, no. 3, p. 650, 2020, doi: 10.30865/mib.v4i3.2181.
T. Turki and S. S. Roy, “Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer,” Appl. Sci., vol. 12, no. 13, 2022, doi: 10.3390/app12136611.
M. A. Muslim et al., “Support Vector Machine (SVM) Optimization Using Grid Search and Unigram to Improve E-Commerce Review Accuracy,” J. Soft Comput. Explor., vol. 1, no. 1, pp. 8–15, 2020, doi: 10.52465/joscex.v1i1.3.
B. F. S. Supriyanto and S. Rosalin, “Analisis Sentimen Program Merdeka Belajar dengan Text Analysis Wordcloud & Word Frequency,” J. Minfo Polgan, vol. 12, no. 1, pp. 25–32, 2023, doi: 10.33395/jmp.v12i1.12312.
N. Cahyono and Dewi Setiyawati, “Analisis Sentimen Pengguna Sosial Media Twitter Terhadap Perokok Di Indonesia,” Indones. J. Comput. Sci., vol. 12, no. 1, pp. 262–272, 2023, doi: 10.33022/ijcs.v12i1.3154.
D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 697–711, 2021.
D. Krstinić, M. Braović, L. Šerić, and D. Božić-Štulić, “Multi-label Classifier Performance Evaluation with Confusion Matrix,” Comput. Sci. Inf. Technol. (CS IT), pp. 01–14, 2020, doi: 10.5121/csit.2020.100801.
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