Analisis survival adalah salah satu analisis data statistika yang bertujuan untuk melakukan analisis fungsi survival (waktu sembuh). Pertanggal 30 Juli 2020 sebanyak 108.376 orang positif covid-19, dengan 65.907 pasien dinyatakan sembuh dan 5.131 pasien meninggal dunia di Indonesia. Sementara itu di kabupaten Banyuwangi tercatat sebanyak 56 orang positif Covid-19 dengan 44 pasien sembuh dan 2 pasien meninggal. Tingginya angka kematian tersebut menunjukkan bahwa virus covid-19 ini sangat berbahaya bagi manusia. Namun demikian angka kesembuhan virus ini juga cukup tinggi. Untuk itu dilakukan analisis survival waktu sembuh pasien covid-19 di Banyuwangi dengan uji Kaplan-Meier sebagai penghitung estimasi fungsi survival (waktu sembuh) pasien Covid-19, uji Log-Rank untuk menguji adanya perbedaan fungsi survival (S(t)) waktu sembuh pasien Covid-19 pada dua kelompok. Data yang digunakan adalah data pasien Covid-19 di Kabupaten Banyuwangi sejak Mei sampai 27 Juli 2020. Kesimpulan yang diperoleh adalah median fungsi survival waktu sembuh pasien Covid-19 di Kabupaten Banyuwangi adalah 16 hari perawatan, dengan fungsi survival waktu sembuh pasien laki-laki memiliki median 15,5 hari perawatan dan fungsi survival waktu sembuh pasien perempuan memiliki median 13 hari perawatan. Namun, berdasarkan uji Log-Rank dengan α = 0,05, disimpulkan tidak terdapat perbedaan yang signifikan pada lama waktu sembuh Covid-19 antara pasien perempuan dan pasien laki-laki.
Abstract. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is a popular method for forecasting univariate time series data for data containing seasonality. This method consists of several stages, namely: identification, parameter assessment, diagnostic examination, and forecasting using the SARIMA (p,d,q)(P,D,Q)S model. The SARIMA model can be applied in various fields, one of which is the medical field. The number of patients infected with the CoVID-19 virus continues to grow every day. Indonesia is one of the countries experiencing the impact of the COVID-19 virus. On December 28, 2021, the number of positive Covid-19 patients in Indonesia was 4,262,157, with 4,113,472 patients recovering and 144,071 patients dying. Seeing the high number of positive cases of Covid-19 in Indonesia, the author wants to conduct research on modeling cases of patients who are confirmed to be positive for Covid-19 per day in Indonesia and then from this model, data forecasting will be carried out for the next 28 periods. The data collection period is from November 1, 2021 to December 28, 2021. And the results of a good model for predicting cases of confirmed positive COVID-19 patients per day in Indonesia are the SARIMA (2,1,2)(2,1,1)7 model, with The seasonal length is 7 periods, and the sum squared resid is 0.927619. Abstrak. Model Seasonal Autoregressive Integrated Moving Average (SARIMA) adalah metode populer untuk meramalkan data deret waktu univariat untuk data yang mengadung musiman. Metode ini terdiri dari beberapa tahapan, yaitu: identifikasi, penilaian parameter, pemeriksaan diagnostik, dan peramalan menggunakan model SARIMA (p,d,q)(P,D,Q)S. Model SARIMA dapat diterapkan di berbagai bidang, salah satunya bidang medis. Jumlah pasien yang terinfeksi virus CoVID-19 terus bertambah setiap harinya. Negara Indonesia merupakan salah satu Negara yang mengalami dampak virus covid-19. pada 28 Desember 2021, jumlah pasien positif Covid-19 di Indonesia sebanyak 4.262.157 pasien, dengan 4.113.472 pasien sembuh dan 144.071 pasien meninggal dunia. Melihat tingginya kasus positif Covid-19 di Indonesia, maka penulis ingin melakukan penelitian tentang pemodelan kasus pasien terkonfirmasi positif covid-19 perhari di Indonesia untuk kemudian dari model tersebut akan dilakuakn peramalan data untuk 28 periode kedepan. Periode pendataan dari tanggal 1 November 2021 sampai dengan 28 Desember 2021. Dan hasil model yang baik untuk memprediksi kasus pasien terkonfirmasi positif covid-19 perhari di Indonesia adalah model SARIMA (2,1,2)(2,1,1)7, dengan panjang musiman nya 7 periode, dan nilai sum squared resid sebesar 0.927619.
The autoregressive integrated moving average (ARIMA) model is a popular method for forecasting univariate time series dataset. This method consists of four major stages, namely: identification, parameter assessment, diagnostic examination, and forecasting using the ARIMA model (p, d, q). ARIMA model can be applied in various fields, one of which is medical field. Currently, there had been a daily increase in the number of patients infected with Corona virus. Jember is one of the regencies in East Java with a high number of confirmed patients. On February 5, 2021, it was recorded that 5,872 patients were confirmed positive for Corona, 5,241 patients had been declared cured, and 352 patients were declared dead. Given the high number of confirmed cases of Covid-19 in Jember, the authors would like to conduct a prediction research on the increasing number of confirmed cases of Covid-19 in Jember Regency for the upcoming period using the ARIMA model (p,d,q). The research was conducted in the Jember Regency, East Java. The data were collected from March 28, 2020 to January 30, 2021. The study showed that the ARIMA model (1,2,3) was the best model for predicting the additional positive cases of Covid-19 per week in Jember, with the sum squared resid of 7.9496. The data forecast for the additional positive cases of Covid-19 for the next 6 periods is: 224,56 patients, 247,84 patients, 273,53 patients, 301,89 patients, 333,18 patients, and 367,72 patients. Received February 10, 2021Revised April 8, 2021Accepted April 22, 2021
Abstract. One of the methods used to overcome overdispersion in poisson regression model is a bivariate negative binomial regression model also known as BNBR Model. Leprosy is a dangerous infectious disease, because it can cause paralysis. Leprosy is divided into 2 types, namely is a leprosy Pausibasilier(PB) type and leprosy Multibasilier (MB) type. Where PB type leprosy is a dry leprosy and MB type leprosy is a wet leprosy. Analysis of the data used to model the number of PB leprosy and MB leprosy cases and find out what factor influence it in East Java, the writer uses the BNBR models. Parameter estimation of the BNBR model uses to Maximum likelihood estimation (MLE) methods with Newton-Raphson iteration as well as testing the hypothesis using MLRT methods. After regression analysis, the results are obtained that of the 10 predictor variables tested, both in PB leprosy and MB leprosy, there are 3 predictor variables that are not significant in the model, namely are: variable percentage of poor population, variable ratio of population who did not graduated SMA, and variable ratio of health facilities. Abstrak. Salah satu metode yang digunakan untuk mengatasi overdispersi dalam regresi Poisson yakni dengan regresi binomial negatif bivariat atau dikenal juga dengan model regresi BNBR. Penyakit Kusta adalah salah satu penyakit menular yang berbahaya, karena dapat menyebabkan kelumpuhan. Jenis penyakit kusta terbagi menjadi 2, yakni Kusta tipe Pausibasiler (PB) dan tipe Multibasiler.(MB). Dimana kusta tipe PB merupakan Kusta kering, dan kusta tipe MB adalah kusta basah. Analisis data yang digunakan untuk memodelkan besarnya jumlah kasus kusta tipePB dan tipe MB, kemudian untuk mengetahui faktor apa saja yang mempengaruhinya di Jawa Timur, penulis menggunakan model BNBR. Penaksiran parameter model BNBR menggunakan Maximum Likelihood Estimation (MLE) dengan iterasi Newton-Raphson serta melakukan pengujian hipotesis menggunakan metode MLRT. Setelah dilakukan analisis regresi, diperoleh hasil bahwa dari 10 variabel prediktor yang diujikan, baik pada kusta tipe PB maupun tipe MB, terdapat 3 variabel prediktor yang tidak signifikan dalam model, yakni: variabel presentase penduduk miskin, variabel rasio penduduk yang tidak tamat SMA, dan variabel rasio sarana kesehatan.
Human Immunodeficiency Virus (HIV) is dangerous diseases for humans, and until now has not found a cure. Virus HIV is attacks the human immune system so that someone is susceptible to disease. This causes if someone is infected with HIV, then the person can experience an danger condition, it will even effect is death. In recent years, the number of children aged 5 – 14 years old that infected with HIV continues to increase. Therefore the author was moved to write about the application of the ARIMA model in forecasting the number of children aged 5 – 14 years old that infected with HIV in Indonesia by 2023. With the hope that the public or the govermment can find out the potential dangers of HIV disease, especially in children aged 5 – 14 years old. So that the public and govermment can jointly eradicate the spread of the HIV virus, especially in chidren. the result are obtained that the model that is suitable for use in forecasting is the ARIMA(0,1,2) models, with error value obtained is 0.057429. with the forecast value of the number of children aged 5 – 14 years old that infected with HIV in Indonesia from 2019 – 2023 in a row is : 570.82, 647.12, 734.14, 823.85, 944.83.
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