Prediction The Number of Dengue Hemorrhagic Fever Patients Using Fuzzy Tsukamoto Method at Public Health Service of Purbalingga

Zahra Shofia Hikmawati, Riza Arifudin, Alamsyah Alamsyah

Abstract


DHF (Dengue Hemorrhagic Fever) is still a major health problem in Indonesia. One of the factors that led to an increase in dengue cases is uncertain climate that causes dengue fever is difficult to be predicted. Prediction is an important thing that is used to determine future events by identifying patterns of events in the past. When knowing the events that happen, it will make everyone to make better preparation for everything. This research is aimed at determining the accuracy of Tsukamoto Fuzzy method in the number of dengue patients in Puskesmas Purbalingga. Tsukamoto Fuzzy method can be used for prediction because it has the ability to examine and identify the pattern of historical data. Tsukamoto fuzzy that used to predict the number of dengue fever patients at Puskesmas Purbalingga has several stages. The first stage is the collection of climate data includes precipitation, humidity, water temperature and the data of dengue fever patients in Puskesmas Purbalingga. The next stage is processing the data that has been obtained. The last stage is to make predictions. Based on the results of the implementation by Tsukamoto Fuzzy method in predicting the number of dengue fever patients in Purbalingga for twelve months in 2016, it was obtained a percentage error (MAPE) of 8.13% or had an accuracy rate of 91.87 %. With the small value of MAPE and high accuracy, it shows that the system can predict well.


Keywords


Prediction, Tsukamoto Fuzzy Method, Number of Patients with Hemorrhagic Fever

Full Text:

PDF

References


Pusat Data dan Surveilance Epidemiologi Kementrian Kesehatan Republik Indonesia. 2010. Jendela Epidemiologi. Kementrian Kesehatan Republik Indonesia. Jakarta.

Barrios, J., Pietrus, A., Marrero, A., de Arazoza, H. & Joya, G. 2011. Dengue model described by differential inclusions. Advances in computational intelligence Proceedings, 11th. June 8-10. Torremolinos-Malaga, Spain:540-547.

Whitehead, S. S. & Durbin, A. P. 2010. Prospects and challenges for dengue virus vaccine development. Caister Academic Press: Portland, OR, USA.

Arifin, S., Muslim, M. A., & Sugiman. 2015. Implementasi Logika Fuzzy Mamdani untuk Mendeteksi Kerentanan Daerah Banjir di Semarang Utara. Scientific Journal of Informatics, 2(2):179-192.

Cakara, A. A., Haryanto, H., Kusumaningrum, D. P., & Astuti, S. 2015. Logika Fuzzy Menggunakan Metode Tsukamoto Untuk Prediksi Perilaku Konsumen Di Toko Bangunan, Jurnal Teknik Informatika UDINUS, 4(14):255-265.

Sihombing, D. J. C., Santoso, A. J., & Rahayu, S. 2015. Model Perangkingan Proyek Kontruksi pada Asosiasi Kontraktor Menggunakan Fuzzy AHP. Scientific Journal of Informatics, 2(1):73-81.

Widaningrum, I. 2015. Analisis Hubungan Proses Pembelajaran dengan Kepuasan Mahasiswa Menggunakan Logika Fuzzy. Scientific Journal of Informatics, 2(1):91-98.

Sutoyo, M. N. & Sumpala, A. T. 2015. Penerapan Fuzzy C-Means untuk Deteksi Dini Kemampuan Penalaran Matematis. Scientific Journal of Informatics, 2(2):129-136.

Alamsyah & Muna, I. H. 2016. Metode Fuzzy Inference System untuk Penilaian Kinerja Pegawai Perpustakaan dan Pustakawan. Scientific Journal of Informatics, 3(1):88-98.

Saman, M. & Alamsyah. 2015. Implementasi Fuzzy Inference System Sebagai Sistem Pengambilan Keputusan Pemilihan Program Studi Di Perguruan Tinggi. Journal of Mathematics, 4(1):68-74.

Prasetiyo, B., Baroroh, N., & Rufiyanti, D. E. 2016. Fuzzy Simple Additive Weighting Method in the Decision Making of Human Resource Recruitment. LONTAR KOMPUTER, 7(3):850-857.

Mubin, L. F., Anggraeni, W., & Vinarti, R. A. 2012. Prediksi Jumlah Kunjungan Pasien Rawat Jalan Menggunakan Metode Genetic Fuzzy Systems Studi Kasus: Rumah Sakit Usada Sidoarjo. Jurnal Teknik ITS, 1(4): 82-487.

Rahmadiani, A. & Wiwik, A. 2012. Implementasi Fuzzy Neural Network untuk Memperkirakan Jumlah Kunjungan Pasien Poli Bedah di Rumah Sakit Onkologi. Jurnal Teknik ITS, 1(1):403-407.

Adibah, S. 2016. Analisis Komparasi Metode Tsukamoto dan Sugeno dalam Prediksi Jumlah Siswa Baru. Jurnal Bianglala Informatika, 1(4):1-5

Kusumadewi. 2004. Aplikasi Logika Fuzzy untuk Pendukung Keputusan. Yogyakarta :Graha Ilmu.




DOI: https://doi.org/10.15294/sji.v4i2.10342

Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.