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Jurnal Statistika Universitas Muhammadiyah Semarang
Published by Universitas Muhammadiyah Semarang
ISSN : 23383216     EISSN : 25281070     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Focus and Scope a. Statistika Teori, Statistika Komputasi, Statistika terapan b. Matematika Teori dan Aplikasi c. Design of Experiment
Arjuna Subject : Matematika - Pemodelan dan Simulasi
Articles 9 Documents
 1 
Search results for , issue "Vol 9, No 1 (2021): Jurnal Statistika" : 9 Documents clear
ANALISIS KETAHANAN HIDUP MENGGUNAKAN METODE PERLUASAN REGRESI COX DENGAN VARIABEL BERGANTUNG WAKTU Nabila Chairunnisa; Agus Rusgiyono; Puspita Kartikasari
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.40-46

Abstract

Extension of the cox proportional hazards model for time dependent variables is an alternative method used if the PH assumption is not satisfied for one or more of the predictors in the model. The step of this method are testing the PH assumption, interacting the time independent variable not satisfying the PH assumption with function of time (linear, logarithm and the combination), testing the parameters using the Likelihood Ratio test and the Wald test, determine the better model of both models by the AIC values, calculating the hazard ratio of each variable that significantly affected ASD. Based on the smallest AIC values, a better model is the extension of the cox proportional hazards model for time dependent variables interacted with logarithm time function. From the better model obtained the variables that affect the recurrence time ASD is patient with heart beat, noisy heart, blood pressure, body mass index, and treatment with catheter.
PEMODELAN REGRESI ROBUST M-ESTIMATOR DALAM MENANGANI PENCILAN (STUDI KASUS PEMODELAN JUMLAH KEMATIAN IBU NIFAS DI JAWA TENGAH Alan Prahutama; Agus Rusgiyono; Dwi Ispriyanti; Tiani Wahyu Utami
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.35-39

Abstract

Regression analysis is statistical method that used to model between predictor variables and response variables. In the regression model, the residual assumed normal distribution, non-autocorrelation, and homoscedasticity. When the assumptions doesn’t fulfilled, then the measurement of goodness not well enough. One of the causes may be outlier of data. Handling the outlier can be used robust regression, which one of method is robust M-estimator.   In this article, we purposed modelling the number of maternal postpartum in Central Java province with predictor variables are the percentage of pregnant who consumed Fe tablet (X1), the percentage of household whom applied clean and health lifestyle(X2), and the percentage of pregnant who First visited to midwife of doctor (K1) (X3).  In the multiple regression only X3 was significantly with R-square was 14.25209%, and Mean Square Error (MSE) was 20.4177. Moreover, in outlier detection, there were two outlier in the data, then modelled with Robust M-estimator. The measurement of goodness used R-square of regression robust M-estimator was 21.74% with MSE was 15.02766. Robust M-estimator regression resulted better model than multiple regression to model the number of maternal postpartum in Central Java Province.
ANALISIS MODEL ANTREAN NON POISSON DAN UKURAN KINERJA SISTEM PELAYANAN MENGGUNAKAN GUI R Luthfi Nashukha Dewi; Sugito Sugito; Alan Prahutama; Mustafid Mustafid; Dwi Ispriyanti Dwi Ispriyanti
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.28-34

Abstract

The queue process is a process related to the arrival of a customer at a service facility, then waits in line if not yet received service and leaves the service facility after receiving service. The queue occurs because many people need services at the same time and the number of service facilities available is limited. In this case, the arrival pattern follows the Poisson distribution assuming the arrival is random. Departement of Population and Civil Registration in Semarang City (Dispendukcapil) is one of the public service places that often arise in line. Therefore, this system needs to be applied with queue theory. The queue theory was developed to provide a model in determining system performance. The queue model that has been obtained at every counter in  Dispendukcapil is customer service (UNIF/LOGN/1):(GD/∞/∞), legalized (UNIF/LOGN/2):(GD/∞/∞), data changes (UNIF/BETA/1):(GD/∞/∞), birth (UNIF/BETA/2):(GD/∞/∞), death (UNIF/BETA/2):(GD/∞/∞), second quote (UNIF/LOGN/1):(GD/∞/∞), biometric (UNIF/LOGN/2):(GD/∞/∞), resident registration (UNIF/LOGN/2):(GD/∞/∞), electronic ID Card recording (UNIF/GAMM/1):(GD/∞/∞). In measuring the performance of the system obtained through the GUI R. Based on the results obtained, the Dispendukcapil service system is optimal because of the low waiting time.
MODEL REGRESI COX PROPORSIONAL HAZARD PADA DATA DURASI PROSES KELAHIRAN DENGAN TIES Triastuti Wuryandari; Danardono Danardono; Gunardi Gunardi
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.47-55

Abstract

Survival data are usually found in the fields of health, insurance, epidemiology, demography, etc. Survival data is characterized by a response in the form of time, one example is the duration of the birth process. The duration of the birth process is thought to be influenced by several factors, including the baby's weight, baby's height, mother's age, gestational age, gender and the method used to birth process. One of the regression models for survival data is the Cox regression proportional hazard model. Parameter estimation in the Cox regression is based on partial likelihood. If two or more individuals have the same survival value, it is called ties. If there are ties, then the partial likelihood will have problems in determining the risk set, so it is necessary to modify the partial likelihood. Methods that can be used to overcome ties are the Breslow, Efron and Exact methods. This method is a modification of parameter estimation using maximum partial likelihood. Parameter estimation results are obtained by maximizing the partial likelihood function using Newton Raphson iteration. The case study in this paper is data on the duration of the birth process. The best model for the duration of the birth process with ties is the Exact method because it has the smallest AIC value
ESTIMASI CADANGAN KLAIM MENGGUNAKAN METODE DETERMINISTIK DAN STOKASTIK Yuciana Wilandari; Gunardi Gunardi; Adhitya Ronnie Effendie
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.56-63

Abstract

The estimated of claims reserve has a very important in insurance companies, because it is the company's liability to policyholders in the future and can also result in the bankruptcy of the insurance company. In general, there are two methods for calculating claims reserves are the deterministic method (Chain Ladder and Bornhuetter Ferguson) and the stochastic method (Benktander Hovinen and Cape Cod). This article compares the two methods and determines the best method. Using the claim payments data that have been paid by an insurance company in Indonesia, the best method is the Benktander Hovinen method.
PENCARIAN KERNEL TERBAIK SUPPORT VECTOR REGRESSION PADA KASUS DATA KEMISKINAN DI INDONESIA DENGAN USER INTERFACE (GUI) MATLAB Muhammad Ghazali; Ramdani Purnamasari
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika
Publisher : Department Statistics, Faculty Mathematics and Natural Science, Universitas Muhammadiyah S

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.1-8

Abstract

Poverty is a topic that is often discussed in various scientific study forums. Facts on the ground show that increasing development has not been able to reduce the increasing number of poor people. Statistical studies on poverty data need to be carried out to assist the government in mapping policy-making patterns. One of the variables in mapping poverty data patterns is the Poverty Depth Index. The poverty depth index is a measure of the average gap in the distribution of each population's expenditure on the poverty line. Many factors affect the poverty depth index, especially from health, human resources and economic indicators. Therefore, a statistical modeling is needed to analyze the factors that affect the poverty depth index in Indonesia. The poverty data used in this study were sourced from the 2019 SUSENAS data in the form of data with individual observations of all provinces in Indonesia. Several previous studies using the Support Vector Regression (SVR) method to estimate the Poverty Depth Index as a response variable with several variables from health and economic indicators showed a very good level of model accuracy. However, SVR is constrained by choosing the right kernel to find the optimum prediction accuracy. So it is necessary to create a user interface that automatically selects the best type of kernel to facilitate the modeling process. The user interface will also help users to use the SVR even if they do not know the programming language. This study aims to: (1) produce a statistical analysis that makes it easier to map the pattern of factors that influence poverty in Indonesia, (2) produce a user interface that makes it easier for users to analyze poverty data in Indonesia. The conclusion obtained from this study is that the most accurate estimation is to use a degree 1 Gaussian kernel (RBF) SVR model while using the Polynomial kernel is not enough to provide a good estimate.
CATEGORIC DATA GROUPING BY ALGORITHM QUICK ROBUST CLUSTERING USING LINKS (QROCK) (Case Study: Status of Value Addrd Tax Payments at the Samarinda Ulu Primary Tax Office in 2018) Nana Nirwana; Memi Nor Hayati; Syaripuddin Syaripuddin
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.18-27

Abstract

Clustering is a method for finding and grouping data that have similar characteristics (similarity) between one data and another. The method of grouping used in this study is the Qrock Algorithm (Quick Robust Using Links).The Qrock Algorithm has a more efficient method to produce the final cluster when the Rock Algorithm has no link beetwen the clusters.The concept of the Qrock Algorithm basically has the same principles as the Rock Algorithm, except that the Qrock Algorithm classifies objects only based on the neighbors of each object. The purpose of this study was to classify 200 Value Added Tax Payment Status data at the Samarinda Ulu Tax Service Office in 2018. Based on the analysis results, the threshold value ( ) = 0.1; 0.2; 0.3; 0.4; 0, 5 and 0.6 produce 1 cluster while the threshold values ( ) = 0.7; 0.8 and 0.9 produce 56 clusters.
MODEL SEEMINGLY UNRELATED REGRESSION PADA DATA KEMISKINAN JAWA TIMUR MENGGUNAKAN MATRIKS PEMBOBOT QUEEN CONTIGUITY DAN ROOK CONTIGUITY Cika Awani Ayuwida
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika
Publisher : Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.64-68

Abstract

Jawa timur, salah satu provinsi yang memiliki sumbangan cukup tinggi yakni 16% dari pertumbuhan ekonomi nasional, merupakan daerah yang potensial baik dari segi ekonomi maupun geografis. Berdasarkan data Sensus Penduduk tahun 2020 mencapai 4.419,10 ribu jiwa (11,09 persen), bertambah sebesar 363,1 ribu jiwa. Tujuan dari penelitian ini adalah untuk memodelkan Seemingly Unrelated Regression (SUR) terbaik pada data Kemiskinan di Provinsi Jawa Timur dengan menggunakan matrik pembobot queen contiguity dan rook contiguity. Penerapan persamaan regresi dalam sebuah kasus seringkali memiliki keterkaitan dengan persamaan yang lain. Jika sebuah persamaan saling berkaitan dikarenakan error regresinya saling berkorelasi, maka pendekatan yang dapat digunakan adalah Seemmingly Unrelated Regression (SUR).
MODEL SEEMINGLY UNRELATED REGRESSION PADA DATA KEMISKINAN JAWA TIMUR MENGGUNAKAN MATRIKS PEMBOBOT QUEEN CONTIGUITY DAN ROOK CONTIGUITY Cika Awani Ayuwida; Prizka Rismawati Arum; M. Al Haris
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.64-68

Abstract

Jawa timur, salah satu provinsi yang memiliki sumbangan cukup tinggi yakni 16% dari pertumbuhan ekonomi nasional, merupakan daerah yang potensial baik dari segi ekonomi maupun geografis. Berdasarkan data Sensus Penduduk tahun 2020 mencapai 4.419,10 ribu jiwa (11,09 persen), bertambah sebesar 363,1 ribu jiwa. Tujuan dari penelitian ini adalah untuk memodelkan Seemingly Unrelated Regression (SUR) terbaik pada data Kemiskinan di Provinsi Jawa Timur dengan menggunakan matrik pembobot queen contiguity dan rook contiguity. Penerapan persamaan regresi dalam sebuah kasus seringkali memiliki keterkaitan dengan persamaan yang lain. Jika sebuah persamaan saling berkaitan dikarenakan error regresinya saling berkorelasi, maka pendekatan yang dapat digunakan adalah Seemmingly Unrelated Regression (SUR).

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