Sudarno Sudarno
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

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Journal : Media Statistika

HAZARD PROPORTIONAL REGRESSION STUDY TO DETERMINE STROKE RISK FACTORS USING BRESLOW METHOD Sudarno Sudarno; Eri Setiani
MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (492.514 KB) | DOI: 10.14710/medstat.12.2.200-213

Abstract

Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is phrase used to describe analysis of data in the form of times from a well-defined time origin until occurrence of some particular be death. In analysis survival sometimes ties are found, namely there are two or more individual that have together event. The objectives of this research are applied Cox proportional hazard regression on ties event using Breslow methodand determine factors that affect survival of stroke patients in Tugurejo Hospital Semarang. The response variable is length of stay at hospital, and the predictors are gender, age, type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, blood sugar levels, and body mass index. The factors cause stroke disease by significant are type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, and blood sugar level. By the survivorship function that the patients have been looked after at hospital greater than 20 days, they have probability of healthy be little even go to death. A person in order to be healthy must notice and prevent some factors cause disease.
PEMODELAN HYBRID ARIMA-ANFIS UNTUK DATA PRODUKSI TANAMAN HORTIKULTURA DI JAWA TENGAH Tarno Tarno; Agus Rusgiyono; Budi Warsito; Sudarno Sudarno; Dwi Ispriyanti
MEDIA STATISTIKA Vol 11, No 1 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (506.342 KB) | DOI: 10.14710/medstat.11.1.65-78

Abstract

The research purpose is modeling adaptive neuro fuzzy inference system (ANFIS) combined with autoregressive integrated moving average (ARIMA) for time series data. The main topic is application of Lagrange Multiplier (LM) test for input selection, determining the number of membership function and generating rules in ANFIS. Based on partial autocorrelation (PACF) plot, the lag inputs which are thought have an effect to data are evaluated by using LM-test. Procedure of LM test is applied to determine the optimal number of membership functions. Based on the result, a number of rule-bases are generated. The best model is applied for forecasting potato production data in Central Java. The case study of this research is modeling monthly data of potato production from January 2004 up to December 2016. From empirical study, ANFIS optimal was obtained with lag-1 and lag-11 as inputs with two membership functions and two fuzzy rules. The hybrid method based on ARIMA and ANFIS is also implemented. The result of the prediction with a hybrid method is compared to the ANFIS and ARIMA. Based on the value of Mean Absolute Percentage Error (MAPE), hybrid model ARIMA-ANFIS has a good performance as a model of ANFIS and ARIMA individually.Keywords: Time Series, Potato production, hybrid, ANFIS, ARIMA, LM-test