Analisis Sentimen Isu Childfree Di Media Sosial Twitter Menggunakan Algoritma Support Vector Machine
Abstract
Abstract
Sentiment analysis is a process to process, convert and interpret a text and classify it in the form of positive and negative sentiments. The phenomenon of childfree in Indonesia is currently causing debate and has become a trending topic on several social media, especially Twitter. The assumption that childfree decisions are categorized as selfish decisions is certainly closely related to the patriarchal culture that exists in Indonesia. This patriarchal culture is certainly very much in line with the concept of gender construction, where the childfree decision for women is considered a form of female selfishness. Based on this, an analysis of public sentiment related to the issue of childfree on Twitter social media using the Support Vector Machine (SVM) algorithm using 4 kernels. This research uses the KDD method by going through the stages of data selection, preprocessing, transformation, data mining, and evaluation. The data used are tweets totaling 1,447 tweets. The data was then selected into 1,447 which were divided into 1178 positive label data and 226 negative label data. In the data mining stage, the data is divided into 4 scenarios, namely 90:10, 80:20, 70:30, and 60:40. The best results were found in the first scenario with the Linear kernel, resulting in 75.93% accuracy, 83.33% precision, and 68.97% recall, showing the effectiveness of the algorithm in analyzing sentiment regarding the childfree phenomenon on Twitter.
Keywords: Sentiment Analysis, Childfree, Support Vector Machine
References
Arsi, P., & Waluyo, R. (2021). ANALISIS SENTIMEN WACANA PEMINDAHAN IBU KOTA INDONESIA MENGGUNAKAN ALGORITME SUPPORT VECTOR MACHINE (SVM). 8(1), 147–156. https://doi.org/10.25126/jtiik.202183944
Arsyad, Z. (2019). TEXT MINING MENGGUNAKAN GENERATE ASSOCIATION RULE WITH WEIGHT (GARW) ALGORITHM UNTUK ANALISIS TEKS WEB CRAWLER. 153–171.
Ayani, D. D., Pratiwi, H. S., & Muhardi, H. (2019). Implementasi Web Scraping untuk Pengambilan Data pada Situs Marketplace. 7(4), 257–262.
Blidex, B., & Wibowo, J. S. (2021). ANALISIS SENTIMEN KLASIFIKASI TWEET VAKSIN COVID 19 DENGAN NAÏVE BAYES. Jurnal Mahajana Informasi, 6(2), 103-110.
Chandra, A. I., Yulia, Y., & Adipranata, R. (2020). Aplikasi Penentu Subyek Skripsi Menggunakan Metode Support Vector Machine. Jurnal Infra, 8(2), 182-188.
Cindo, M., Rini, D. P., & Ermatita. (2019). Literatur Review : Metode Klasifikasi Pada Sentimen Analisis. 66–70.
Data, K. (2023). 10 Negara dengan Jumlah Pengguna Twitter Terbanyak di Dunia (Januari 2023). https://databoks.katadata.co.id/datapublish/2023/02/27/pengguna-twitter-di-indonesia-capai-24-juta-hingga-awal-2023-peringkat-berapa-di-dunia
Drus, Z., & Khalid, H. (2019). Sentiment Analysis in Social Media and Its Application : Systematic Literature Review Literature Review. Procedia Computer Science, 161, 707–714. https://doi.org/10.1016/j.procs.2019.11.174
Ela, M., Savira, A., Dewi, N. U. S., & Triyanti, F. (2022). FENOMENA CHILDFREE DI JEPANG DALAM PERSPEKTIF TEORI FEMINISME EKSISTENSIALIS. 1(2), 61–72.
Filcha, A., & Hayaty, M. (2019). Implementasi Algoritme Rabin-Karp untuk Pendeteksi Plagiarisme pada Dokumen Tugas Mahasiswa ( Rabin-Karp Algorithm Implementation to Detect Plagiarism o n Student ’ s Assignment Document ). VII, 25–32.
Fitriyah, N., Warsito, B., & Maruddani, D. A. I. (2020). Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (SVM). Jurnal Gaussian, 9(3), 376–390. https://doi.org/10.14710/j.gauss.v9i3.28932
Hasri, C. F., & Alita, D. (2022). PENERAPAN METODE NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE PADA ANALISIS SENTIMEN TERHADAP DAMPAK VIRUS CORONA DI TWITTER. 3(2), 145–160.
Hastie, T., Tibshirani, R., & Friedman, J. (2021). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition). Springer.
Herlinawati, N., Yuliani, Y., Faizah, S., & Gata, W. (2020). ANALISIS SENTIMEN ZOOM CLOUD MEETINGS DI PLAY STORE MENGGUNAKAN NAÏVE BAYES DAN SUPPORT VECTOR MACHINE . 5(2), 293–298.
Ilmawan, L. B., & Mude, M. A. (2020). Perbandingan Metode Klasifikasi Support Vector Machine dan Naïve Bayes untuk Analisis Sentimen pada Ulasan Tekstual di Google Play Store. ILKOM Jurnal Ilmiah, 12(2), 154–161. https://doi.org/10.33096/ilkom.v12i2.597.154-161
Indonesia, D. (2022). 8 Negara Ini Miliki Tingkat Kelahiran Sangat Rendah di Dunia. https://dataindonesia.id/ragam/detail/8-negara-ini-miliki-tingkat-kelahiran-sangat-rendah-di-dunia
Jenuri, Islmay, M. R. F., Komariah, K. S., Suwarma, D. M., & Fitria, A. H. N. (2022). Fenomena childfree di era modern: studi fenomenologis generasi gen z serta pandangan islam terhadap childfree di indonesia. 19(2).
Kumar, A. (2020). Data Science: Support Vector Machine (SVM). https://medium.datadriveninvestor.com/data-science-support-vector-machine-svm-fa74fb12d349
Liu, B., Li, X., Li, X., Li, Y., & Ji, Z. (2018). A hybrid KDD model based on multi-granularity fuzzy clustering for financial risk analysis. IEEE Access, 6, 28922-28932.
Marisa, F., Maukar, A. L., & Akhriza, T. M. (2021). DATA MINING KONSEP DAN PENERAPANNYA. Deepublish Publisher.
Mingkase, N., & Rohmaniyah, I. (2022). Konstruksi gender dalam problematika childfree di sosial media Twitter. Yinyang: Jurnal Studi Islam Gender dan Anak, 17(2), 201-222.
Nabila, Z., Isnain, A. R., Permata, P., & Abidin, Z. (2021). Analisis data mining untuk clustering kasus covid-19 di Provinsi Lampung dengan algoritma k-means. Jurnal Teknologi Dan Sistem Informasi, 2(2), 100-108.
Natasuwarna, A. P. (2019). Analisis Sentimen Keputusan Pemindahan Ibukota Negara Menggunakan Klasifikasi Naive Bayes. Sensitif (Seminar Nasional Sistem Informasi Dan Teknik Informatika), 47–53.
Pamungkas, F. S., & Kharisudin, I. (2021). Analisis Sentimen dengan SVM , NAIVE BAYES dan KNN untuk Studi Tanggapan Masyarakat Indonesia Terhadap Pandemi Covid-19 pada Media Sosial Twitter. 4, 628–634.
Petiwi, M. I., Triayudi, A., & Sholihati, I. D. (2022). Analisis Sentimen Gofood Berdasarkan Twitter Menggunakan Metode Naïve Bayes dan Support Vector Machine . 6, 542–550. https://doi.org/10.30865/mib.v6i1.3530
Pradana, M. G. (2020). Penggunaan Fitur Wordcloud Dan Document Term Matrix Dalam Text Mining. Jurnal Ilmiah Informatika (JIF), 8(1), 38–43.
Pratiwi, B. P., Handayani, A. S., & Sarjana. (2020). Pengukuran Kinerja Sistem Kualitas Udara Dengan Teknologi WSN Menggunakan Confusion matrix. 6(2), 66–75.
Pravina, A. M., Cholissodin, I., & Adikara, P. P. (2019). Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine (SVM) (Vol. 3, Issue 3). http://j-ptiik.ub.ac.id
Ramadhani, K. W., & Tsabitah, D. (2022). FENOMENA CHILDFREE DAN PRINSIP IDEALISME KELUARGA INDONESIA DALAM PERSPEKTIF MAHASISWA. 11(1), 17–29.
Rohanah, A., Rianti, D. L., & Sari, B. N. (2021). Perbandingan Naïve Bayes dan Support Vector Machine untuk Klasifikasi Ulasan Pelanggan Indihome. STRING (Satuan Tulisan Riset Dan Inovasi Teknologi), 6(1), 23. https://doi.org/10.30998/string.v6i1.9232
Saini, A. (2021). Support Vector Machine (SVM): A Complete guide for beginners. https://www.analyticsvidhya.com/blog/2021/10/support-vector-machinessvm-a-complete-guide-for-beginners/
Saputra, R., & Sibarani, A. J. (2020). Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Meningkatkan Pola Penjualan Obat. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 7(2), 262-276.
Seera, M., & Zhang, Z. (2019). Enhancing credit scoring models using synthetic data augmentation. Expert Systems with Applications, 123, 347-357.
Sianturi, F. A., Hasugian, P. M., Simangunsong, A., & Nadeak, B. (2019). Data Mining Teori dan Aplikasi Weka. IOCS Publisher.
Siswanto, A. W., & Nurhasanah, N. (2022). Analisis Fenomena Childfree di Indonesia. 64–70.
Syahrudin, A. N., & Kurniawan, T. (2018). INPUT DAN OUTPUT PADA BAHASA PEMROGRAMAN PYTHON. 1–7.
Tineges, R., Triayudi, A., & Sholihati, I. D. (2020). Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM). JURNAL MEDIA INFORMATIKA BUDIDARMA, 4(3), 650. https://doi.org/10.30865/mib.v4i3.2181
Twitter. (2023). Twitter. https://help.twitter.com/id/resources/new-user-faq
United Nations, Department of Economic and Social Affairs, Population Division. (2022). World Population Prospects 2022: Data Portal. Retrieved from https://population.un.org/dataportal/data/indicators/19/locations/96,116,360,418,458,104,608,702,764,626,704/start/2022/end/2022/table/pivotbylocation
Zulqornain, J. A., Indriati, I., & Adikara, P. P. (2021). Analisis sentimen tanggapan masyarakat aplikasi tiktok menggunakan metode naïve bayes dan categorial propotional difference (cpd). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 5(7), 2886-2890.
