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All Journal Journal of Software Engineering, Information and Communication Technology
Mohammad Farrel Nur Rilwanu
Universitas Pendidikan Indonesia - UPI
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Implementation of the K-Neighbors Algorithm to Detect Diabetes Web Based Application Mohammad Farrel Nur Rilwanu; Faris Huwaidi; Hibar Taufikurachman
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 3, No 1: June 2022
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/seict.v3i1.42347

Abstract

Indonesia is the fifth country with the most diabetes sufferers in the world. This is influenced by an unhealthy lifestyle and then coupled with a lack of public awareness to check whether he has diabetes or not. The KNN (K-Nearest Neighbors) algorithm can be used to pr edict whether a person has diabetes. By using a dataset from the Pima Indian Diabetes Database, the data training process was carried out using the KNN algorithm and obtained decent accuracy results using a Jupyter notebook. From the results of the trained data set, it is then exported to be used in website development using the Python programming language. In the web application developed, the user is asked to input data on pregnancies (a person's pregnancy rate as long as he is alive), insulin levels, glucose levels, BMI, blood pressure, family history of diabetes, skin thickness, and age in the form of a slider. The input data is processed by the KNN algorithm to determine the outcome in the form of a positive or negative diabetes result based on the proximity of the new data entered with other data that has been trained.
Co-Authors Faris Huwaidi Hibar Taufikurachman