Optimization of support vector machine based on particle swarm optimization for detecting hate speech for karawang election 2020
The rise of hate speech on social media can harm various parties, including the candidate for regional head of Karawang Regency in 2020, but because of the large number of comments, the sanctions given to violators are not evenly distributed. To make it easier for Bawaslu to give sanctions to violators and to provide a deterrent effect to the Karawang community so that hate speech does not occur again. Therefore, this study was conducted by classifying positive and negative comments. The methodology used is Knowledge Discovery in Database (KDD) by dividing the data into 4 scenarios. The results obtained state that the Support Vector Machine (SVM) Algorithm with scenario "2" on a linear kernel gets the highest accuracy value of "72.66%". Then the results of the 4 scenarios were optimized by Particle Swarm Optimization which got the highest accuracy value, namely the linear and polynomial kernels in the 4th scenario with 90:10 data sharing of "78.00%". Other evaluation values also experienced the same increase, starting from precision, recall, and f1-score. It can be concluded that the Support Vector Machine algorithm optimized with Particle Swarm Optimization can increase the accuracy value.
C. Sutrisno, “Partisipasi Warga Negara Dalam Pilkada,” JPK J. Pancasila Dan Kewarganegaraan, vol. 2, no. 2, pp. 36–48, 2017.
“Daftar 270 Daerah Penyelenggara Pilkada Serentak pada 2020.” https://tirto.id/daftar-270-daerah-penyelenggara-pilkada-serentak-pada-2020-ecZT (accessed Dec. 16, 2021).
“Berikut Daftar 270 Daerah yang Gelar Pilkada Serentak 9 Desember 2020 Halaman all - Kompas.com.” https://www.kompas.com/tren/read/2020/12/05/193100165/berikut-daftar-270-daerah-yang-gelar-pilkada-serentak-9-desember-2020?page=all (accessed Dec. 16, 2021).
litigasi.id, "Jeratan Hukum Ujaran Kebencian (Hate Speech)," 28 Mei 2018.
S. N. Asiyah, “Klasifikasi berita online menggunakan metode support vector machine dan k-nearest neighbor,” PhD Thesis, Institut Teknologi Sepuluh Nopember, 2016.
A. Saepudin, R. Aryanti, E. Fitriani, and D. Dahlia, “Optimasi Algoritma SVM Dan k-NN Berbasis Particle Swarm Optimization Pada Analisis Sentimen Fenomena Tagar# 2019GantiPresiden,” J. Tek. Komput., vol. 6, no. 1, pp. 95–102, 2020.
F. F. Irfani, M. Triyanto, and A. D. Hartanto, “Analisis Sentimen Review Aplikasi Ruangguru Menggunakan Algoritma Support Vector Machine,” JBMI J. Bisnis Manaj. Dan Inf. Vol 16 No 3 P 258 2020 Doi 1026487jbmi V16i3 8607, 2020.
H. S. Utama, D. Rosiyadi, B. S. Prakoso, and D. Ariadarma, “Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine,” J. RESTI Rekayasa Sist. Dan Teknol. Inf., vol. 3, no. 2, pp. 243–250, 2019.
U. Ependi and A. Putra, “Solusi prediksi persediaan barang dengan menggunakan algoritma apriori (studi kasus: regional part depo auto 2000 Palembang),” JEPIN J. Edukasi Dan Penelit. Inform., vol. 5, no. 2, pp. 139–145, 2019.
K. A. Safitri and R. Wulanningrum, “Aplikasi Pengenalan Pola Tulisan Tangan Menggunakan Metode Support Vector Machine,” in Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 2020, vol. 4, no. 1, pp. 201–206.
N.R. Feta, and A.R. Ginanjar, "Komparasi Fungsi Kernel Metode Support Vector Machine Untuk Pemodelan Klasifikasi Terhadap Comparison of the Kernel Function of Support Vector Machine Method for Modeling Classification of Soybean Plat Disease," vol. 1, no. 1, pp. 33-39, 2019.
M. R. Lubis, “Metode Hybrid Particle Swarm Optimization-Neural Network Backpropagation Untuk Prediksi Hasil Pertandingan Sepak Bola,” J-SAKTI J. Sains Komput. Dan Inform., vol. 1, no. 1, pp. 71–83, 2017.
L. Mutawalli, M. T. A. Zaen, and W. Bagye, “Klasifikasi Teks Sosial Media Twitter Menggunakan Support Vector Machine (Studi Kasus Penusukan Wiranto),” J. Inform. Dan Rekayasa Elektron., vol. 2, no. 2, pp. 43–51, 2019.
Copyright (c) 2021 Wahyuningrum Ayu, Rijal Abdulhakim, Yuyun Umaidah, Jajam Haerul Jaman
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).