COMPARISON OF SUPPORT VECTOR MACHINE BASED ON FASTTEXT WITHOUT AND WITH FIREFLY OPTIMIZATION PARAMETERS FOR DISASTER SENTIMENT ANALYSIS IN INDONESIA
Abstract
Sentiment analysis is a process for analyzing opinions, sentiments, assessments, and emotions from someone's statements regarding a domain or is also a process for entering and processing data in the form of text. Support vector machine (SVM) is a supervised machine learning technique that functions as a separator of two classes of data. SVM aims to obtain numerical vectors using fasttext. SVM cannot choose appropriate parameters so the use of parameters is not optimal. To obtain optimal parameters with better classification results, firefly optimization was carried out. This research compares the fasttext-based SVM method without and with firefly optimization parameters using data from tweets with the keyword "Indonesian disaster" which was crawled using the Twitter application. The results of this research obtained 128 dimensions that form the weight of each word. This means that each word is represented in a 128-dimensional vector space. The evaluation of the SVM classification model with and without firefly optimization provides an accuracy of 89.1% and 61.3% respectively. This shows that the SVM classification method with firefly optimization provides quite good classification performance compared to the SVM model without optimization.
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M. R. Pahleviannur, “Edukasi Sadar Bencana Melalui Sosialisasi Kebencanaan Sebagai Upaya Peningkatan Pengetahuan Siswa Terhadap Mitigasi Bencana,” Jurnal Pendidikan Ilmu Sosial, vol. 29, no. 1, pp. 49–55, Jun. 2019, doi: 10.23917/jpis.v29i1.8203.
N. Giarsyani, “Komparasi Algoritma Machine Learning dan Deep Learning untuk Named Entity Recognition: Studi Kasus Data Kebencanaan,” Indonesian Journal of Applied Informatics, vol. 4, no. 2, p. 138, Aug. 2020, doi: 10.20961/ijai.v4i2.41317.
Kasbawati, “Kontrol Optimal Upaya Pencegahan Infeksi Virus Flu Burung H5N1 dalam Populasi Burung dan Manusia,” Jurnal Matematika, Statistika dan Komputasi, vol. 8, no. 1, pp. 12–24, 2011.
C.-H. Wu, “Social Sensor: An Analysis Tool for Social Media,” International Journal of Electronic Commerce Studies, vol. 7, no. 1, pp. 77–94, Jun. 2016, doi: 10.7903/ijecs.1411.
F. Fitriana, E. Utami, and H. Al Fatta, “Analisis Sentimen Opini Terhadap Vaksin Covid - 19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes,” Jurnal Komtika (Komputasi dan Informatika), vol. 5, no. 1, pp. 19–25, Jul. 2021, doi: 10.31603/komtika.v5i1.5185.
P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 8, no. 1, pp. 147–156, 2021, doi: 10.25126/jtiik.202183944.
A. R. W. Rapsanjani and E. Junianto, “Implementasi Probabilistic Neural Network Dan Word Embedding Untuk Analisis Sentimen Vaksin Sinovac,” Jurnal Responsif: Riset Sains dan Informatika, vol. 3, no. 2, pp. 233–242, Aug. 2021, doi: 10.51977/jti.v3i2.588.
M. Awad and R. Khanna, “Support Vector Machines for Classification,” in Efficient Learning Machines, Berkeley, CA: Apress, 2015, pp. 39–66. doi: 10.1007/978-1-4302-5990-9_3.
A. Noor and M. Islam, “Sentiment Analysis for Women’s E-commerce Reviews using Machine Learning Algorithms,” in 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, Jul. 2019, pp. 1–6. doi: 10.1109/ICCCNT45670.2019.8944436.
Styawati, Andi Nurkholis, Zaenal Abidin, and Heni Sulistiani, “Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 5, pp. 904–910, Oct. 2021, doi: 10.29207/resti.v5i5.3380.
K. Devi Thangavel, U. Seerengasamy, S. Palaniappan, and R. Sekar, “Prediction of factors for Controlling of Green House Farming with Fuzzy based multiclass Support Vector Machine,” Alexandria Engineering Journal, vol. 62, pp. 279–289, Jan. 2023, doi: 10.1016/j.aej.2022.07.016.
I. Pakpahan and Jasman Pardede, “Analisis Sentimen Penanganan Covid-19 Menggunakan Metode Long Short-Term Memory Pada Media Sosial Twitter,” Jurnal Publikasi Teknik Informatika, vol. 2, no. 1, pp. 12–25, Jan. 2023, doi: 10.55606/jupti.v1i1.767.
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
R. Nazrul and N. C. Aminuallah, “Klasifikasi Data Opini Film Algoritma Support Vector Machine-Firefly,” Portal Data, vol. 2, no. 5, pp. 1-12, 2022.
W. Chen, Z. Xu, X. Zheng, Q. Yu, and Y. Luo, “Research on Sentiment Classification of Online Travel Review Text,” Applied Sciences, vol. 10, no. 15, p. 5275, Jul. 2020, doi: 10.3390/app10155275.
S. Mulyani, S. A. Thamrin, and S. Siswanto, “Analisis Sentimen Masyarakat Pada Kebijakan Vaksinasi Covid-19 Di Twitter Menggunakan Metode Mesin Vektor Pendukung Dengan Kernel Radial Basis Function Berbasis Fitur Leksikon,” Jambura Journal of Probability and Statistics, vol. 3, no. 2, pp. 110–119, Nov. 2022, doi: 10.34312/jjps.v3i2.16663.
S. Symeonidis, D. Effrosynidis, and A. Arampatzis, “A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis,” Expert Syst Appl, vol. 110, pp. 298–310, Nov. 2018, doi: 10.1016/j.eswa.2018.06.022.
R. Yang, M. Burns, A. De Hoedt, S. Williams, S. Freedland, and Z. Klaassen, “MP29-09 Identification of Prostate Cancer Metastatic Disease for Different Risk Groups Based On Fasttext Word Embedding And Supervised Learning,” Journal of Urology, vol. 209, no. Supplement 4, Apr. 2023, doi: 10.1097/JU.0000000000003257.09.
M. D. Rahman, A. Djunaidy, and F. Mahananto, “Penerapan Weighted Word Embedding pada Pengklasifikasian Teks Berbasis Recurrent Neural Network untuk Layanan Pengaduan Perusahaan Transportasi,” Jurnal Sains dan Seni ITS, vol. 10, no. 1, Aug. 2021, doi: 10.12962/j23373520.v10i1.56145.
S. Chatterjee, L. Evenss, P. Bhattacharyya, and J. Mondal, “LSJSP at SemEval-2023 Task 2: FTBC: A FastText based framework with pre-trained BERT for NER,” in Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), Stroudsburg, PA, USA: Association for Computational Linguistics, 2023, pp. 1254–1259. doi: 10.18653/v1/2023.semeval-1.174.
H. Li, “Support Vector Machine,” in Machine Learning Methods, Singapore: Springer Nature Singapore, 2024, pp. 127–177. doi: 10.1007/978-981-99-3917-6_7.
N. Rezki, S. A. Thamrin, and S. Siswanto, “Sentiment Analysis of Merdeka Belajar Kampus Merdeka Policy Using Support Vector Machine with Word2vec,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 1, pp. 0481–0486, Apr. 2023, doi: 10.30598/barekengvol17iss1pp0481-0486.
D. A. Sani and M. Z. Sarwani, “Koreksi Jawaban Esai Berdasarkan Persamaan Makna Menggunakan Fasttext dan Algoritma Backpropagation,” Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), vol. 11, no. 2, pp. 92–111, Aug. 2022, doi: 10.23887/janapati.v11i2.49192.
S. K. Sunori, D. K. Singh, A. Mittal, S. Maurya, U. Mamodiya, and P. K. Juneja, “Rainfall Classification using Support Vector Machine,” in 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), IEEE, Nov. 2021, pp. 433–437. doi: 10.1109/I-SMAC52330.2021.9640773.
H. Swapnarekha, J. Nayak, H. S. Behera, P. B. Dash, and D. Pelusi, “An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets,” Mathematical Biosciences and Engineering, vol. 20, no. 2, pp. 2382–2407, 2022, doi: 10.3934/mbe.2023112.
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