Implementation of Fuzzy Mamdani Method to Classify Public Health Level in North Sumatra Province


  • Sagita Charolina Sihombing Institut Bisnis dan Teknologi Pelita Indonesia
  • Agus Dahlia Universitas Islam Riau



Fuzzy Mamdani method, public health level, North Sumatra Province


Health is one of the most important fields aspects in people’s lives. Public health is influenced by several factors, such as environmental factors and lifestyle. Every year, the government makes a program to improve people’s health and welfare. Therefore, the government needs to obtain information about the classification of public health levels to monitor the progress of the programs that have been carried out. The research created an interface application to classify the level of public health in North Sumatra Province. The research classified the public health level using data from Badan Pusat Statistik (BPS) in 2018. Next, the application was made using the Matlab 2013b GUI. Classification of the level of public health was carried out using the Fuzzy Mamdani method that generated public health classification levels based on the inputted indicators. Then, the level of health classification was divided into four parts: low, lower middle, upper middle, and high. The result shows that for the 2018 data, some cities/ districts are still at low health levels. However, many cities or districts have good health levels. The final result can be used as a reference for the government to analyze which cities/districts should be paid attention to improve their health and welfare.


Plum Analytics

Author Biographies

Sagita Charolina Sihombing, Institut Bisnis dan Teknologi Pelita Indonesia

Management Department, Faculty of Bisnis

Agus Dahlia, Universitas Islam Riau

Teknik Perminyakan, Faculty of Engineering


Abd Halim, S., & Lazim, S. M. (2021). Development of GUI for Malaysian herbs plant image identification. Journal of Physics: Conference Series, 1988, 1−11.

Ahmad Shukri, F. A., & Isa, Z. (2021). Experts’ judgment-based Mamdani-type decision system for risk assessment. Mathematical Problems in Engineering, 2021, 1−13.

Badan Pusat Statistik Provinsi Sumatera Utara. (2018). Provinsi Sumatera Utara dalam angka 2018. Retrieved from

Batubara, S. (2017). Analisis perbandingan metode fuzzy Mamdani dan fuzzy Sugeno untuk penentuan kualitas cor beton instan. IT Journal Research & Development, 2(1), 1−11.

Chusyairi, A., Saputra, P. R. N., & Zaenudin, E. (2021). Fuzzy C-Means clustering algorithm for grouping health care centers on diarrhea disease. International Journal of Artificial Intelegence Research, 5(1), 35−43.

Dinas Kesehatan Provinsi Sumatera Utara. (2021). Tekan angka kematian ibu dan bayi baru lahir, Pemprov Sumut jalin kerja sama dengan USAID. Retrieved from

Egaji, O. A., Griffiths, A., Hasan, M. S., & Yu, H. N. (2015). A comparison of Mamdani and Sugeno fuzzy based packet scheduler for MANET with a realistic wireless propagation model. International Journal of Automation and Computing, 12, 1−13.

Mugirahayu, A. S., Linawati, L., & Setiawan, A. (2021). Penentuan status kewaspadaan COVID-19 pada suatu wilayah menggunakan metode Fuzzy Inference System (FIS) Mamdani. Jurnal Sains dan Edukasi Sains, 4(1), 28−39.

Pamuji, A. (2016). Assessment the method of fuzzy logic to determine the quality of service expedition in Jabodetabek area. Scientific Journal of Informatics, 3(2), 11−20.

Permana, Y., & Lelah. (2020). Pengklasifikasian tingkat kesejahteraan keluarga di Desa Citamiang dengan penerapan logika fuzzy model Tahani. RABIT: Jurnal Teknologi dan Sistem Informasi Univrab, 5(2), 97−107.

Putri, S. N., & Saputro, D. R. S. (2021). Construction fuzzy logic with curve shoulder in inference system Mamdani. Journal of Physics: Conference Series, 1776, 1−8.

Rahanyamtel, R., Nurjazuli, & Sulistiyani. (2019). Faktor lingkungan dan praktik masyarakat berkaitan dengan kejadian filariasis di Kabupaten Semarang. Jurnal Kesehatan Lingkungan Indonesia, 18(1), 8–11.

Sari, F., Desyanti, Radillah, T., Nurjannah, S., Julimar, & Pakpahan, J. Y. (2021). Examining child obesity risk level using fuzzy inference system. International Journal of Public Health Science (IJPHS), 10(3), 679–687.

Wijirahayu, S., & Sukesi, T. W. (2019). Hubungan kondisi lingkungan fisik dengan kejadian demam berdarah dengue di wilayah kerja Puskesmas Kalasan Kabupaten Sleman. Jurnal Kesehatan Lingkungan Indonesia, 18(1), 19–24.

Zulaikhah, S. T., Ratnawati, R., Sulastri, N., Nurkhikmah, E., & Lestari, N. D. (2019). Hubungan pengetahuan, perilaku dan lingkungan rumah dengan kejadian transmisi tuberkulosis paru di wilayah kerja Puskesmas Bandarharjo Semarang. Jurnal Kesehatan Lingkungan Indonesia, 18(2), 81–88.






Abstract 69  .
PDF downloaded 53  .