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Amelia, Putri Juli
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Computer Science & IT

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Prediksi Jumlah Pasien Covid-19 Dengan Menggunakan Klasifikasi Algoritma Machine Learning Aidia, Aidia Khoiriyah Firdausy; Amelia, Putri Juli; Setyaning Nastiti, Vina Rahmayanti
SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v5i2.1163

Abstract

Corona virus or servere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a disease that results in the occurrence of mild to moderate respiratory tract infections. Positive cases of Covid-19 in Indonesia were first detected on March 2, 2020 and continue until 2022. The additional number of deaths caused by COVID-19 has also increased. Therefore, the author is interested in making a predictive model of the cumulative number of COVID-19 patients who died in Indonesia. Therefore, in this study is how to predict the number of patients who die from COVID-19 in Indonesia by creating an appropriate accuracy model to help estimate the number of deaths associated with COVID-19 in Indonesia and assist the government in dealing with cases of new variants of COVID-19. In this study, the authors used the Decision Tree model  using entropy criteria as well as Information Gain and Random Forest which resulted in accuracy rates of 91.83% (Decission Tree) and 73.80% (Random Forest). The results, explain that the model used is good. The more the R-squared error value is close to 1, the better the model used will be
Perbandingan Algoritma Klasifikasi Data Mining Untuk Prediksi Penyakit Stroke Azhar, Yufis; Firdausy, Aidia Khoiriyah; Amelia, Putri Juli
SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v5i2.1222

Abstract

Data mining is often called knowledge Discovery in Database (KDD). Data mining is usually used to improve future decision making based on information obtained from the past. For example for prediction, estimation, association, clustering, and description. Stroke is the second most deadly disease in the world according to WHO. The sufferer has an injury to the nervous system. Because of this, health experts, especially in the field of nursing, need special attention. Currently, the development of the Industrial Revolution Era 4.0 is collaborating in the fieldsof technology and health science so that it becomes something useful by using Machine Learning. There are so many benefits that are used in predicting several diseases that can be anticipated. In this study the dataset is dividedinto 2 parts, namely training data and testing data using split validation. Based on the results of the test that have been carried out in this study, the algorithm that has the highest accuracyvalue on balanced data is Logistic Regression with an accuracy rate of 75.65%, while for unbalanced data, the algorithm that has the highest accuracy results is Logistic Regression, Random Forest, SVM, and KNN with an accuracy rate of 98.63%. This testing process is carried out to identify stroke with data mining algorithms
Co-Authors Aidia, Aidia Khoiriyah Firdausy Firdausy, Aidia Khoiriyah Setyaning Nastiti, Vina Rahmayanti Yufis Azhar