ABSTRAKAnalisis survival merupakan suatu himpunan dari prosedur statistika untuk menganalisis data di mana variabel respon diakibatkan waktu (time) sampai suatu peristiwa terjadi. Salah satu penerapan dari regresi survival adalah mengetahui laju kesembuhan penderita demam berdarah dengue. Karena penyebaran peyakit demam berdarah dengue disebabkan oleh penyebaran nyamuk, maka terdapat kemungkinan bahwa kejadian di suatu lokasi pasti mempengaruhi kejadian lokasi lain. Oleh karena itu lebih tepat dimodelkan dengan spasial survival dan metode yang digunakan adalah metode Bayesian. Model menyertakan efek random spasial CAR (conditionally autoregresive) untuk mengatasi pengaruh spasial terhadap model survival dengan menggunakan pembobot tipe Queen Contiguity. Tujuan dari penelitian ini adalah memperoleh model survival spasial pada data survival tahun 2013 untuk kejadian demam berdarah dengue di Kota Malang. Berdasarkan data tersebut, nilai moran I sebesar -0,5930 dengan nilai uji Z sebesar -2,002 yang berarti bahwa terdapat autokorelasi spasial pada kejadian deman berdarah dengue di Kota Malang. Model spasial survival dengan menggunakan distribusi Weibull-3 Parameter (Weibull-3P) didapatkan faktor-faktor yang berpengaruh signifikan terhadap demam berdarah dengue yaitu jenis kelamin, kadar hematrokit dan jumlah trombosit serta di tiap kecamatan memiliki laju kesembuhan yang sama.Kata kunci : demam berdarah dengue, Bayesian, spasial survival Weibull-3P, frailty berdistribusi conditionaliy autoregressive (CAR), queen contiguity dan Moran's I. ABSTRACTSurvival analysis is a collection of statistical procedures for data analyzing, where respon variables caused by time until an event occurs. One of application of survival regression's purpose is to know dengue hemorragic fever. Since the spread of dengue hemorragic fever caused by the spread of mosquito, there is probability that event in one location affects other event in another locations thus, it is better to model with Bayessian method of spatial survival. Model includes random spatial effect CAR to overcome the spatial effect in survival model using queen contiguity type weight. This study aimed to obtain spatial survival model one survival data year of 2013 which was the event of dengue hemorragic fever in city of Malang. Based on the data, moran value I was -0.5930 with Z-test value equal to -2,002, which means there is a spatial autocorelation on the event of dengue hemorragic fever in city of Malang. Spatial survival model with Weibull-3 Parameter (Weibull-3P) distribution obtained the factors significantly affecting dengue hemorragic fever, which were sex, hematrocit rate, thrombocyte volume had equal rate of healing in each subdistrict.Keywords : dengue hemorragic fever, Bayessian, spatial survival Weibbul-3P, frailty distributed conditionally autoregresive (CAR), queen contiguity and Moran's I PENDAHULUANAnalisis survival adalah suatu himpunan dari prosedur statistika untuk menganalisis data
Survival analysis is a statistical procedure that describes a mathematical model that is often applied in various studies, especially in health. One application of survival analysis is to determine the rate of survival and the factors affecting HIV / AIDS sufferers in East Java. HIV / AIDS is a virus that attacks or infects white blood cells, causing a decrease in immune cells. This disease causes a decrease in productivity in the health and economic sectors of a country. Even if the disease continues to increase, the weak economic development will decrease due to the treatment of HIV/AIDS and the risk of death of people infected with the HIV / AIDS virus is getting higher in East Java. In addition to these health and economic quality factors, factors such as residents' knowledge of the disease. By knowing the factors of HIV/AIDS survival rate, mathematical modelling can be done to estimate the duration of the patient's survival power comprehensively and accurately. In this study, we want to find out what factors affect the survival rate of HIV/AIDS using the 3-Parameter Lognormal Survival Link Function model in which the method of parameter estimation used is the Bayesian MCMC-Gibbs Sampling method. The best models is the 3-parameter lognormal survival with frailty that is normally distributed and factors affect the survival rate of HIV/AIDS is education (X3), marital status (X5), Stadium of the patient (X8), adherence of therapy (X10), opportunistic infection (X11) and risk factor of infection (X13). Analisis survival merupakan suatu prosedur statistika yang menjelaskan model matematis yang seringkali diaplikasikan dalam berbagai penelitian, terutama di bidang kesehatan. Salah satu penerapan dari analisis survival adalah untuk mengetahui laju bertahan hidup dan faktor-faktor yang mempengaruhi penderita HIV/AIDS di Jawa Timur. Penyakit HIV/AIDS adalah virus yang menyerang atau menginfeksi sel darah putih yang menyebabkan turunnya sel kekebalan tubuh. Penyakit ini mengakibatkan penurunan produktivitas di bidang kesehatan dan ekonomi di suatu negara. Bahkan apabila penyakit ini terus meningkat maka lemahnya perkembangan ekonomi akan menjadi menurun akibat pengobatan penyakit HIV/AIDS dan resiko kematian dari orang yang terinfeksi virus HIV/AIDS tersebut semakin tinggi di Jawa Timur. Disamping faktor kualitas kesehatan dan ekonomi tersebut, faktor seperti pengetahuan warga terhadap penyakit HIV/AIDS. Dengan mengetahui faktor-faktor laju bertahan hidup penyakit HIV/AIDS dapat dilakukan pemodelan matematis untuk memperkirakan durasi daya survival secara aktual, dan komprehensif. Tujuan artikel dalam penelitian ini adalah menjelaskan faktor-faktor yang mempengaruhi laju bertahan hidup pasien terhadap penyakit HIV/AIDS dengan menggunakan model Survival Lognormal 3 parameter Link Function. Metode estimasi parameter yang digunakan adalah metode Bayesian MCMC-Gibbs Sampling. Model Survival Lognormal 3 Parameter dengan Frailty yang berdistribusi normal menghasilkan faktor-faktor yang mempengaruhi laju bertahan hidup pasien HIV/AIDS di Jawa Timur adalah pendidikan(X3), status perkawinan (X5), stadium penderita (X8), kepatuhan terapi (X10), infeksi oportunitis (X11) dan resiko penularan (X13).
There are various types of data, one of which is the time-series data. This data type is capable of predicting future data with a similar speed as the forecasting method of analysis. This method is applied by Bank Indonesia (BI) in determining currency inflows and outflows in society. Moreover, Inflows and outflows of currency are monthly time-series data which are assumed to be influenced by time. In this study, several forecasting methods were used to predict this flow of currency including ARIMA, Time Series Regression (TSR), ARIMAX, and NN. Furthermore, RMSE accuracy was used in selecting the best method for predicting the currency flow. The results showed that the ARIMAX method was the best for forecasting because this method had the smallest RMSE.
Background: Adequate fluid consumption and hydration status of students become a special concern because being dehydrated by just 1%-2% can impair cognitive performance. The objectives of this study were to assess the daily fluid consumption, and analyze the correlation of fluid consumption and other associated factors with hydration status of medical students in Universitas Sriwijaya.Methods: A total of 93 medical students in Universitas Sriwijaya were recruited to complete a 7-day cross-sectional study. Subjects were asked to complete a self-administered 7-day-24-hours fluid record and provide first morning urine sample on the last day. Gender information was collected. Physical activity was evaluated by self-administered long version of IPAQ. Body mass index was calculated using body weight and body height measurement. Urine specific gravity was determined by urinometer. The 7-day-24-hours fluid record and 1-day-24-hours urine specific gravity were calculated and analyzed.Results: Majority of the subjects were well hydrated, while 10.8% were slightly hydrated, 6.5% were moderately hydrated and 9.7% were severely dehydrated. The average of daily fluid consumption was 1789.28 (989.3-2930) mL. Coefficient correlation of fluid consumption from beverages with urine specific gravity was -0.651 (p=0.00) by Pearson correlation test. The hydration status showed no association with gender, physical activity and body mass index.Conclusions: Most subjects in this study were well hydrated. A strong association was found between fluid consumption and hydration status. It was feasible to use daily fluid consumption from beverages to predict hydration status.
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