Lazuardi, “Correlation between Google Trends on dengue fever and national surveillance report in Indonesia,” Glob. Nijman, “CITES-listings, EU eel trade bans and the increase of export of tropical eels out of Indonesia,” Mar. Udin, “Penerapan Internet Sehat Dan Produktif ( Insap ) Bagi Kelompok Remaja Di Lingkungan Sumber Ketangi Kelurahan Wirolegi Kecamatan Sumbersari Kabupaten Jember,” Semin. Abdillah, “Analisis Pemanfaatan Search Engine Optimization dalam Meningkatkan Penjualan Produk UMKM di Pasar Internasional,” J. Fahriannur, “Google Trend untuk Analisa Pasar Bisnis Online & Pemilihan Keywords pada E-Commerce Web,” Semin. Nuti et al., “The use of google trends in health care research: A systematic review,” PLoS One, vol. Eugene Stanley, “Quantifying trading behavior in financial markets using google trends,” Sci. Kristoufek, “Can google trends search queries contribute to risk diversification?,” Sci. Varian, “Predicting the Present with Google Trends,” Econ. Olson et al., “Searching for better flu surveillance? A brief communication arising from Ginsberg et al. Mylonakis, “Google Trends: A Web‐Based Tool for Real‐Time Surveillance of Disease Outbreaks,” Clin. Valleron, “More Diseases Tracked by Using Google Trends,” Clin. Eysenbach, “Infodemiology : The Epidemiology of ( Mis ) information,” vol. Eysenbach, “Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet.,” J. Choi, “Ten years of research change using Google Trends: From the perspective of big data utilizations and applications,” Technol. Finally, it is possible to monitor and make quick surveillance in tuberculosis in Indonesia through Google Trend and we have created a novel set of search terms that can be used as the basis in monitoring other diseases in Indonesia From the correlation analysis, we get a set of proposed effective search terms with the highest score equals to 0.907. The analysis shows that the studied search terms give strong positive relationships between GT trend data and Tuberculosis cases number in Indonesia. The collected data is analyzed using the Pearson correlation. From the data, we design a set of new search terms to take GT trend data. We collect data from the Ministry of Health of Indonesia. We use correlation as the technique to define the relatedness between the real case data and GT results. In this research, we explore, analyze and create a set of the best search terms to be used in utilizing GT for disease surveillance in Indonesia, especially Tuberculosis. Due to limited research in this area and the increasing level of internet penetration in Indonesia, a further study is needed in disease monitoring by utilizing Google Trends. These advantages become great opportunities for disease surveillance agencies in Indonesia to get rapid early disease monitoring. The search digital footprint, such as in Google Trend (GT), forms a large dataset that is suitable to be used as surveillance data and supports early warning systems.
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