Classification and Prediction of Smoking Behavior and Hypertension in the Healthy Family Program with R (Case Study : Bali Provincial Government Department of Health)
Abstract
The Healthy Indonesia Program with the Family Approach aims to improve the quality of human life and is a government program that starts from the family environment. There are 12 indicators marking the health status of a family. The Provincial Government of Bali in implementing the 12 indicators there are two main problems of non-communicable diseases, namely patients with hypertension and smoking behavior. An analysis in the form of classification and prediction is needed to overcome these problems. Classification and prediction is one of the techniques in data mining. Through R, a decision tree can be produced which can be used to help the classification process and produce predictions related to the problem of non-communicable diseases. The decision tree results can be predicted that the dominant hypertension are in the age group classification of 20-40 years and 41-70 years with a probability of 0.14. As for smoking behavior, the highest smoking tendency was obtained in the classification of male sex whose status worked with a probability of 0.44.
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References
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[16] Song Yan-Yan, LU Ying, “Decision tree methods: applications for classification and prediction,” Shanghai Archives of Psychiatry, Vol.27, No. 2, Shanghai, 2015.
[2] Ministry Of Health Republic Of Indonesia. (2012, May 6). Masalah Hipertensi di Indonesia [Online]. Available at: https://www.kemkes.go.id/article/view/1909/masalah-hipertensi-di-indonesia.html [Accessed:17-May-2020].
[3] Ministry Of Health Republic Of Indonesia. (2015, Nov 25). Inilah 4 Bahaya Merokok Bagi Kesehatan Tubuh [Online]. Available at: https://www.kemkes.go.id/development/site/dinas-kesehatan/index.php?cid=1-15112500015&id=inilah-4-bahaya merokok-bagi-kesehatan-tubuh.html [Accessed:17-May-2020].
[4] Kastawan P. W, Wiharta Dewa M, M. Sudarma, “Implementasi Algoritma C5.0 pada Penilaian Kinerja Pegawai Negeri Sipil,“ Majalah Ilmiah Teknologi Elektro, [S.l.], v. 17, n. 3, p. 371-376, dec. 2018, ISSN 2503-2372, 2018.
[5] W. P Dewa Ayu, P. B Kadek Ary, M. Sudarma. “Prediction of Days in Hospital Dengue Fever Patients using K-Nearest Neighbor,” International Journal of Engineering and Emerging Technology, [S.l.], v. 3, n. 1, p. 23-25, july 2018. ISSN 2579-597X.
[6] A.U. Begum. (2019, August). Data Mining Techniques For Big Data. International Journal of Advanced Research in Science, Engineering and Technology” [Online]. Vol.6..Available.:.https://www.researchgate.net/publication/336197482_Data_Mining_Techniques_For_Big_Data_Vol_6_Special_Issue [Accessed:17-May-2020].
[7] S. Putri, B. Adi, M. Sudarma, “The Optimization of Feature Selection Using Genetic Algorithm with Naïve Bayes Classification for Home Improvement Recipients,” International Journal of Engineering and Emerging Technology, [S.l.], v. 3, n. 1, p. 66-70, july 2018, ISSN 2579-597X, 2018.
[8] T. U. Sawant. (2016, May). R: Data Mining Tool And Its Applications. International Journal of Advanced Computer Technology & Management (IJACTM) [Online].. Available at :.https://www.researchgate.net/publication/338853853_R_Data_Mining_Tool_And_Its_Applications [Accessed: 17-May-2020].
[9] Andisana I Putu Gd. S, M. Sudarma, W. I Made Oka, “Pengenalan dan Klasifikasi Citra Tekstil Tradisional Berbasis Web Menggunakan Deteksi Tepi Canny, Local Color Histogram dan Co-Occurrence Matrix,” Majalah Ilmiah Teknologi Elektro, [S.l.], v. 17, n. 3, p. 401-408, dec. 2018, ISSN 2503-2372, 2018.
[10] A. S Mahendra I G. N, Leo Mahadya Suta I. B, M. Sudarma, “Classification of Data Mining with Adaboost Method in Determining Credit Providing for Customers,” International Journal of Engineering and Emerging Technology, [S.l.], v. 4, n. 1, p. 31--36, oct. 2019, ISSN 2579-597X, 2019.
[11] M. Sudarma, Harsemadi I Gede, “Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction,” International Journal of Electrical and Computer Engineering, Vol. 7, Iss. 1, p. 486-495, (Feb 2017), 2017
[12] Harsemadi Gede, M. Sudarma, N. Pramaita, “Implementasi Algoritma K-Nearest Neighbor pada Perangkat Lunak Pengelompokan Musik untuk Menentukan Suasana Hati,” Majalah Ilmiah Teknologi Elektro, [S.l.], v. 16, n. 1, p. 14-20, july 2016, ISSN 2503-2372, 2016
[13] Madni A.H, Anwar Zahid, Shah Ali M, “Data Mining Techniques and Applications – A Decade Review,” COMSATS Institute of Information Technology, Pakistan, 2017.
[14] A. I Made Dwi; A. A Made Pasek, M. Sudarma, “Data Mining, Evaluation, K-means Evaluation of Supporting Work Quality Using K-Means Algorithm,” International Journal of Engineering and Emerging Technology, [S.l.], v. 3, n. 1, p. 52-55, july 2018, ISSN 2579-597X, 2018.
[15] Grabusts Pēteris, Borisovs Arkādijs, Aleksejeva Ludmila, “Decision Tree Creation Methodology Using Propositionalized Attributes,” Information Technology and Management Science, Vol. 19, pp 34-38, De Gruyter Open, Riga Technical University, Latvia, 2016.
[16] Song Yan-Yan, LU Ying, “Decision tree methods: applications for classification and prediction,” Shanghai Archives of Psychiatry, Vol.27, No. 2, Shanghai, 2015.
Published
2020-12-13
How to Cite
UTAMA, I Gede Abi Yodita; PRAMAITA, Nyoman; SUDARMA, Made.
Classification and Prediction of Smoking Behavior and Hypertension in the Healthy Family Program with R (Case Study : Bali Provincial Government Department of Health).
International Journal of Engineering and Emerging Technology, [S.l.], v. 5, n. 2, p. 36-41, dec. 2020.
ISSN 2579-5988.
Available at: <https://ojs.unud.ac.id/index.php/ijeet/article/view/60052>. Date accessed: 21 nov. 2024.
doi: https://doi.org/10.24843/IJEET.2020.v05.i02.p07.
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