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All Journal Jurnal Ilmu Dasar Jurnal Gaussian KADIKMA BERKALA SAINSTEK Prosiding Seminar Matematika dan Pendidikan Matematik UNEJ e-Proceeding International Conference on Mathematics and Science Education of Universitas Pendidikan Indonesia Majalah Ilmiah Matematika dan Statistika (MIMS) JURNAL BIOSHELL Pancaran Pendidikan
I Made Tirta
Program Studi Magister Matematika, FMIPA, Universitas Jember
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Control & Systems Engineering Education Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Physics Other

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Analysis of the Death Risk of Covid-19 Patients Using Extended Cox model Cyndy Romarizka; Mohamat Fatekurohman; I Made Tirta
Jurnal ILMU DASAR Vol 24 No 1 (2023)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jid.v24i1.33074

Abstract

Globally, in 2021, there were 170,051,718 COVID-19 cases and 3,540,437 patients who died. The high mortality rate of patients infected with COVID-19 gives an idea to research the analysis of the factors that influence the death of Covid-19 patients. The data used in this study is data on Covid-19 patients obtained from the Mexican Government, with response variables namely time and status and predictor variables, namely patient laboratory results in the form of a history of illness that has been suffered by Covid-19 patients so that they adopt the extended model to evaluate the data. The data in this study are heterogeneous and large in number so that data clustering is carried out into 3 clusters, namely low emergency clusters, medium emergency clusters and high emergency clusters using K-means clustering. Because the study could not find the factors that influence the death of Covid-19 patients, two clusters were chosen, namely the medium emergency cluster and the high emergency cluster. So that the factors that influence the death of Covid-19 patients in the medium emergency cluster are sorted by the highest hazard ratio, namely pneumonia, old age, renal chronic, diabetes, Chronic Obstructive Pulmonary Disease (COPD), immune system, hypertension, cardiovascular, obesity, gender, and asthma. In the high emergency cluster, sorted by the highest hazard ratio is the immune system, renal chronic, cardiovascular, COPD, tobacco, hypertension, obesity, gender, and pneumonia.
PENERAPAN MODEL LEAST SQUARE SUPPORT VECTOR MACHINE (LSSVM) UNTUK PERAMALAN KASUS COVID-19 DI INDONESIA Lutfi Ardining Tyas; I Made Tirta; Yuliani Setia Dewi
Jurnal Gaussian Vol 12, No 2 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.2.304-313

Abstract

Forecasting is about predicting the future based on historical data and any information that might affects the forecasts. This article applies the LSSVM model to forecast Covid-19 cases in Indonesia. The purpose of this study is to find out how the LSSVM model applied and the model performances for forecasting Covid-19 cases in Indonesia, using time series data and the factors that influence it, as input features. The factor data used in this study are mobility data and daily fully vaccinated data. The research has three main objectives; first, calculate the correlation between confirmed cases data and past data (lag) of mobility and vaccination. Second, is the selection of input features based on the highest correlation coefficient value of each variable. Third, do LSSVM modeling and Covid-19 case forecasting with the optimal model. RBF kernel and grid-search algorithm with 10-fold cross-validation are used to tune model parameters. The results show that the LSSVM model provides good performance for Covid-19 forecasting and the optimal LSSVM model for forecasting Covid-19 cases in Indonesia is using time lag 14 for the mobility factor and time lag 24 for the vaccination factor.
Co-Authors A As’ad Agus Isdarmawan Agustina Pradjaningsih Aminatus Sholiha Anak Agung Gede Sugianthara Andina Ishmah Almira, Andina Ishmah Budi Lestari Budi Lestari Cyndy Romarizka Dian Anggareni Dian Anggraeni Dian Anggraeni Erfan Syahuri Fikriana Nur Istiqomah Habibi, Azwar Haeruddin Haeruddin Holipah, Holipah Indriasih Yanuwijaya Istiqomah, Fikriana Nur Kasmuri Kasmuri Kasmuri Kasmuri, Kasmuri Lutfi Ardining Tyas M Fatekurrahman M. Isnaini Marino Marino, Marino Miftahul Ulum Mohamad Fatekurohman Mutiarasari, Yusna Nawal Ika Susanti Nawal Ika Susanti, Nawal Ika Rizka Dwi Hidayati T C P Utama Tika Clarinta Putri Utama Yeni Rismawati Yuliani Setia Dewi