Nowadays, social media has become very accessible to the public. The amount of information shared makes social media a very potential source for gathering public opinion. One real example is looking at people’s acceptance and rejection of vaccinations. Realizing this, FPH UI doctoral candidate, Agustina Setyaningsih, wrote a dissertation entitled, “Model of Early Detection of Vaccine Rejection Through Social Media Analytics”. The preparation of his dissertation was supervised by Prof. dr. Kemal N. Siregar, M.A., Ph.D., as promoter and Dr. Drs. Tris Eryando, M.A., and Dr. Ir. Heru Purnomo Ipung, M.Eng., as co-promoter.
The research began by conducting a systematic literature review to identify negative sentiments related to the issue of vaccine rejection with social network analysis. This was then followed by data collection from Facebook, YouTube, Instagram, and Twitter. Of the 16 variables, 7 (seven) of them were obtained which were needed and an assessment of the quality of the data had been carried out, so that the research could be continued. Agustina then selects a model from the machine learning algorithm to find the best model and perform data training. After that, there are still 4 (four) stages that must be passed before finally obtaining a threat level value related to the issue of vaccine rejection based on the calculation formula.
As a result, the negative opinion variable related to vaccine efficacy is the highest predictor variable for vaccine rejection. Other variables that were also studied were social and environmental perceptions, general behavior, perceptions of side effects, health workers’ beliefs, government beliefs, and vaccine misinformation. Overall, there are 36.8% negative opinions, 34.1% positive opinions regarding the vaccine. This figure increased in August-September 2018, to be precise when the second phase of measles immunization was being carried out outside Java. Apart from analyzing sentiment, Agustina also looked at the opinion leaders who discussed the issue of vaccine rejection. It doesn’t stop there, this Vaccine Rejection Early Detection Model can also be implemented in practice through the AGUSTINA application, an acronym for Artificial Intelligence for User Tool to Measuring Situational Awareness. The hope is that this application can be used by health providers to detect early vaccine rejection.
After studying for 4 (four) years, Agustina had the opportunity to hold an open doctoral promotion session on Wednesday, 12 July 2023. The examiner team consisted of Prof. Dr. dr. Anhari Achadi, S.K.M., Sc.D., as chairman; Elizabeth Jane Soepardi, M.P.H., D.Sc.; Dr. Rahma Fitriawati; and Dr. Mujiono Sadikin, MT. CISA., CGEIT., IPU. As a result, Agustina passed with a very satisfactory graduation and GPA of 3.78.
Agustina is the 21st Doctor who graduated in 2023 and the 375th Doctor who has graduated from FPH UI. (BK)
