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Journal : Jurnal Gaussian

ANALISIS ANTRIAN PASIEN INSTALASI RAWAT JALAN RSUP Dr. KARIADI BAGIAN POLIKLINIK, LABORATORIUM, DAN APOTEK Wahyuningtias, Rany; Ispriyanti, Dwi; Sugito, Sugito
Jurnal Gaussian Vol 2, No 4 (2013): Wisuda Periode Oktober 2013
Publisher : Jurusan Statistika UNDIP

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Abstract

Queue process is a process of the coming of a customer to a service facility, then waiting in line (queue) when the officers busy, and leaving the place after getting the service.  Patient’s line at RSUP DR. Kariadi is a lot enough then it will making the service from the hospital isn’t optimal as a result.  Hence, it needed a queue model to optimize the service to patient. From the result of the analysis in RSUP Dr. Kariadi it gives the best queue models is  in polyclinic area second floor, laboratory, and pharmacy.
PERBANDINGAN MODEL REGRESI BINOMIAL NEGATIF DENGAN MODEL GEOGRAPHICALLY WEIGHTED POISSON REGRESSION (GWPR) (Studi kasus : Angka Kematian Ibu di Provinsi Jawa Timur Tahun 2011) Suparti, Suparti; Ispriyanti, Dwi
Jurnal Gaussian Vol 2, No 3 (2013): Wisuda Periode Agustus 2013
Publisher : Jurusan Statistika UNDIP

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Abstract

Maternal mortality rate is one of the crucial problems of death in Indonesia. Maternal deaths in East Java province is likely to increase so that the role of data and information are very important. Negative Binomial Regression is a model that can be used to address the problem overdispersion. While the method of spatial attention factor for type discrete data is Geographically Weighted Poisson Regression Model (GWPR). This study was conducted on the comparison between the Negative Binomial Regression and GWPR to discuss the factors that influence maternal mortality rate in the province of East Java. Indicators that affect maternal mortality include maternal health services. Maternal health services such as antenatal care, obstetric complications treated, Aid deliveries by skilled health care child birth, and neonatal health care services handled neonatal complications. The results of testing the suitability of model shows that there is no influence of spatial factors on maternal mortality rate in the province of East Java. Based on Negative Binomial Regression derived variable number of puerperal women who received vitamin A significantly affect maternal mortality rate, while for GWPR is divided into six clusters districts/cities by same significant variables. From the comparison value of AIC was found that GWPR better to analyzing Maternal mortality in East Java because it has the smallest value of AIC
ANALISIS REGRESI LINIER PIECEWISE DUA SEGMEN Syilfi, Syilfi; Ispriyanti, Dwi; Safitri, Diah
Jurnal Gaussian Vol 1, No 1 (2012): Wisuda Periode Oktober 2012
Publisher : Jurusan Statistika UNDIP

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Abstract

Regression analysis is a statistical method that is widely used in research. In general, the regression analysis is the study of the relationship of one or more independent variables with the dependent variable. In analyze the functional relationship between X as the independent variables and Y as the dependent variable, there may be a linear relationship is different for each interval X. If the regression of X on Y has a linear relationship on the certain of the interval of X, but also has a distinct linear relationship at another interval of X, so the use of piecewise linear regression is appropriate in this case. Piecewise linear regression is a method in regression analysis that divided the independent variable into several segments based on a particular value called the X-knots, and in each segment of the data contained linear regression model. X-knot is a value on the independent variable, where X is the current value of the X-knots, it will form a linear regression equation of the line that is different than the current value of X is under X-knots. Piecewise linear regression can be applied in many fields, one of them in the waters of the analysis regarding the influence of river discharge on the basis of the number of transport sediman. By comparison MSE simple linear regression and multiple linear piecewise two segments, the result that the two segments piecewise linear regression is a model that describes the influence of river discharge on the basis of the number of bedload transport
PENENTUAN MODEL SISTEM ANTREAN KENDARAAN DI GERBANG TOL BANYUMANIK SEMARANG Nugraha, Dedi; Sugito, Sugito; Ispriyanti, Dwi
Jurnal Gaussian Vol 2, No 2 (2013): Wisuda Periode April 2013
Publisher : Jurusan Statistika UNDIP

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Abstract

The arrival rate of vehicles that have occured at the Banyumanik tollgate is randomly and fluctuatly. Those condition would make difficult for tollgate management to determine policies in operating the substation service. If the substation service operates slightly, can occur long queues, especially at certain time. In the meantime, if the substation service operates many service, service to be inefficient. Therefore, it is necessary to determine the queuing system model in accordance with the conditions and characteristics of the queue from service facilities at the Banyumanik tollgate appropriately. So it can be determined the efektif and efisien number of service substation. Based on the analysis of data obtained, a queue model system that occurred at the Banyumanik tollgate is . The efektif number of substations service for directions Ungaran-Semarang are two subtations service. While for direction Semarang-Ungaran, the efektif number of substation service is three.
APLIKASI MODEL REGRESI POISSON TERGENERALISASI PADA KASUS ANGKA KEMATIAN BAYI DI JAWA TENGAH TAHUN 2007 Umami, Nurwihda Safrida; Ispriyanti, Dwi; Widiharih, Tatik
Jurnal Gaussian Vol 2, No 4 (2013): Wisuda Periode Oktober 2013
Publisher : Jurusan Statistika UNDIP

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Abstract

Infant Mortality is one of the issues that can affect the number and age composition of the population. The Government pays special attention to reduce the amount of Infant Mortality Rate in Central Java, so the role of data and information becomes very important. Poisson regression is a nonlinear regression which is often used to model the relationship between the response variable in the form of discrete data with predictor variables in the form of continuous or discrete data. Poisson regression models have equidispersi assumption, a condition in which the mean and variance of the response variable have equal value. In practice, the assumption is sometimes violated in the analysis of discrete data in the form of overdispersi (value of variance greater than the mean value) so that Poisson regression model is not appropriate to be used. Overdispersi is a condition in which the data of response variable shows. One model that can be used to solve the overdispersi problem is generalized Poisson regression model. The regression model is an extension of the Poisson regression and part of the Generalized Linear Model (GLM) which does not require constancy of variance to test the hypothesis. From the data of Infant Mortality Rate in Central Java on 2007 known that there overdispersi. And the factors affecting Infant Mortality Rate is the number of health facilities, the number of medical personnel, and the percentage of households with clean water each county / city.
REGRESI ROBUST MM-ESTIMATOR UNTUK PENANGANAN PENCILAN PADA REGRESI LINIER BERGANDA Candraningtyas, Sherly; Safitri, Diah; Ispriyanti, Dwi
Jurnal Gaussian Vol 2, No 4 (2013): Wisuda Periode Oktober 2013
Publisher : Jurusan Statistika UNDIP

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Abstract

The multiple linear regression model is used to study the relationship between a dependent variable and more than one independent variables. Estimation method which is the most frequently be used to analyze regression is Ordinary Least Squares (OLS). OLS for linear regression models is known to be very sensitive to outliers. Robust regression is an important method for analyzing data contaminated by outliers. This paper will discuss the robust regression MM-estimator. This estimation is a combined estimation method which has a high breakdown value (LTS-estimator or S-estimator) and M-estimator. Generally, there are three steps for MM-estimator: estimation of regression parameters initial using LTS-estimators, residual and robust scale using M-estimator, and the final estimation parameter using M-estimator. The purpose of writing this paper are to detect outliers using DFFITS and determine the multiple linear regression equations containing outliers using robust regression    MM-estimator. The data used is the generated data from software Minitab 14.0. Based on the analysis results can be concluded that data 21st, 27th, 34th are outliers and equation of multiple linear regression using robust regression MM-estimators is .
PENENTUAN CADANGAN DISESUAIKAN DENGAN METODE ILLINOIS PADA ASURANSI JIWA ENDOWMEN SEMIKONTINU Revani, Marlia Aide; Wilandari, Yuciana; Ispriyanti, Dwi
Jurnal Gaussian Vol 1, No 1 (2012): Wisuda Periode Oktober 2012
Publisher : Jurusan Statistika UNDIP

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Abstract

Semicontinuous endowment insurance is a kind of insurance with a periodic premium payments which gives two benefits, payment of death benefit at the moment of death if the insured dies during a certain period of years or payment of living benefit if the insured survives to the end of the period. The insurer’s obligation of insured’s premium payments, provides net level premium reserves for benefit payment in the future. The insurer needs expenses for it’s operate and in fact, the first year expenses usually exceed the loading. This means that an insurance company have to find funds to cover the first year expenses. The funds can be obtained by modified reserve system. To get information of modified reserve value for semicontinuous life insurance, the study of determination of modified reserve value using Illinois method has been done. The full net level reserves are lesser than the reserves under the Illinois method before the end of min(n, 20) years and both of these reserves will be equal at the the end of min(n, 20) years, with n is premium period.
PERBANDINGAN ARIMA DENGAN FUZZY AUTOREGRESSIVE (FAR) DALAM PERAMALAN INTERVAL HARGA PENUTUPAN SAHAM (Studi Kasus pada Jakarta Composite Index) Anshari, Muhammad Fitri Lutfi; Ispriyanti, Dwi; Wilandari, Yuciana
Jurnal Gaussian Vol 2, No 3 (2013): Wisuda Periode Agustus 2013
Publisher : Jurusan Statistika UNDIP

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Abstract

The capital market is one of the most popular investment option today. In capital market, stock price prediction is an important issue for investors, so needed a good forecasting method as a basic for decision-making for the transaction. One of the most popular forecasting method is ARIMA, but this method still uses the concept that measurement error which is obtained from the difference between the observed values with estimated values. To resolve the error in modeling, Fuzzy Autoregressive was developed, it is a model combination of Fuzzy Regression and Autoregressive (AR). This method gives results in interval forecasting, thus providing information to decision makers regarding the best and worst situation that may occur. This paper discusses the application of Fuzzy Autoregressive forecasting interval for the Jakarta Composite Index and compare it with the ARIMA prediction interval. The result of this study is Fuzzy Autoregressive interval is narrower than the ARIMA 95% significance rate
PERBANDINGAN ANALISIS DISKRIMINAN LINIER KLASIK DAN ANALISIS DISKRIMINAN LINIER ROBUST UNTUK PENGKLASIFIKASIAN KESEJAHTERAAN MASYARAKAT KABUPATEN/KOTA DI JAWA TENGAH Kartikawati, Ana; Mukid, Moch. Abdul; Ispriyanti, Dwi
Jurnal Gaussian Vol 2, No 3 (2013): Wisuda Periode Agustus 2013
Publisher : Jurusan Statistika UNDIP

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Abstract

Discriminant analysis is a statistics method which is used to classify an individual or object into certain group which has determined based on its independent variables. Discriminant analysis that commonly used is classical discriminant analysis which consist of classical linear discriminant analysis and classical quadratic discriminant analysis. In classical linear discriminant analysis there are two assumptions to be fulfilled i.e. independent variables have to be normal multivariate distributed and the covariance matrix from the two observed objects should be the same. Classical discriminant analysis cannot work properly if the data which being analyzed consists of many outliers. In order to make discriminant analysis works optimally within the classification though in the condition of data which contains of many outliers, robust estimator is needed. The robust discriminant analysis is used to get the high classification accuracy for data which contains of many outliers. Fast-MCD estimator is one of the robust estimators which is aimed to get the smallest determinant of covariance matrices. The robust linear discriminant analysis with fast-MCD method in this graduating paper is implemented to determine the prosperity status of the people in the regencies or towns in Central Java. The total proportion of classification accuracy using robust linear discriminant analysis method on the data of Central Java people prosperity is 77.14 percent. It is equal with the result from classic linear discriminant analysis which is also 77.14 percent. It is caused by the few amount of outlier on the data of Central Java people prosperity.
PENGAMBILAN SAMPEL BERDASARKAN PERINGKAT PADA ANALISIS REGRESI LINIER SEDERHANA Wijayanti, Pritha Sekar; Ispriyanti, Dwi; Wuryandari, Triastuti
Jurnal Gaussian Vol 2, No 3 (2013): Wisuda Periode Agustus 2013
Publisher : Jurusan Statistika UNDIP

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Abstract

Ranked Set Sampling and Ranked Set Sampling concomitant are more efficient than Simple Random Sampling. This can be determined by calculating the Relative Precision which is a ratio value from the variance of the mean from each sampling technique. From the research of Ranked Set Sampling, obtained ,  and  so Ranked Set Sampling is more efficient than Simple Random Sampling. For the research of Ranked Set Sampling concomitant, obtained ,  and  so Ranked Set Sampling concomitant is more efficient than Simple Random Sampling, and for simple linear regression analysis obtained , , ,  so simple linear regression model of Ranked Set Sampling is more efficient than simple linear regression model of Simple Random Sampling
Co-Authors A Rusgiyono Abdul Hoyyi Agus Rusgiyono Ain Hafidita Alan Prahutama Ana Kartikawati Anisa Septi Rahmawati, Anisa Septi Anjan Setyo Wahyudi, Anjan Setyo Arief Rachman Hakim Atika Elsadining Tyas, Atika Elsadining Aulia Ikhsan Avia Enggar Tyasti, Avia Enggar Berta Elvionita Fitriani, Berta Elvionita Bitoria Rosa Niashinta, Bitoria Rosa Budi Warsito Dedi Nugraha Diah Safitri Dita Ruliana, Dita Dwi Rahmayani, Dwi Dyan Anggun Krismala Dydaestury Jalarno Erna Sulistio Evi Yulia Handaningrum Firdha Rahmatika Pratami, Firdha Rahmatika Gera Rozalia, Gera Hasbi Yasin Henny Widayanti, Henny Ilham Maggri Irawati Tamara, Irawati Kishatini Kishartini Marlia Aide Revani Masfuhurrizqi Iman Maulida Azkiya, Maulida Maulida Najwa, Maulida Moch. Abdul Mukid Muhammad Fitri Lutfi Anshari Muhammad Rosyid Abdurrahman Mustafid Mustafid Nanci Rajagukguk, Nanci Nandang Fahmi Jalaludin Malik Natalia P P, Sylvi Nova Nova Noviana Nurhayati Nurwihda Safrida Umami Oka Afranda, Oka Pritha Sekar Wijayanti Pusphita Anna Octaviani Rahafattri Ariya Fauzannissa, Rahafattri Ariya Rahmah Merdekawaty, Rahmah Rany Wahyuningtias Ratih Nurmalasari, Ratih Ratna Pratiwi Ria Sutitis, Ria Rio Tongaril Simarmata Rita Rahmawati Riza Adi Priantoro, Riza Adi Sherly Candraningtyas Sindy Saputri Sisca Agustin Diani Budiman, Sisca Agustin Diani Sri Maya Sari Damanik Sudarno Sudarno Sugito Sugito Suparti Suparti Suparti, S. Syilfi Syilfi Tarno Tarno Taryono, Arkadina Prismatika Noviandini Tatik Widiharih Tiani Wahyu Utami Triastuti Wuryandari Warsito Budi Yani Puspita Kristiani, Yani Puspita Yuciana Wilandari