COMPARATIVE ANALYSIS OF PSO AND FIREFLY OPTIMIZATION FOR VIOLENCE REPORT CLASSIFICATION
Abstract
Cases of violence against children and women continue to increase, but the handling of reports is often hampered by the large volume of incoming reports and the lengthy manual classification process. This study aims to address these issues by developing a method for automatically classifying reports of violence using the Support Vector Machine (SVM) algorithm optimized with Particle Swarm Optimization (PSO) and Firefly algorithms. The main objective is to group types of violence accurately to facilitate faster and more effective identification and handling. The research dataset consists of 500 reports obtained from Kaggle, with stages including text pre-processing, implementation of optimization algorithms, and evaluation based on accuracy, precision, recall, and misclassification error. The experiments were conducted using Python on the Google Colab platform. The results showed that PSO-SVM achieved an accuracy of 87.00% and a recall of 80.42%, outperforming Firefly-SVM which achieved an accuracy of 86.00% and a recall of 78.75%. Although Firefly-SVM demonstrated slightly higher precision (92.63%) compared to PSO-SVM (91.53%), PSO-SVM had a lower misclassification error (13.00% compared to 14.00%). These findings indicate that PSO-SVM is more effective for applications requiring better case detection, while Firefly-SVM is more suitable for applications prioritizing precision in positive predictions.
References
M. R. Fanani, F. K. Fikriah, U. Azizah, and M. G. Aziz, “Seleksi Fitur PSO untuk Klasifikasi Jenis Kekerasan dengan Algoritma C4.5,” Smart Comp Jurnalnya Orang Pint. Komput., vol. 12, no. 1, pp. 55–63, 2023, doi: 10.30591/smartcomp.v12i1.4407.
E. F. Santika, “Kekerasan Seksual Jadi Jenis yang Paling Banyak Dialami Korban Sepanjang 2022,” 2023. https://databoks.katadata.co.id/datapublish/2023/02/03/kekerasan-seksual-jadi-jenis-yang-paling-banyak-dialami-korban-sepanjang-2022.
P. R. Eshardiansyah, N. Sulistiyowati, and M. Jajuli, “Algoritma C4.5 Untuk Klasifikasi Jenis Kekerasan pada Anak (Kasus DP3A Kabupaten Karawang),” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 687–696, 2021.
J. A. Tatimu, R. V. Karamoy, and A. T. Koesoemo, “Analisis Yuridis Undang-Undang Tindak Pidana Kekerasan Seksual Berbasis Gender,” J. Fak. Huk. UNSRAT Lex Adm., vol. 12, no. 3, 2024, [Online]. Available: https://lm.psikologi.ugm.
E. Prastini, “Kekerasan Terhadap Anak dan Upaya Perlindungan Anak di Indonesia,” J. Citizenhip Virtues, vol. 4, no. 2, pp. 760–770, 2024, [Online]. Available: https://sidiaperka.kemenpppa.go.id/kekerasan-terhadap-anak-dan-remaja-di-indonesia/#:~:text=Kekerasan fisik dibedakan menjadi 3,dengan pisau atau senjata lain.
V. A. De Wahyu, A. E. Junita, A. Destiana, K. A. Setyabudi, F. N. Daini, and F. H. B. Laksiao, “Analisis Kinerja Penyelidikan Dan Penyidikan Dalam Menanggulangi Tindak Pidana Kriminal di Polres Karanganyar,” Aliansi J. Hukum, Pendidik. dan Sos. Hum., vol. 1, no. 2, pp. 50–62, 2024, doi: 10.62383/aliansi.v1i2.58.
B. A. R. P. Wahyu, F. A. Fayi, C. P. Mahendra, and R. K. Hapsari, “Klasifikasi Penderita Penyakit Diabetes Menggunakan Algoritma Decision Tree C4.5,” J. Inf. Technol., vol. 8, no. 1, pp. 80–89, 2023, doi: 10.47970/siskom-kb.v4i1.173.
S. R. Cholil, V. Vydia, and Susanto, “Prediksi Lama Masa Tunggu Alumni USM dalam Mendapatkan Pekerjaan dengan Algoritma KNN,” Indones. J. Comput. Inf. Technol., vol. 9, no. 2, pp. 104–112, 2024.
G. Subroto, N. Sulistiyowati, and A. A. Ridha, “Klasifikasi Jenis Kekerasan Pada Perempuan Dan Anak Dengan Algoritma Multinomial Naïve Bayes,” INTECOMS J. Inf. Technol. Comput. Sci., vol. 5, no. 1, pp. 104–113, 2022, doi: 10.31539/intecoms.v5i1.3598.
N. Rohim and N. C. Aminuallah, “Klasifikasi Data Opini Film Algoritma Support Vector,” Portaldata.org, vol. 2, no. 2, pp. 1–12, 2022.
I. Nurul Hassanah, S. Faisal, and A. Mutoi Siregar, “Perbandingan Algoritma Support Vector Machine Dengan Decision Tree Pada Aplikasi Ruangguru,” Kumpul. J. Ilmu Komput., vol. 10, no. 1, pp. 39–50, 2023.
L. B. Ilmawan and M. A. Mude, “Perbandingan Metode Klasifikasi Support Vector Machine dan Naïve Bayes untuk Analisis Sentimen pada Ulasan Tekstual di Google Play Store,” Ilk. J. Ilm., vol. 12, no. 2, pp. 154–161, 2020, doi: 10.33096/ilkom.v12i2.597.154-161.
M. I. Maulana, E. Budianita, M. Fikry, and F. Yanto, “Klasifikasi Sentiment Ulasan Aplikasi Sausage Man Menggunakan VADER Lexicon dan Naïve Bayes Classifier,” J. Sist. Komput. dan Inform., vol. 4, no. 3, pp. 485–492, 2023, doi: 10.30865/json.v4i3.5854.
R. C. Rivaldi and T. D. Wismarini, “Analisis Sentimen Pada Ulasan Produk Dengan Metode Natural Language Processing ( NLP ) ( Studi Kasus Zalika Store 88 Shopee ),” J. Ilm. Elektron. dan Komput., vol. 17, no. 1, pp. 120–128, 2024.
A. Andi, J. Charles, O. Pribadi, C. Juliandy, and R. Robet, “Game Development ‘Kill Corona Virus’ For Education About Vaccination Using Finite State Machine and Collision Detection,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, no. 4, 2022, doi: 10.22219/kinetik.v7i4.1470.
Robet, C. Juliandy, Andi, Hendri, J. Hendrik, and F. A. Tarigan, “Image Road Surface Classification Based on GLCM Feature Using LGBM Classifier,” IOP Conf. Ser. Earth Environ. Sci., vol. 1083, no. 1, 2022, doi: 10.1088/1755-1315/1083/1/012006.
K. L. Kohsasih, B. H. Hayadi, Robet, C. Juliandy, O. Pribadi, and Andi, “Sentiment Analysis for Financial News Using RNN-LSTM Network,” 2022 4th Int. Conf. Cybern. Intell. Syst. ICORIS 2022, 2022, doi: 10.1109/ICORIS56080.2022.10031595.
Andi, C. Juliandy, R. Robet, O. Pribadi, and R. Wijaya, “Image Authentication Application with Blockchain to Prevent and Detect Image Plagiarism,” 2021 6th Int. Conf. Informatics Comput. ICIC 2021, no. December, 2021, doi: 10.1109/ICIC54025.2021.9632966.
M. A. Muslim et al., Data Mining Algoritma C4.5. 2019.
Andi, C. Juliandy, Robet, and O. Pribadi, “Securing Medical Records of COVID-19 Patients Using Elliptic Curve Digital Signature Algorithm (ECDSA) in Blockchain,” CommIT J., vol. 16, no. 1, pp. 87–96, 2022, doi: 10.21512/COMMIT.V16I1.7958.
M. Abbaszadeh, S. Soltani-Mohammadi, and A. N. Ahmed, “Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method,” Comput. Geosci., vol. 165, no. 8, 2022.
I. M. D. P. Asana, I. D. G. A. Oka, I. M. O. Widyantara, and I. M. S. Sandhiyasa, “Improved SVM Classification Using Particle Swarm Optimization for Student Completion Prediction System,” JUITA J. Inform., vol. 12, no. 2, pp. 217–225, 2024.
DOI: https://doi.org/10.33387/jiko.v8i2.9721
Refbacks
- There are currently no refbacks.