Prediksi Penyakit Diabetes Mellitus Tipe I dan Tipe II Menggunakan Metode KNN di Klinik Dharma Husada

Richa Nanda Fitria; Wahyu Sugianto; Amalia Cemara Nur’aidha

doi:10.57213/antigen.v2i3.303

Authors

Richa Nanda Fitria Universitas PGRI Yogyakarta
Wahyu Sugianto Universitas PGRI Yogyakarta
Amalia Cemara Nur’aidha Universitas PGRI Yogyakarta

DOI:

https://doi.org/10.57213/antigen.v2i3.303

Keywords:

Diabetes Mellitus, KNN, Dataset

Abstract

Diabetes Mellitus (DM) is a metabolic disorder characterized by high blood sugar levels due to insulin deficiency. Factors causing Diabetes Mellitus (DM) are lifestyle which includes diet, lack of exercise, monitoring blood sugar, and medication. Most people do not realize that they have DM and only find out when they experience severe symptoms. To avoid this, the k-Nearest Neighbor (KNN) method can be used to predict the possibility of developing diabetes. The aim of this research is to classify diabetes mellitus using the K-Nearest Neighbor (KNN) method and make people more aware of the risk of disease through healthy lifestyle changes. Data received from the Dharma Husada Clinic is categorized based on researchers' needs, including age, BMI, insulin, skin thickness, glucose, diabetes, genetics, and insulin. This research was carried out in three main steps: dataset input, preprocessing, and evaluation. The first stage is data analysis which begins by entering a dataset to train and test the model, where each data element has certain characteristics (attributes) and classes. Preprocessing steps include training data generation and data cleaning, which includes sanitization, lowercase, normalization, stopwords, stemming, and tokenizing. The final step is evaluating. Evaluation includes building an evaluation model and measuring the level of accuracy, building a predictive model, and saving the model. This research shows that the K-Nearest Neighbor (KNN) method can be used to classify diabetes mellitus (DM), but especially in a small dataset consisting of 245 dates and 8 attributes it is not accurate for patients aged 30 years. . A k value that is too small can cause overfitting, and a k value that is too large can cause underfitting. However, if the amount of data is small, the choice of k can have a large impact.

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