Analyzes Health Data By Applying Data Science For Students in The Undergraduate Study Program in Public Health Faculty of Health Hang Tuah University Pekanbaru
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Abstract
Health services are one of the rapidly growing public service sectors currently, resulting in large piles of patient medical record data. This pile of data can provide valuable knowledge if processed in the right way. Data Science is a series of processes for exploring hidden knowledge patterns in large data sets. Data Science can be applied to discover knowledge patterns from patient profiles and health history data. The knowledge gained can be used for analysis and decision making, including to predict the type of disease, determine the pattern of disease spread, and see the effectiveness of treatment. So far, students from the Hang Tuah University Pekanbaru Public Health Study Program have been carrying out the data analysis process using statistics. Community Service Activities aim to enable students to use Data Science as an alternative in analyzing health data. So students can use Data Science to help analyze health data in their research. Data Science techniques discussed include Basic Concepts and Data Science Algorithms. The output of this PkM activity is increasing partner skills, publication in mass media and scientific publications
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Abdillah, N., Susilo, H., & Ihksan, M. (2023). Sosialisasi Pemanfaatan Teknologi Data Mining Untuk Analisis Data Kesehatan Di Klinik Amanah. Jurnal Abdimas Saintika, 5(1), 181–186. https://doi.org/10.30633/JAS.V5I1.1940
Daniel T, L. (2005). DISCOVERING KNOWLEDGE IN DATA An Introduction to Data Mining. In Structure and Bonding (Vol. 134). https://doi.org/10.1007/430_2009_1
Dqlab. (n.d.). Langkah Awal dalam Pemrosesan Data: Data Preprocessing dalam... Retrieved April 9, 2022, from https://www.dqlab.id/langkah-awal-dalam-pemrosesan-data-dalam-data-mining
Flin. (n.d.). Metodologi CRISP-DM Beserta Contoh Kasusnya - Flin Setyadi. Retrieved November 27, 2022, from https://flinsetyadi.com/metodologi-crisp-dm-beserta-contoh-kasusnya/
Ha, J., Kambe, M., & Pe, J. (2012). Data Mining: Concepts and Techniques. Data Mining: Concepts and Techniques, 1–703. https://doi.org/10.1016/C2009-0-61819-5
Habibah, N. N., Nazir, A., Iskandar, I., Syafria, F., Oktavia, L., & Syurfi, I. (2023). Pemodelan Klasifikasi Untuk Menentukan Penyakit Diabetes dengan Faktor Penyebab Menggunakan Decision Tree C4.5 Pada Wanita. Jurnal Sistem Komputer Dan Informatika (JSON), 4(4), 654–661. https://doi.org/10.30865/JSON.V4I4.6202
Han, J. (2011). Han and Kamber: Data Mining---Concepts and Techniques, 2nd ed., Morgan Kaufmann, 2006. https://hanj.cs.illinois.edu/bk3/bk3_slidesindex.htm
Mauritsius, T., & Binsar, F. (2020). Cross-Industry Standard Process for Data Mining (CRISP-DM) – MMSI BINUS University. https://mmsi.binus.ac.id/2020/09/18/cross-industry-standard-process-for-data-mining-crisp-dm/
Rofiqo, N., Windarto, A. P., & Hartama, D. (2018). PENERAPAN CLUSTERING PADA PENDUDUK YANG MEMPUNYAI KELUHAN KESEHATAN DENGAN DATAMINING K-MEANS. KOMIK (Konferensi Nasional Teknologi Informasi Dan Komputer), 2(1). https://doi.org/10.30865/KOMIK.V2I1.929
Zunaidi, M., Nasyuha, A. H., & Sinaga, S. M. (2020). Penerapan Data Mining Untuk Memprediksi Pertumbuhan Jumlah Penderita Human Immunodeficiency Virus (HIV) Menggunakan Metode Multiple Linier Regression (Studi Kasus Dinas Kesehatan Provinsi Sumatera Utara). Jurnal Teknologi Sistem Informasi Dan Sistem Komputer TGD, 3(1), 137–147. https://doi.org/10.53513/JSK.V3I1.205