The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments
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....
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| Format: | Article |
| Language: | English |
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Department of Mathematics, Universitas Negeri Gorontalo
2024-02-01
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| Series: | Jambura Journal of Mathematics |
| Subjects: | |
| Online Access: | https://ejurnal.ung.ac.id/index.php/jjom/article/view/23591 |
| _version_ | 1797291996060057600 |
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| author | Amimah Shabrina Putri Prasmono Mujiati Dwi Kartikasari |
| author_facet | Amimah Shabrina Putri Prasmono Mujiati Dwi Kartikasari |
| author_sort | Amimah Shabrina Putri Prasmono |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-07T19:45:50Z |
| format | Article |
| id | doaj.art-d38443ed771247fabcb0eb5b1f9b2070 |
| institution | Directory Open Access Journal |
| issn | 2654-5616 2656-1344 |
| language | English |
| last_indexed | 2024-03-07T19:45:50Z |
| publishDate | 2024-02-01 |
| publisher | Department of Mathematics, Universitas Negeri Gorontalo |
| record_format | Article |
| series | Jambura Journal of Mathematics |
| spelling | doaj.art-d38443ed771247fabcb0eb5b1f9b20702024-02-29T02:09:22ZengDepartment of Mathematics, Universitas Negeri GorontaloJambura Journal of Mathematics2654-56162656-13442024-02-0161293810.37905/jjom.v6i1.235917278The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video CommentsAmimah Shabrina Putri Prasmono0Mujiati Dwi Kartikasari1Universitas Islam IndonesiaUniversitas Islam IndonesiaChildfree 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%.https://ejurnal.ung.ac.id/index.php/jjom/article/view/23591childfreesentiments analysislexiconsupport vector machinerandom forest |
| spellingShingle | Amimah Shabrina Putri Prasmono Mujiati Dwi Kartikasari The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments Jambura Journal of Mathematics childfree sentiments analysis lexicon support vector machine random forest |
| title | The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments |
| title_full | The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments |
| title_fullStr | The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments |
| title_full_unstemmed | The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments |
| title_short | The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments |
| title_sort | childfree phenomenon in indonesia an analysis of sentiments on youtube video comments |
| topic | childfree sentiments analysis lexicon support vector machine random forest |
| url | https://ejurnal.ung.ac.id/index.php/jjom/article/view/23591 |
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