Analisis Sentimen Pengguna Media Sosial Terhadap Identitas Kependudukan Digital Menggunakan Metode Support Vector Machine (SVM)
- Vina Alipah Sulistiani Universitas Muhammadiyah Purwokerto, Banyumas, Indonesia
-
Muhammad Hamka
*
Universitas Muhammadiyah Purwokerto, Banyumas,
Indonesia
http://orcid.org/0000-0002-0984-3608
(*) Corresponding Author
Abstract
The Government of the Republic of Indonesia has implemented a policy of using Digital Population Identity (IKD) as one of the priority services of the Electronic Based Government System (SPBE) to accelerate digital transformation in the context of integrase national digital service. The implementation of IKD raises pros and cons perspectives in society. The counter perspective that is a problem in using IKD is that people are still worried about the security and privacy of personal data listed on digital systems. People can easily voice opinions, aspirations and participation through social media. However, given the large amount of social media data available, the process of obtaining the right information requires a lot of time and special skills. So a sentiment analysis of IKD on social media was carried out using the method Support Vector Machine (SVM) and improved with techniques Synthetic Minority Oversampling Technique (SMOTE) which aims to find out views or opinions regarding IKD which are positive, negative and neutral based on other people's points of view. The opinion data used in this research was 6697 with the keywords "digital population identity", "electronic ID card", and "digital ID card" taken from 2021 to 2024. Then, the data was classified based on polarity values using a dictionary lexicon that is senticnet7 with sentiment results positive by 57.1% and sentiment negative amounting to 42.9%. The results of the classification process using the SVM method and enhanced with the SMOTE technique have results accuracy of 89%, negative precision of 88%, positive precision of 90%, recall negative by 85%, recall positive by 92%, f1-score negative by 86%, and f1-score positive by 91%.
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References
Peraturan Menteri Dalam Negeri, “Standar Dan Spesifikasi Perangkat Keras, Perangkat Lunak, Dan Blangko Kartu Tanda Penduduk Elektronik Serta Penyelenggaraan Identitas Kependudukan Digital” 2022
I. B. Permadi and A. Rokhman, “Implementasi Identitas Kependudukan Digital Dalam Upaya Pengamanan Data pribadi,” JOPPAS: Journal of Public Policy and Administration Silampari, vol. 4, no. 2, pp. 80–88, Jun. 2023, doi: 10.31539/joppas.v4i2.6199.
B. Putri and O. Reviandani, “Penerapan E-Government Melalui Pelaksanaan Program Ktp Digital Di Kelurahan Dr. Soetomo Kota Surabaya,” 2023. [Online]. Available: http://journal.uta45jakarta.ac.id/index.php/admpublik/iex
C. Mole, E. Chalstrey, P. Foster, and T. Hobson, “Digital identity architectures: comparing goals and vulnerabilities,” Feb. 2023, [Online]. Available: http://arxiv.org/abs/2302.09988
M. Alfarizi, “Digitalisasi Kartu Tanda Penduduk dan Partisipasi Milenial-Gen Z: Investigasi Penerimaan Transformasi Digital dalam Kebijakan Kependudukan Indonesia,” Jurnal Studi Kebijakan Publik, vol. 2, no. 1, pp. 41–54, May 2023, doi: 10.21787/jskp.2.2023.41-54.
M. Riyanto and V. Kovalenko, “Partisipasi Masyarakat Menuju Negara Kesejahteraan: Memahami Pentingnya Peran Aktif Masyarakat Dalam Mewujudkan Kesejahteraan Bersama,” 2023.
D. Fitriani et al., “Partisipasi Masyarakat Dalam Proses Demokrasi Di Indonesia: Analisis Peran Teknologi Dan Media Sosial,” 2023.
D. R. Sari, B. Matsaany, and M. Hamka, “Aspect Extraction Of E-Commerce And Marketplace Applications Using Word2vec And Wordnet Path,” Jurnal Teknik Informatika (Jutif), vol. 4, no. 4, pp. 787–796, Aug. 2023, doi: 10.52436/1.jutif.2023.4.4.726.
F. Parsakh Nursyamsyi and F. Noor Hasan, “KLIK: Kajian Ilmiah Informatika dan Komputer Klasifikasi Sentimen Terhadap Aplikasi Identitas Kependudukan Digital Menggunakan Algoritma Naïve Bayes dan SVM,” Media Online, vol. 4, no. 3, pp. 1788–1798, 2023, doi: 10.30865/klik.v4i3.1517.
R. A. Lestari, A. Erfina, W. Jatmiko, and P. Korespondensi, “Penerapan Algoritma Support Vector Machine Pada Analisis Sentimen Terhadap Identitas Kependudukan Digital,” vol. 10, no. 5, pp. 1063–1070, 2023, doi: 10.25126/jtiik.2023107264.
R. Hidayat, R. Nur Rahman, M. Reifin Perdana, P. Teknik Informatika, F. Sains dan Teknologi, and U. Muhammadiyah Kalimantan Timur, “Analisis Sentimen Aplikasi Identitas Kependudukan Digital (IKD) Menggunakan Metode Naïve Bayes,” Jurnal Sistem Informasi dan Ilmu Komputer, vol. 2, no. 1, pp. 129–140, 2024, doi: 10.59581/jusiik-widyakarya.v2i1.2320.
V. Fitriyana et al., “Analisis Sentimen Ulasan Aplikasi Jamsostek Mobile Menggunakan Metode Support Vector Machine,” 2023.
N. Fitriyah, B. Warsito, D. Asih, and I. Maruddani, “Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm),” Jurnal Gaussian, vol. 9, no. 3, pp. 376–390, 2020, [Online]. Available: https://ejournal3.undip.ac.id/index.php/gaussian/
B. Ramadhani, R. R. Suryono, and K. Kunci, “Jurnal Media Informatika Budidarma Komparasi Algoritma Naïve Bayes dan Logistic Regression Untuk Analisis Sentimen Metaverse,” 2024, doi: 10.30865/mib.v8i2.7458.
S. Aftab, S. Shah Muhammad, S. Awan, M. Ahmad, and S. Ahmad, “Machine Learning Techniques for Sentiment Analysis: A Review,” International Journal Of Multidisciplinary Sciences And Engineering, vol. 8, no. 3, 2017, [Online]. Available: www.ijmse.org
R. Pappagari, P. Zelasko, J. Villalba, Y. Carmiel, and N. Dehak, “Hierarchical Transformers for Long Document Classification,” in 2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., Dec. 2019, pp. 838–844. doi: 10.1109/ASRU46091.2019.9003958.
M. Ahmad, S. Aftab, M. S. Bashir, N. Hameed, I. Ali, and Z. Nawaz, “SVM optimization for sentiment analysis,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 4, pp. 393–398, 2018, doi: 10.14569/IJACSA.2018.090455.
E. Erlin, Y. Desnelita, N. Nasution, L. Suryati, and F. Zoromi, “Dampak SMOTE terhadap Kinerja Random Forest Classifier berdasarkan Data Tidak seimbang,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 3, pp. 677–690, Jul. 2022, doi: 10.30812/matrik.v21i3.1726.
A. Surya Firmansyah, A. Aziz, and M. Ahsan, “Optimasi K-Nearest Neighbor Menggunakan Algoritma Smote Untuk Mengatasi Imbalance Class Pada Klasifikasi Analisis Sentimen,” 2023.
Y. Xu and R. Xu, “Research on Interpolation and Data Fitting: Basis and Applications,” Aug. 2022, [Online]. Available: http://arxiv.org/abs/2208.11825
D. Aby Vonega, A. Fadila, and D. Ely Kurniawan, “Analisis Sentimen Twitter Terhadap Opini Publik Atas Isu Pencalonan Puan Maharani Dalam Pilpres 2024,” 2022. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
E. Febriyani and H. Februariyanti, “Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Algoritma Naive Bayes Classifier Di Twitter,” vol. 17, no. 1, 2023
“Titian: Jurnal Ilmu Humaniora,” 2023, [Online]. Available: https://online-journal.unja.ac.id/index.php/titian
D. Haryani, “Kualitas Pelayanan Kartu Tanda Penduduk Elektronik (Ktp-El) Di Kecamatan Banyuasin Kabupaten Banyuasin,” 2024.
M. I. Amal, E. S. Rahmasita, E. Suryaputra, and N. A. Rakhmawati, “Analisis Klasifikasi Sentimen Terhadap Isu Kebocoran Data Kartu Identitas Ponsel di Twitter,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 8, no. 3, Dec. 2022, doi: 10.28932/jutisi.v8i3.5483.
W. Ayunda Sari, D. Arifianto, and A. Nilogiri, “Analisis Sentimen Terhadap Ulasan Netizen Pada Aplikasi CamScanner Menggunakan Metode Support Vector Machine (SVM).” 2023
R. Tineges, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM),” Jurnal Media Informatika Budidarma, Vol. 4, No. 3, P. 650, Jul. 2020, Doi: 10.30865/mib.v4i3.2181.
S. A. Ashari, M. W. A. Saputra, E. Larosa, and B. S. Rijal, “Analisis Sentimen pada Aplikasi Translate Google Menggunakan Metode SVM (Studi Kasus: Komentar Pada Playstore),” Jurnal Teknik, vol. 21, no. 2, pp. 168–182, Dec. 2023, doi: 10.37031/jt.v21i2.412.
F. Zuhad and N. Wilantika, “Perbandingan Penggunaan Kamus Normalisasi dalam Analisis Sentimen Berbahasa Indonesia.” 2022
S. Fachri and P. J. Ramdan, “Pemodelan Machine Learning : Analisis Sentimen Masyarakat Terhadap Kebijakan PPKM Menggunakan Data Twitter,” 2022. [Online]. Available: https://t.co/IEnucGFuuJ
D. R. Sari, B. Matsaany, and M. Hamka, “Aspect Extraction Of E-Commerce And Marketplace Applications Using Word2vec And Wordnet Path,” Jurnal Teknik Informatika (Jutif), vol. 4, no. 4, pp. 787–796, Aug. 2023, doi: 10.52436/1.jutif.2023.4.4.726.
M. Hamka and D. Ratna Sari, “Analisis Sentimen Dan Information Extraction Pembelajaran Daring Menggunakan Pendekatan Lexicon,” 2022.
A. Firdaus, “Analisis Sentimen Pada Aplikasi Alfagift.” UIN Jakarta, 2023
R. Parlika, S. Ilham Pradika, A. M. Hakim, and R. N. M. Kholilul, “Analisis Sentimen Twitter Terhadap Bitcoin dan Cryptocurrency Berbasis Python TextBlob,” 2020. [Online]. Available: https://t.co/QaUW3P2TKc
S. Abdullah, “Penulis Pertama: Visualisasi Data Analisa Sentimen … 261 Visualisasi Data Analisa Sentimen RUU Omnibus law Kesehatan Menggunakan KNN dengan Software RapidMiner,” vol. 8, no. 3, 2023.
C. Cahyaningtyas, Y. Nataliani, and I. R. Widiasari, “Analisis sentimen pada rating aplikasi Shopee menggunakan metode Decision Tree berbasis SMOTE,” AITI: Jurnal Teknologi Informasi, vol. 18, no. Agustus, pp. 173–184, 2021.
R. Turaina, R. Saputra, S. Informasi, U. Metamedia, and J. Khatib Sulaiman Dalam, “Optimalisasi Klasifikasi Umpan Balik Mahasiswa Terhadap Layanan Kampus dengan Sinergi Random Forest dan Smote,” Jurnal Nasional Komputasi dan Teknologi Informasi, vol. 6, no. 6, 2023.
D. Hana Amalia and W. Yustanti, “Klasifikasi Buku Menggunakan Metode Support Vector Machine pada Digital Library,” Journal of Informatics and Computer Science, vol. 03, 2021
J. Teknika, M. K. Rifa, M. H. Totohendarto, and M. R. Muttaqin, “Analisis Sentimen Penguna E-Wallet Dana Dan Gopay Pada Twitter Menggunakan Metode Support Vector Machine (SVM),” IJCCS, 2023
F. Setya Ananto and F. N. Hasan, “Implementasi Algoritma Naïve Bayes Terhadap Analisis Sentimen Ulasan Aplikasi MyPertamina pada Google Play Store,” Jurnal ICT : Information Communication & Technology, vol. 23, no. 1, pp. 75–80, 2023, [Online]. Available: https://ejournal.ikmi.ac.id/index.php/jict-ikmi
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