Data Mining untuk Prediksi Status Pasien Covid-19 dengan Pengklasifikasi Naïve Bayes

Liliana, Dewi Yanti; Maulana, Hata; Setiawan, Agus

doi:10.32722/multinetics.v7i1.3786

Cited by 8 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Data mining is used to find patterns in large sets of raw data. Data Mining applies Machine Learning techniques to draw knowledge from data (Liliana et al, 2021). Data mining has several stages: data cleansing, data integration, data transformation, data mining techniques, pattern evaluation, and knowledge presentation.…”

Section: Literature Review Data Miningmentioning

confidence: 99%

Classification of Covid-19 vaccine data screening with Naive Bayes algorithm using Knowledge Discovery in database method

Alam

Resmi

Masripah

2022

CNAHPC

View full text Add to dashboard Cite

Acute Respiratory Syndrome Coronavirus-2 (SARS-Cov-2) known as covid-19 was detected and caused a very large number of deaths due to a mysterious respiratory disease. With the death toll continuing to rise, the government was forced to take swift action to break the chain of spread and reduce the number of deaths by taking vaccinations. An adequate vaccine against Covid-19 is expected to vaccinate at least 70% of the population. Therefore, this study was carried out as a step to help break the chain of the spread of the Covid-19 virus, by classifying the Covid-19 vaccine screening data. The research method applied in this study is the Knowledge Discovery in Database (KDD) method, in which there are several processes, namely selection, pre-processing, transformation, data mining, and evaluation. The application of the Naive Bayes method is expected to be able to classify Covid-19 vaccine screening data with vaccine class values, no, and delay. The results of the research on the classification of the Naive Bayes method show that there are 959 data with Vaccine data 695, No 200, and Delay 64. Processed using the Rapidminer application, the accuracy is 96.56%, Precision is 92.46%, and Recall is 92.13%.

show abstract

Section: Literature Review Data Miningmentioning

confidence: 99%

Classification of Covid-19 vaccine data screening with Naive Bayes algorithm using Knowledge Discovery in database method

Alam

Resmi

Masripah

2022

CNAHPC

View full text Add to dashboard Cite

show abstract

“…Pada masa sekarang, Covid-19 merupakan pandemi global yang membutuhkan respons dunia yang terkoordinasi di semua sistem kesehatan dan perawatan kesehatan nasional. Corona virus disease 2019 atau sering disebut dengan Covid-19 ialah suatu penyakit infeksi yang menyerang saluran pernafasan akut [1]. Covid-19 merupakan penyakit yang menyebabkan peneumonia yang umumnya dapat dilihat dari pasien yang terjangkit .…”

Section: Pendahuluanunclassified

Nave Bayes Optimization with PSO for Predicting ICU Needs for Covid-19 Patients

Lestari

Harifal²,

Taslim³

et al. 2022

SISTEMASI

View full text Add to dashboard Cite

Covid-19 merupakan pandemi global yang membutuhkan respons dunia yang terkoordinasi di semua sistem kesehatan dan perawatan kesehatan nasional. Mengidentifikasi pasien yang berisiko tinggi terjangkit virus Covid-19 adalah hal penting untuk meningkatkan kewaspadaan sebelum pasien terinfeksi lebih lanjut oleh virus Covid-19 yang dapat menyebabkan penyakit pernapasan parah sehingga membutuhkan perawatan khusus di intensive care units (ICU). Penelitian ini bertujuan untuk memprediksi kebutuhan ICU pada pasien terjangkit virus Covid-19. Hasil nilai dari prediksi kebutuhan ICU dipergunakan sebagai bahan acuan rumah sakit untuk memenuhi kebutuhan ICU pada pasien terjangkit Covid-19 sehingga dapat meningkatkan persediaan ICU. Prediksi tersebut akan dilakukan dengan metode algoritma Naïve Bayes dengan dilakukan optimasi menggunakan algoritma PSO. Berdasarkan hasil penelitian, maka diperoleh population size 20 dengan nilai akurasi algoritma NBC sebesar 87,03%, population size 40 dengan nilai akurasi 87,28, population size 60 didapatkan hasil akurasi 87,13%, population size 80 dengan nilai akurasi 87,16% dan population size 100 didapatkan hasil 87,26% sehingga disetiap population mengalami peningkatan nilai akurasinya.

show abstract

“…Covid-19 adalah virus baru yang muncul di akhir tahun 2019 di kota Wuhan,Tiongkok. Virus ini terus berkembang hingga menyebar ke seluruh dunia pada tahun 2020 [1][2][3] [4]. World Health Organization (WHO) menginformasikan bahwa Covid-19 sebagai pandemi pada 11 Maret 2020 [5] [6].…”

Section: Pendahuluanunclassified

“…World Health Organization (WHO) menginformasikan bahwa Covid-19 sebagai pandemi pada 11 Maret 2020 [5] [6]. Virus ini menyebabkan infeksi pada paru-paru yang berdampak fatal berupa kematian [1][7] [8][9] [10]. Virus ini dapat diderita oleh siapa saja.…”

Section: Pendahuluanunclassified

See 1 more Smart Citation

Prediksi Status Penanganan Pasien Covid-19 dengan Algoritma Naïve Bayes Classifier di Provinsi Riau

Pramana

Mustakim

2021

json

View full text Add to dashboard Cite

Covid-19 is a new virus that emerged at the end of 2019 in Wuhan city, China. This virus continues to grow until it spreads to various countries in the world. As a result, there has been a large accumulation of Covid-19 patients in every hospital in every country affected by Covid-19. Covid-19 patients receiving treatment in hospitals have different conditions and severity, this of course affects the different mechanism for handling patients. Therefore, technological support is needed to help classify the treatment of patients so that they can be concentrated on patients who can be treated with isoman treatment or must be referred to hospital. This research was conducted to build a model based on a dataset of patients infected with Covid-19 using the Naive Bayes Classifier algorithm. The model built can predict the treatment status of patients based on age and gender who have the highest probability of being treated in an isoman way or having to be referred to hosspital. Data used is applied using Rapidminer with validation used is spill validation with the ratio of training data is 70% and test data is 30%. The results of this research indicate classification using the Naive Bayes Classifier algorithm has a high level of accuracy in classifying patient status data, rately 83.33%.

show abstract

Data Mining untuk Prediksi Status Pasien Covid-19 dengan Pengklasifikasi Naïve Bayes

Cited by 8 publications

References 13 publications

Classification of Covid-19 vaccine data screening with Naive Bayes algorithm using Knowledge Discovery in database method

Classification of Covid-19 vaccine data screening with Naive Bayes algorithm using Knowledge Discovery in database method

Nave Bayes Optimization with PSO for Predicting ICU Needs for Covid-19 Patients

Prediksi Status Penanganan Pasien Covid-19 dengan Algoritma Naïve Bayes Classifier di Provinsi Riau

Contact Info

Product

Resources

About