The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments

Amimah Shabrina Putri Prasmono; Mujiati Dwi Kartikasari

doi:10.37905/jjom.v6i1.23591

The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments

Amimah Shabrina Putri Prasmono, Mujiati Dwi Kartikasari

Abstract

Childfree is a condition in which a person or couple decides not to have children in marriage. Childfree became popular in Indonesia when YouTuber and influencer Gita Savitri uploaded an Instagram story about it. This brought many pros and cons among the people towards the freedom to have children. Many TV broadcasts and YouTube videos cover this phenomenon. Several YouTube channels that broadcast this phenomenon are Menjadi Manusia and Analisa Channel. We collect YouTube comment data using web scraping techniques. From September 2021 to September 2022, 674 sample data points were obtained from two YouTube videos. Data is labelled (positive, negative, and neutral) using the Indonesian language lexicon approach as well as the Support Vector Machine (SVM) and Random Forest algorithms to determine the best model for classifying YouTube comments. The purpose of this research is to understand the public's perception of childfree and to compare the accuracy and AUC values of the two methods. Based on the results of the analysis, 128 comments are classified as positive, the remaining 39 comments are classified as neutral, and 503 comments are classified as negative. This shows that that the commentators on YouTube do not support or give a negative stigma to people who adhere to childfree. The solution to the balanced data problem for each sentiment class uses the random oversampling (ROS) approach. The RBF kernel SVM classification algorithm is a suitable method for classifying commentary data with an accuracy of 98.01% and an AUC of 98.58%, while the Random Forest algorithm only obtains an accuracy of 94.37% and an AUC of 95.87%.

Keywords

Childfree; Sentiments Analysis; Lexicon; Support Vector Machine; Random Forest

Full Text:

PDF

References

N. Mingkase and I. Rohmaniyah, “Konstruksi Gender dalam Problematika Childfree di Sosial Media Twitter,” Yinyang: Jurnal Studi Islam Gender dan Anak, vol. 17, no. 2, pp. 201–222, 2022, doi: 10.24090/yinyang.v17i2.6486.

A. W. Siswanto and N. Nurhasanah, “Analisis Fenomena Childfree di Indonesia,” Bandung Conference Series: Islamic Family Law, vol. 2, no. 2, 2022, doi: 10.29313/bcsifl.v2i2.2684.

Badan Pusat Statistik, “Laju Pertumbuhan Penduduk (Persen) Indonesia,” Badan Pusat Statistik (BPS). Accessed: Jan. 02, 2024.[Online]. Available: https://www.bps.go.id/id/statistics-table/2/MTk3NiMy/lajupertumbuhan-penduduk.html

A. Budianto, D. Maryono, and R. Ariyuana, “Perbandingan K-Nearest Neighbor (KNN) dan Support Vector Machine (SVM) Dalam Pengenalan Karakteristik Plat Kendaraan Kendaraan Bermotor,” Jurnal Ilmiah Pendidikan Teknik Kejuruan, vol. 11, no. 1, pp. 1–9, 2018, doi: 10.20961/jiptek.v11i1.18018.

M. J. Ubaidillah, L. Munadhif, and N. Rinanto, “Klasifikasi Gelombang Otot Lengan pada Robot Manipulator Menggunakan Support Vector Machine,” REKAYASA, vol. 2, no. 12, pp. 91–97, 2019, doi: 10.21107/rekayasa.v12i2.5406.

R. Pratiwi, D. Dairoh, D. Af ’idah, S. H, Q. A, and A. F, “Analisis Sentimen Pada Review Skincare Female Daily Menggunakan Metode Support Vector Machine (SVM),” Journal of Informatics Information System Software Engineering and Applications (INISTA), vol. 4, no. 1, pp. 40–46, 2021, doi: 10.20895/inista.v4i1.387.

C. Septiani Hudaya, H. Fakhrurroja, and A. Alamsyah, “Analisis Persepsi Konsumen Terhadap Brand Go-Jek Pada Media Sosial Twitter Menggunakan Metode Sentimen Analysis dan Topic Modelling,” Jurnal Mitra Manajemen (JMM Online), vol. 1, no. 6, pp. 664–673, 2019.

J. F. Sánchez-Rada and C. A. Iglesias, “CRANK: A Hybrid Model for User and Content Sentiment Classification Using Social Context and Community Detection,” Applied Sciences (Switzerland), vol. 10, no. 5, Mar. 2020, doi: 10.3390/app10051662.

J. Jasmir, S. Nurmaini, R. F. Malik, and D. Z. Abidin, “Text Classification of Cancer Clinical Trials Documents Using Deep Neural Network and Fine Grained Document Clustering,” Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019), vol. 172, 2020, [Online]. Available: https://clinicaltrials.gov.

K. Elshakankery and M. F. Ahmed, “HILATSA: A Hybrid Incremental Learning Approach for Arabic Tweets Sentiment Analysis,” Egyptian Informatics Journal, vol. 20, no. 3, pp. 163–171, Nov. 2019, doi: 10.1016/j.eij.2019.03.002.

M. C. Untoro, “Optimasi MWMOTE Pada Data Tidak Seimbang Menggunakan Complete Linkage,” Jurnal Teknologi dan Sistem Komputer, vol. 9, no. 2, pp. 77–82, 2021, doi: 10.14710/jtsiskom.2021.13748.

A. A. H. de Hond, E. W. Steyerberg, and B. van Calster, “Interpreting Area Under the Receiver Operating Characteristic Curve,” Lancet Digit Health, vol. 4, no. 12, pp. e853–e855, Dec. 2022, doi: 10.1016/S2589-7500(22)00188-1.

J. Bhattacharjee, S. Santra, and A. Deyasi, “Novel Detection of Cancerous Cells Through an Image Segmentation Approach Using Principal Component Analysis,” in Recent Trends in Computational Intelligence Enabled Research, Elsevier, 2021, pp. 171–195. doi: 10.1016/B978-0-12-822844-9.00035-9.

Nurani and Afif, “Perbandingan Kinerja Algoritma Naive Bayes dan C4.5 Untuk Klasifikasi Harga Pangan,” Jurnal Ilmiah Teknik Elektro, vol. 7, no. 1, 2020.

M. S. Alrajak, I. Ernawati, and N. Ika, “Analisis Sentimen Terhadap Pelayanan PT PLN di Jakarta Pada Twitter Dengan Algoritma K-Nearest Neighbor (K-NN),” in Seminar Nasional Mahasiswa Ilmu Komputer dan Aplikasinya, 2020.

H. Annur, “Klasifikasi Masyarakat Miskin Menggunakan Metode Naïve Bayes,” ILKOM Jurnal Ilmiah, vol. 10, Aug. 2018.

I. G. N. E. Susena, M. T. Furqon, and R. C. Wihandika, “Optimasi Parameter Support Vector Machine (SVM) dengan Particle Swarm Optimization (PSO) Untuk Klasifikasi Pendonor Darah dengan Dataset RFMTC,” Jurnal Pembangunan Teknologi dan Informasi Komputer, vol. 2, pp. 7278–7284, 2018, [Online]. Available: http://j-ptiik.ub.ac.id

J. Han, M. Kamber, and J. Pei, Data Mining Concepts and Techniques Third Edition. United States of America: ELSEVIER, 2012, doi: 10.1016/C2009-0-61819-5.

R. A. Haristu and P. H. P. Rosa, “Penerapan metode Random Forest untuk prediksi win ratio pemain player Unknown Battleground,” MEANS (Media Informasi Analisa dan Sistem), vol. 4, no. 2, 2019, doi: 10.54367/means.v4i2.545.

J. Xu, Y. Zhang, and D. Miao, “Three-way Confusion Matrix for Classification: A Measure Driven View,” Inf Sci (N Y), vol. 507, pp. 772–794, Jan. 2020, doi: 10.1016/j.ins.2019.06.064

DOI: https://doi.org/10.37905/jjom.v6i1.23591