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Sanusi
Universitas Abulyatama
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SEIITR Model for Diabetes Mellitus Distribution in Case of Insulin and Care Factors Nur Fajri; Sanusi; Asmaidi
Jurnal Inotera Vol. 5 No. 2 (2020): July - December 2020
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol5.Iss2.2020.ID113

Abstract

This research is done to learn diabetes mellitus type SEIITR with insulin and care factors. Mathematical model type SEIITR is a mathematical model of diabetes in which the human population is divided into five groups: susceptible humans (Susceptible) S, exposed (Exposed) E, infected I without treatment, infected (Infected) IT with treatment dan recovered (Recovery) R. The SEIITR model has two fixed points, namely, a fixed point without disease and an endemic fixed point. By using basic reproduction numbers (R0), it is found that the fixed point without disease is stable if R0 < 1 and when R0 > 1. Then the fixed point without disease is unstable. The simulation shows the effect of giving insulin to changes in the value of the basic reproduction number. If the effectiveness of β decreases, the basic reproduction number decreases too. Thus, a decrease in the value of this parameter will be able to help reduce the rate of diabetes mellitus in the population.
Utilization of Rapidminer using the K-Means Clustering Algorithm for Classification of Dengue Hemorrhagic Fever (DHF) Spread in Banda Aceh City Sanusi; Juniana Husna
Jurnal Inotera Vol. 5 No. 2 (2020): July - December 2020
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol5.Iss2.2020.ID119

Abstract

Dengue Hemorrhagic Fever (DHF) is still a serious problem in Banda Aceh City. The grouping of dengue disease distribution areas can use data mining techniques through the K-Means Clustering Algorithm by involving several factors that influence it such as population density, rainfall, air humidity and temperature. The purpose of this study is to try to create a distribution cluster group that is included in the high category (C1), medium (C2), and low (C3) in 9 sub-districts in the city of Banda Aceh. The data used in this study are secondary data during the period 2010 to 2017, which includes population density data obtained from the Banda Aceh City BPS office, rainfall, humidity, temperature were obtained from the BMKG Indrapuri Aceh Besar office and data on dengue cases were obtained from the Banda Aceh City Health Office. The results showed that up to 4 iterations of K-Means Clustering was good enough for the classification of dengue case data. The high cluster group (C1) is Baiturrahman, Kuta Alam and Syiah Kuala sub-districts, the medium cluster group (C2) is Jaya Baru, Banda Raya and Ulee Kareng sub-districts, then the low cluster group (C3) is Meuraxa and Kuta Raja.
Co-Authors Asmaidi Juniana Husna Nur Fajri