Exploratory Data Analysis and Machine Learning Algorithms to Classifying Stroke Disease

Riyantoko, Prismahardi Aji; Fahrudin, Tresna Maulana; Hindrayani, Kartika Maulida; Idhom, Mohammad

doi:10.33005/ijconsist.v2i02.49

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Jumlah air bersih yang kecil menyebabkan susahnya penduduk mengakses air bersih. berdasarkan data dari WHO sebanyak 663 juta penduduk kesusahan untuk mengakses air bersih [3].…”

Section: Pendahuluanunclassified

Klasifikasi Kualitas Air Minum menggunakan Penerapan Algoritma Machine Learning dengan Pendekatan Supervised Learning

Savitri,

Nursalim

2023

diophantine

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The need for the provision and service of clean water from time to time is increasing which is sometimes not matched by the ability and knowledge of clean water. The majority of people still do not know whether water is suitable for consumption or not. The quality of drinking water can be distinguished based on the mineral parameters contained in the water. This article will explain the classification of water sample data by applying a Machine Learning Algorithm, which includes modeling with Logistic Regression, Support Vector Machine (SVM), Random Forest Classifier, K- Nearest Neighbor(KNN), XGBoost Classifier. Classification models produce varying degrees of accuracy. The highest accuracy is obtained in the Random Forest Classifier model with an accuracy rate of 78%. Analysis of drinking water quality with machine learning algorithms is very easy to understand, because the results of this study produce very simple results so that they are easy to understand

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“…Jumlah air bersih yang kecil menyebabkan susahnya penduduk mengakses air bersih. berdasarkan data dari WHO sebanyak 663 juta penduduk kesusahan untuk mengakses air bersih [3].…”

Section: Pendahuluanunclassified

Klasifikasi Kualitas Air Minum menggunakan Penerapan Algoritma Machine Learning dengan Pendekatan Supervised Learning

Savitri,

Nursalim

2023

diophantine

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“…For the coming years, the World Water Assessment Programme (WWAP), overseen by UNESCO, has planned for clean water conditions. 85% of clean water becomes waste under certain conditions, to meet their daily needs, each person uses one hundred liters of water every day (Riyantoko et al, 2021). There are many efforts to maintain water quality, such as conducting checks to determine if there are bacteria or diseases in the water, so that preventive measures can be taken if the water quality declines (Hartanti & Pradana, 2023).…”

Section: Introductionmentioning

confidence: 99%

Classification of Drinking Water Potability With Artificial Neural Network Algorithm

Darmawan,

Muhammad Fatchan,

Andri Firmansyah

2024

IJIST

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Having safe water for consumption is essential for public health in every region. However, water quality is declining in some places, especially to meet human needs for drinking water. There are many efforts to maintain water potability, such as checking to see if there are bacteria or diseases in the water. This research classifies water potability using the Artificial Neural Network method, a technique in the field of machine learning. This research classifies water quality using a python library to analyze data and perform classification. Data is processed through stages such as data cleaning and data division into training and testing. In testing, the data is divided into 20% for testing and 80% for training. The results of the ANN algorithm show 70% accuracy. in conclusion, the ANN model has moderate performance in classifying the feasibility of drinking water. Model improvement is needed to improve accuracy and prediction, including the use of larger and more diverse datasets.

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Exploratory Data Analysis and Machine Learning Algorithms to Classifying Stroke Disease

Cited by 2 publications

References 5 publications

Klasifikasi Kualitas Air Minum menggunakan Penerapan Algoritma Machine Learning dengan Pendekatan Supervised Learning

Klasifikasi Kualitas Air Minum menggunakan Penerapan Algoritma Machine Learning dengan Pendekatan Supervised Learning

Classification of Drinking Water Potability With Artificial Neural Network Algorithm

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