Particle Swarm Optimization dan Genetic Algorithm untuk analisis sentimen pemekaran Papua di Twitter berbasis Support Vector Machine

Desi Purnamasari; Muhammad Adi Khairul Anshary; Rianto Rianto

doi:10.24246/aiti.v20i2.177-190

Authors

Desi Purnamasari Universitas Siliwangi
Muhammad Adi Khairul Anshary
Rianto Rianto

DOI:

https://doi.org/10.24246/aiti.v20i2.177-190

Keywords:

division of Papua, Genetic Algorithm, Particle Swarm Optimization, Twitter, Sentiment Analysis, Support Vector Machine

Abstract

Support Vector Machine (SVM) can classify sentiment analysis into positive or negative sentiment. In this study, sentiment data on the division of Papua was taken from Twitter. Because SVM has a weakness in feature selection during classification, the SVM algorithm optimization feature is implemented using feature selection, with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Tweets taken were 839 data tweets, then divided into 640 data for the training process and 199 data for the testing process. The data processing process is divided into two stages: data training and data testing. Tests were carried out in four models: the SVM, SVM+PSO, SVM+GA, SVM+PSO+GA algorithms. The experimental results show that SVM+PSO+GA modeling obtained the best accuracy results of 95.00%, with an AUC value of 0.912.

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References

M. I. Petiwi, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Gofood Berdasarkan Twitter Menggunakan Metode Naïve Bayes dan Support Vector Machine,” vol. 6, pp. 542–550, 2022, doi: 10.30865/mib.v6i1.3530.

M. I. D. Sakariana, Idriati, and C. Dewi, “Analisis Sentimen Pemindahan Ibu Kota Indonesia Dengan Pembobotan Term BM25 Dan Klasifikasi Neighbor Weighted K-Nearest Neighbor,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 3, pp. 748–755, 2020.

Ratino, N. Hafidz, S. Anggraeni, and W. Gata, “Sentimen Analisis Informasi Covid-19 menggunakan Support Vector Machine dan Naïve Bayes,” J. JUPITER, vol. 12, no. 2, pp. 1–11, 2020.

L. Nilawati and Y. E. Achyani, “Optimasi Metode Particle Swarm Optimization (PSO) Pada Prediksi Penilaian Apartemen,” Paradig. - J. Komput. dan Inform., vol. 21, no. 2, pp. 227–234, 2019, doi: 10.31294/p.v21i2.6159.

Y. E. Achyani, “Penerapan Metode Particle Swarm Optimization Pada Optimasi Prediksi Pemasaran Langsung,” J. Inform., vol. 5, no. 1, pp. 1–11, 2018, doi: 10.31311/ji.v5i1.2736.

L. Paranduk, A. Indriani, M. Hafid, and Suprianto, “Sistem Informasi Penjadwalan Mata Kuliah Menggunakan Algoritma Genetika Berbasis Web,” Semin. Nas. Apl. Teknol. Inf., pp. E46–E50, 2018.

I. Kurniawati and H. F. Pardede, “Hybrid Method of Information Gain and Particle Swarm Optimization for Selection of Features of SVM-Based Sentiment Analysis,” 2018 Int. Conf. Inf. Technol. Syst. Innov. ICITSI 2018 - Proc., no. October, pp. 1–5, 2018, doi: 10.1109/ICITSI.2018.8695953.

M. A. Hasanah, S. Soim, and A. S. Handayani, “Implementasi CRISP-DM Model Menggunakan Metode Decision Tree dengan Algoritma CART untuk Prediksi Curah Hujan Berpotensi Banjir,” J. Appl. Informatics Comput., vol. 5, no. 2, pp. 103–108, 2021, doi: 10.30871/jaic.v5i2.3200.

N. H. Purnomo, B. Pamungkas, and C. Juliane, “Penerapan Algoritma C4. 5 Untuk Klasifikasi Tren Pelanggaran Kendaraan Angkutan Barang dengan Metode CRISP-DM,” J. Media Inform. Budidarma, vol. 7, no. 1, pp. 30–40, 2023, doi: 10.30865/mib.v7i1.5247.

H. Santoso, I. P. Hariyadi, and Prayitno, “Data Mining Analisa Pola Pembelian Produk,” Tek. Inform., no. 1, pp. 19–24, 2016, [Online]. Available: http://ojs.amikom.ac.id/index.php/semnasteknomedia/article/download/1267/1200.

R. Darmawan, I. Indra, and A. Surahmat, “Optimalisasi Support Vector Machine (SVM) Berbasis Particle Swarm Optimization (PSO) Pada Analisis Sentimen Terhadap Official Account Ruang Guru di Twitter,” J. Kaji. Ilm., vol. 22, no. 2, pp. 143–152, 2022, doi: 10.31599/jki.v22i2.1130.

V. K. S. Que, A. Iriani, and H. D. Purnomo, “Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 9, no. 2, pp. 162–170, 2020, doi: 10.22146/jnteti.v9i2.102.

T. Penentuan, R. Kredit, M. Kecil, and D. A. N. Menengah, “Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi,” vol. 4, no. 1, pp. 212–223, 2023.

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, doi: 10.31294/jtk.v4i2.

H. C. Husada and A. S. Paramita, “Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM),” Teknika, vol. 10, no. 1, pp. 18–26, 2021, doi: 10.34148/teknika.v10i1.311.

M. Syarifuddinn, “Analisis Sentimen Opini Publik Terhadap Efek Psbb Pada Twitter Dengan Algoritma Decision Tree,Knn, Dan Naïve Bayes,” INTI Nusa Mandiri, vol. 15, no. 1, pp. 87–94, 2020, doi: 10.33480/inti.v15i1.1433.

E. Indrayuni, “Analisa Sentimen Review Hotel Menggunakan Algoritma Support Vector Machine Berbasis Particle Swarm Optimization,” J. Evolusi Vol. 4 Nomor 2 - 2016, vol. 4, no. 2, pp. 20–27, 2016.