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Hasih Pratiwi
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hpratiwi@mipa.uns.ac.id
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ijas@mipa.uns.ac.id
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Study Program of Statistics, Universitas Sebelas Maret, Surakarta 57126, Indonesia
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Kota surakarta,
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INDONESIA
Indonesian Journal of Applied Statistics
Published by Universitas Sebelas Maret
ISSN : -     EISSN : 2621086X     DOI : https://doi.org/10.13057/ijas
Core Subject : Economy, Science, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science
Indonesian Journal of Applied Statistics (IJAS) is a journal published by Study Program of Statistics, Universitas Sebelas Maret, Surakarta, Indonesia. This journal is published twice every year, in May and November. The editors receive scientific papers on the results of research, scientific studies, and problem solving research using statistical method. Received papers will be reviewed to assess the substance of the material feasibility and technical writing.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 77 Documents
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Back Matter Vol 1 No 1 Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v1i1.24686

Abstract

Pemodelan Indeks Pembangunan Manusia (IPM) Metode Baru Menurut Provinsi Tahun 2015 Menggunakan Geographically Weighted Regression (GWR) Akbar Maulana; Renny Meilawati; Vita Widiastuti
Indonesian Journal of Applied Statistics Vol 2, No 1 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i1.26170

Abstract

The Human Development Index (HDI) is a parameter of quality of life for an area. The HDI explains how residents can access the results of development in obtaining income, health and education. One method that can be used to find out the factors that influence the human development index in modeling is regression analysis of ordinary least square (OLS). In the Human Development Index data, there is a dependency between measuring data and the location of a region. Therefore, spatial regression analysis can be used in this study. The local form of spatial regression analysis is geographically weighted regression (GWR). GWR shows the existence of spatial heterogeneity (location). This study compares between OLS regression and GWR in the new human development index method by province in 2015. In the GWR model we use fixed Gaussian kernel and kernel fixed bisquare as weighted function. The optimal bandwidth value is obtained by minimizing the cross validation (CV) and Akaike information criterion (AIC) coefficients. The results showed that the GWR model with Gaussian kernel function is better than GWR with bisquare kernel function and OLS model.Keywords: human development index, ordinary least square, geographically weighted regression, kernel fixed Gaussian,  kernel fixed bisquare
Pendekatan Model Nonparametrik untuk Memodelkan Hubungan Antara Jumlah Uang Beredar dan Indeks Harga Konsumen di Indonesia Tahun 1969-2017 Pardomuan Robinson Sihombing
Indonesian Journal of Applied Statistics Vol 3, No 1 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v3i1.41093

Abstract

Inflation is one of the macroeconomic variables of concern to the government in addition to economic growth, unemployment and poverty. Inflation is measured by the Consumer Price Index (CPI). According to the quantity theory of the classics, argues that the price level is determined by the amount of money in circulation, prices will rise if there is an increase in the money supply, assuming the amount of goods offered is fixed, while the amount of money is doubled, sooner or later the price will doubled. Often the relationship between macroeconomic variables is not always linear, it can be exponential, logarithmic, or highly fluctuating patterns. This nonlinear relationship cannot be forced using parametric regression which generally uses the Ordinary Least Square (OLS) or Maximum Likelihood Estimation (MLE) which often implies the existence of certain distributions and linear data patterns. In some literatures, researches using a linear model with OLS, for describing the relationship between CPI and money supply. This research uses several non parametric approaches, namely kernel and spline functions. The results obtained are a strong positive relationship between money supply and CPI, where money supply has a significantly positive effect on CPI. The most suitable non parametric method to describe the relationship pattern between CPI and money supply is the smoothing spline method with Generalized Cross Validation (GCV) parameter optimization method with the smallest RMSE and MAPE criteria and functions that can follow data patterns smoothly.Keywords: CPI, money supply, non parametric, kernel, spline.
Bootstrap Residual Ensemble Methods for Estimation of Standard Error of Parameter Logistic Regression To Hypercolesterolemia Patient Data In Health Laboratory Yogyakarta Fransiska Grace S.W.; Sri Sulistijowati Handajani; Titin Sri Martini
Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v1i1.24086

Abstract

Logistic regression is one of regression analysis to determine the relationship between response variable that have two possible values and some predictor variables. The method used to estimate logistic regression parameters is the maximum likelihood estimation (MLE) method. This method will produce a good estimate of the parameters if the estimation results have a small standard error.In a research, the characteristics of good data must be representative of the population. If the samples taken in small size they will cause a large standard error value. Bootstrap is a resampling method that can be used to obtain a good estimate based on small data samples. Small data will be resampling so it can represent the population to obtain minimum standard error. Previous studies have discussed resampling bootstrap on residuals as much as b times. In this research we will be analyzed resampling bootstrap on the error added to the dependent variable and take the average parameter estimation ensemble logistic regression model resampling result. Next we calculate the standard value error logistic regression parameters bootstrap results.This method is applied to the hypercholesterolemic patient status data in Health Laboratory Yogyakarta and after bootstrapping, the standard error produced is smaller than before the bootstrap resampling.Keywords : logistic regression, standard error, bootstrap resampling, parameter estimation ensemble
Analisis Cluster Intensitas Kebencanaan di Indonesia Menggunakan Metode K-Means Hafiz Yusuf Heraldi; Nabila Churin Aprilia; Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 2, No 2 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i2.34911

Abstract

Indonesia is one of the most prone countries to natural disasters in the world because of the climate, soil, hydrology, geology, and geomorphology. There are many different natural disasters, but the three most common natural disasters in Indonesia are flood, landslide, and tornado. This research aimed to cluster the provinces in Indonesia based on the flood, landslide, and tornado’s intensity in 2018. The results of clustering by K-Means method in this research divided the provinces in Indonesia into four clusters. The second cluster contained West Java, Central Java, and Bali, the third cluster contained DKI Jakarta, the fourth cluster contained DI Yogyakarta, and the first cluster contained the other 29 provinces. The result of this research hopefully can help the government in order to make decision and improve the natural disaster management system, such as preparedness, disaster response, and disaster recovery based on the most common disaster in each province. Furthermore, the society is expected to be more aware on natural disaster management based on the most common natural disaster in province that they lived.Keywords : natural disaster, cluster, k-means
Model Penyebaran Penyakit SIR Tipe Rantai Binomial dengan Kontak Random dan Waktu Penyembuhan Bernilai Tak Hingga Ilham Asyifa Maulana Rosyid; Respatiwulan Respatiwulan; Sri Sulistijowati Handajani
Indonesian Journal of Applied Statistics Vol 3, No 2 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v3i2.44307

Abstract

Susceptible-Infected-Recovered (SIR) epidemic model is an epidemic model that illustrates the pattern of disease spread with the characteristics of individuals who have recovered cannot be re-infected and have a permanent immune system. The binomial chain type epidemic model assumes that infection spreads in discrete time units and the number of the infected individuals follows a binomial distribution. This research aims to discuss  binomial chain type SIR epidemic model by simulating the model. The transition probability depends on  the number of infected individuals in the period   the number of individuals encountered, and  the transmission probability. This model also assumes an infinite recovery time ( = ∞). This situation illustrates that infected individuals remain contagious during the period of spread of the disease. This situation can arise when the causative agent of the disease has a long life. Then simulations are performed by giving different transmission probability  The results show that the greater transmission probability will cause the probability of a new individual being infected in the next period to be greater.Keywords : SIR epidemic model, binomial chain, infinite recovery time
Uji Asumsi Proportional Hazard pada Faktor yang Mempengaruhi Waktu Tahan Hidup Pasien Kanker Paru Elnatan Dimas Aditya; Sri Sulistijowati Handajani; Ririn Setiyowati
Indonesian Journal of Applied Statistics Vol 1, No 2 (2018)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v1i2.26496

Abstract

Lung cancer is the disease that its death risk always increase, because of that the survival time of its patient is interesting to be researched. One of the method that can be used to research survival time of lung cancer patient is Cox regression. It has an assumption that called proportional hazard assumption. Proportional hazard assumption can be tested by graph method that is log-log graph, but the result is only used as temporary suspicion. For a better result, the goodness of fit test can be used by calculate the correlation between rank of survival time and schoenfeld residual. The result is age variabel doesn’t satisfy proportional hazard assumption. Keywords : cox regression; proportional hazard assumption; log-log graph; goodness of fit test.
Analisis Pro-poor Growth Melalui Identifikasi Pengaruh Pertumbuhan Ekonomi Terhadap Ketimpangan Pendapatan dan Kemiskinan Di Indonesia Tahun 2010-2015 Azka Muthia
Indonesian Journal of Applied Statistics Vol 2, No 2 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i2.34915

Abstract

Economic growth, income distribution inequality, and poverty should have interdependent relationships with one another. In 2010 to 2015, Indonesia experienced a decrease in poverty but its economic growth slowed and the Gini ratio was stagnant. Therefore the author conducted research to analyze the influence of economic growth and income disparity on poverty eradication in Indonesia to find out the level of economic growth influence whether it is pro-poor or anti-poor and to find out the sectors influencing the poverty eradication. The panel data obtained from 33 provinces in Indonesia from 2010 to 2015 were analyzed. The result of this study showed that the economic growth had negative influence on poverty level. Based on the influence of elasticity value of net poverty on the economic growth, the economic growth can minimize the poverty. The economic growth in Indonesia for 2010-2015 was pro poor, but the value of gross elasticity and net poverty on Indonesia's economic growth is less elastic. As a result, poverty reduction driven by economic growth was not too large.Keywords : pro poor growth, panel regression analysis, poverty
Pemetaan Risiko Penyakit Tuberkulosis (TBC) di Kota Surakarta dengan Spatial Empirical Bayes Husna Afanyn Khoirunissa
Indonesian Journal of Applied Statistics Vol 3, No 2 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v3i2.41282

Abstract

Tuberculosis is an infectious disease that can attack human with a poor immune system. In 2017, there were 723 residents of Surakarta tested positive for tuberculosis. The spatial empirical Bayes method is a good method for mapping the risk of tuberculosis because this method includes spatial dependency information and can overcome small area problems. This method can help the prevention of tuberculosis in Surakarta. In the analysis, it was found that the number of cases of tuberculosis in Surakarta has a spatial dependency that has an impact of the spread of tuberculosis. Sub-district classification with the highest risk value is Jebres, Tegalharjo, Jajar, Laweyan, Sondakan, Purwosari, Mangkubumen, Keratonan, Timuran, and Punggawan.Keywords : tuberculosis, mapping, spatial empirical Bayes, Surakarta
Peramalan Banyaknya Pengunjung Pantai Glagah Menggunakan Metode Autoregressive Integrated Moving Average Exogenous (ARIMAX) dengan Efek Variasi Kalender Solikhah Novita Intan; Etik Zukhronah; Supriyadi Wibowo
Indonesian Journal of Applied Statistics Vol 1, No 2 (2018)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v1i2.26298

Abstract

Glagah Beach is one of the tourist destinations in Kulon Progo Regency, Yogyakarta which is the most visited by tourists. Glagah Beach visitors data show  that in the month of Eid Al-Fitr there was a significant increase. This shows that there is an effect of the calendar variation of Eid al-Fitr. Therefore, it is needed a method that can be used to analyze time series data which contains effects of calendar variations, that is ARIMAX method. The aim of this study are to find the best ARIMAX model and to predict the number of visitors to Glagah Beach in the future. The result shows that the best ARIMAX model was ARIMAX([24],0,0). Forecasting from January to September 2016 are 37211, 21306, 26247, 24148, 28402, 29309, 81724, 26029, and 23688 visitors. Keywords: Glagah Beach; variation of calendar; Eid al-Fitr; ARIMAX.

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