Analisis Sentimen Komentar Aplikasi Digital Korlantas Menggunakan Metode SPOK dan Algoritma SVM
Keywords:
Analisis Sentimen, Digital Korlantas, SPOK, Support Vector MachineAbstract
Aplikasi Digital Korlantas merupakan platform layanan publik berbasis digital yang dikembangkan oleh Kepolisian Republik Indonesia untuk mendukung transformasi pelayanan berbasis teknologi. Evaluasi terhadap kualitas layanan dan respons pengguna menjadi penting untuk menjamin efektivitas platform tersebut. Penelitian ini memiliki tujuan untuk mengklasifikasikan sentimen pengguna terhadap Aplikasi Digital Korlantas berdasarkan komentar yang diunggah di Google Play Store. Analisis dilakukan terhadap 19.323 komentar pengguna yang dikumpulkan dalam rentang waktu 1 Januari 2024 hingga 8 Mei 2025. Pendekatan yang digunakan menggabungkan metode analisis linguistik berbasis struktur kalimat Subjek, Predikat, Objek, dan Keterangan (SPOK) dengan algoritma pembelajaran mesin Support Vector Machine (SVM). Proses ekstraksi fitur menggunakan metode SPOK dilakukan untuk mengidentifikasi unsur kalimat utama, yang kemudian digunakan sebagai input dalam klasifikasi sentimen. Temuan ini dapat dijadikan acuan untuk peningkatan kualitas layanan dan pengembangan fitur aplikasi Digital Korlantas yang lebih responsif terhadap kebutuhan masyarakat.References
S. Herdianingsih and O. D. Cahya, “User Experience Pengguna Perpanjangan Sim Online Aplikasi Digital Korlantas Menggunakan UEQ,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 8, no. 1, pp. 38–48, 2023, doi: 10.31294/ijcit.v8i1.13247.
E. Budianita, E. P. Cynthia, A. Pranata, and D. Abimanyu, “Pendekatan berbasis Machine Learning dan Leksikal Pada Analisis Sentimen,” Semin. Nas. Teknol. Informasi, Komun. dan Ind., vol. 26, p. 2022, 2022.
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, doi: 10.25126/jtiik.0813944.
A. Mudya Yolanda and R. Tri Mulya, “Implementasi Metode Support Vector Machine untuk Analisis Sentimen pada Ulasan Aplikasi Sayurbox di Google Play Store,” VARIANSI J. Stat. Its Appl. Teach. Res., vol. 6, no. 2, pp. 76–83, 2024, doi: 10.35580/variansiunm258.
M. M. Amin and Y. Dwitayanti, “Komparasi Kinerja Algoritma Blocking Pada Proses Indexing Untuk Deteksi Duplikasi,” J. Teknol. Inf. dan Ilmu Komput., vol. 11, no. 4, pp. 715–722, 2024, doi: 10.25126/jtiik.1148080.
M. B. M. Amin et al., “Deteksi Spam Berbahasa Indonesia Berbasis Teks Menggunakan Model Bert,” J. Teknol. Inf. dan Ilmu Komput., vol. 11, no. 6, pp. 1291–1302, 2024, doi: 10.25126/jtiik.2024118121.
| Jurnal and C. Proklamator, “Analysis of Students Ability To Determine Subject, Predicate, Object, Description (Spok) on Indonesian Language Subject Story Material Fairy Tales in Class 4 of Babadsari State Primary School Analisis Kemampuan Siswa Dalam Menentukan Subjek, Predikat, Obj,” Ghaniyu Faik Aqshal, vol. 11, no. 2, pp. 137–147, 2023.
R. A. Rahman, V. H. Pranatawijaya, and N. N. K. Sari, “Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi Gojek,” KONSTELASI Konvergensi Teknol. dan Sist. Inf., vol. 4, no. 1, pp. 70–82, 2024, doi: 10.24002/konstelasi.v4i1.8922.
W. P. Ali and Y. Sibaroni, “Analisis Sentimen Masyarakat terhadap Kinerja Presiden Indonesia dalam Aspek Ekonomi, Kesehatan, dan Pembangunan Berdasarkan Opini dari Twitter,” e-Proceeding Eng., vol. 6, no. 2, pp. 8637–8649, 2019.
M. Sulistiyono, Y. Pristyanto, S. Adi, and G. Gumelar, “Implementasi Algoritma Synthetic Minority Over-Sampling Technique untuk Menangani Ketidakseimbangan Kelas pada Dataset Klasifikasi,” Sistemasi, vol. 10, no. 2, p. 445, 2021, doi: 10.32520/stmsi.v10i2.1303.
A. Firizkiansah, A. Muhammad, and I. R. Maulana, “Optimasi Klasifikasi Data Teks Menggunakan Algoritma Logistic Regression dengan TF-IDF dan SMOTE,” JIKOMTI J. Ilm. Ilmu Komput. dan Teknol. Inf., vol. 2, no. 1, pp. 29–36, 2025, [Online]. Available: https://ojs.sains.ac.id/index.php/Jikomti/article/view/97/119
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