Twitter Sentiment Analysis of Long-Acting Reversible Contraceptives (LARC) Methods in Indonesia with Machine Learning Approach

DOI

10.2991/978-2-38476-228-6_15How to use a DOI?

Keywords

LARC; Naive Bayes; Random Forest; Sentiment; Tweet; Twitter

Abstract

High population growth rates still need to be solved in almost all parts of the world. Over the past decade, Indonesia has been ranked fourth in the world regarding population. The family planning program has produced positive results. However, changes in the organizational structure of the institution and changes in local government commitment have led to high disparities in the family planning program. Long-term contraception is considered adequate as the primary need to reduce the rate of population increase. However, the pattern of contraceptive selection in couples of childbearing age in Indonesia is still dominated by non- Long-Acting Reversible Contraceptives (Non-LARC) Methods such as pills and injections. In contrast, the rate of Long-Acting Reversible Contraceptives (LARC) use continues to decline yearly. This study aims to see how the Indonesian people respond to long-term contraceptive products from January 1, 2020, to November 11, 2022. The research used data scraping techniques through the Twitter social media application by grouping sentiment based on negative, positive, and neutral classifications. Each sentiment was analyzed with a word cloud using keywords related to long-term contraceptive methods. Furthermore, classification evaluation is carried on by examining the accuracy of machine learning classification algorithms, specifically naive bayes and random forest.The results stated that the Indonesian people's response to long-term contraceptive methods is still negative, which means that long-term contraceptive products still need to be in demand by the Indonesian people. Based on the accuracy results, the random forest algorithm is very good at classifying tweets, which is 99,33% compared to the naïve bayes algorithm.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Nurul Puspita Sari
AU  - Ahmad Munir
AU  - M. Ramli At
AU  - Madris Iskandar
PY  - 2024
DA  - 2024/06/12
TI  - Twitter Sentiment Analysis of Long-Acting Reversible Contraceptives (LARC) Methods in Indonesia with Machine Learning Approach
BT  - Proceedings of the International Conference on Multidisciplinary Studies (ICoMSi 2023)
PB  - Atlantis Press
SP  - 167
EP  - 185
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-228-6_15
DO  - 10.2991/978-2-38476-228-6_15
ID  - Sari2024
ER  -