TEXT MINING UNTUK FORECASTING KEBUTUHAN APD DI KOTA PADANG
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CCOHS. (2018). Personal Protective Equipment (PPE). Canadian Centre for Occupational Helath and Safety. https://www.ccohs.ca/teach_tools/phys_hazards/ppe.html
Chaurasia, V., & Pal, S. (2020). Application of machine learning time series analysis for prediction COVID-19 pandemic. Research on Biomedical Engineering, 1–13. https://doi.org/10.1007/s42600-020-00105-4
Eska, J. (2016). Penerapan Data Mining Untuk Prekdiksi Penjualan Wallpaper Menggunakan Algoritma C4.5. JURTEKSI (Jurnal Teknologi Dan Sistem Informasi), 2(2), 9–13.
Hartanto. (2017). Text Mining dan Sentimen Analisis Twitter pada Gerakan LGBT. INTUISI: Jurnal Psikologi Ilmiah, 9(1), 18–25.
Haryati, S., Sudarsono, A., & Suryana, E. (2015). Implementasi Data Mining Untuk Memprediksi Masa Studi Mahasiswa Menggunakan Algoritma C4.5 (Studi Kasus: Universitas Dehasen Bengkulu). Jurnal Media Infotama, 11(2), 130–138.
Honda, H., & Iwata, K. (2016). Personal protective equipment and improving compliance among healthcare workers in high-risk settings. Current Opinion in Infectious Diseases, 29(4), 400–406. https://doi.org/10.1097/QCO.0000000000000280
Lingga, R. D., Fatichah, C., & Purwitasari, D. (2017). Deteksi Gempa Berdasarkan Data Twitter Menggunakan Decision Tree, Random Forest, dan SVM. Jurnal Teknik ITS, 6(1), A159–A162. https://doi.org/10.12962/j23373539.v6i1.22037
Mabrur, A. G., & Lubis, R. (2012). Penerapan Data Mining Untuk Memprediksi Kriteria Nasabah Kredit. Jurnal Komputer Dan Informatika (KOMPUTA), 1(1), 53–58.
Meilina, P. (2015). Penerapan Data Mining dengan Metode Klasifikasi Menggunakan Decision Tree dan Regresi. Jurnal Teknologi, 7(1), 11–20.
Mihuandayani, Feriyanto, E., Syarham, & Kusrini. (2018). Opinion Mining Pada Komentar Twitter E-Ktp Menggunakan Naive Bayes Classifier. Seminar Nasional Teknologi Informasi Dan Multimedia, 6(1), 25–29.
Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., & Vasilakis, C. (2021). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research, 290(1), 99–115. https://doi.org/10.1016/j.ejor.2020.08.001
Sari, D. I., Wati, Y. F., & Widiastuti, W. (2020). Analisis Sentimen dan Klasifikasi Tweets Berbahasa Indonesia terhadap Transportasi Umum MRT Jakarta menggunakan Naïve Bayes Classifier. Jurnal Ilmiah Informatika Komputer, 25(1), 64–75.
Sun, J., Chen, X., Zhang, Z., Lai, S., Zhao, B., Liu, H., Wang, S., Huan, W., Zhao, R., Ng, M. T. A., & Zheng, Y. (2020). Forecasting the long-term trend of COVID-19 epidemic using a dynamic model. Scientific Reports, 10, 1–10. https://doi.org/10.1038/s41598-020-78084-w
WHO. (2018). Preferred product characteristics for personal protective equipment for the health worker on the frontline responding to viral hemorrhagic fevers in tropical climates. In World Health Organisation. http://apps.who.int/iris/bitstream/handle/10665/272691/9789241514156-eng.pdf
Widyatmoko, H., Honggowibowo, A. S., & Retnowati, N. D. (2012). Implementasi data Mining untuk Meramalkan Penjualan di Minimarket Idola Jl. Pati-Tambakromo Km 2 dengan Metode Time Series. Compiler, 1(2). https://doi.org/10.28989/compiler.v1i2.15
DOI: http://dx.doi.org/10.36275/stsp.v22i1.445
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