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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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Jawa tengah
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 8 Documents
 1 
Search results for , issue "Vol 3, No 1 (2020)" : 8 Documents clear
Laboratory Clustering using K-Means, K-Medoids, and Model-Based Clustering Niswatul Qona'ah; Alvita Rachma Devi; I Made Gde Meranggi Dana
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.v3i1.40823

Abstract

Institut Teknologi Sepuluh Nopember (ITS) is an institute which has about 100 laboratories to support some academic activity like teaching, research and society service. This study is clustering the laboratory in ITS based on the productivity of laboratory in carrying out its function. The methods used to group laboratory are K-Means, K-Medoids, and Model-Based Clustering. K-Means and K-Medoids are non-hierarchy clustering method that the number of cluster can be given at first. The difference of them is datapoints that selected by K-Medoids as centers (medoids or exemplars) and mean for K-Means. Whereas, Model-Based Clustering is a clustering method that takes into account statistical models. This statistical method is quite developed and considered to have advantages over other classical method. Comparison of each cluster method using Integrated Convergent Divergent Random (ICDR). The best method based on ICDR is Model-Based Clustering.Keywords : K-Means, K-Medoids, Laboratory, Model-Based Clustering
Estimating Bark Eating Caterpillars Indarbela quadrinotata (walker) in Populus deltoides Using Ranked Set Sampling Arvind Kumar; Girish Chandra; Sanjay Kumar
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.v3i1.35370

Abstract

The problem of bark eating caterpillar, Indarbela quadrinotata infestation has been observed from variety of horticulture and forest tree species in India. The estimation of infestation of this caterpillar using conventional sampling methods was found difficult because counting the number of caterpillar in each tree is practically not feasible. Ranked set sampling (RSS) is a cost efficient method which provides improved estimators of mean and variance when actual measurement of the observations is difficult to obtain but a reasonable ranking of the units in the sample is relatively easy. In the present study, poplar, Populus deltoides plantation of Western Uttar Pradesh and Uttarakhand was taken for the assessment of Indarbela quadrinotata infestation. The RSS estimator of population mean and variance have been discussed and compared with the corresponding estimators from simple random sampling (SRS). The relative precision (RP) of RSS procedure with respect to the SRS for four different set sizes of k = 3, 5, 7, and 10 has been deliberated. It was seen that RP increase with the increment in k. The method of RSS was found suitable for the assessment of insect pest infestation.Keywords: Indarbela quadrinotata, Populus deltoides, simple random sampling, ranked set sample, order statistics.
Front Matter Vol 3 No 1 2020 Hasih Pratiwi
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.v3i1.43192

Abstract

Analisis Situasi Pembangunan Manusia di Jawa Tengah Laeli Sugiyono
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.v3i1.39910

Abstract

This study aims to analyze the disclosure distribution of the position regency/city in Central Java based on the linkage of Economic Growth (EG) and Human Development Index (HDI). The study uses secondary data in the form of cross-sectional regional regency/city based on EG and HDI components. Data analysis uses regency/city distribution plot diagram based on EG and HDI components in the Cartesian diagram which divides the space into 4 Quadrants, namely: Quadrant I of the regency/city distribution plots with high EG and HDI, Quadrant II of the regency/city distribution plots with low EG and high HDI, Quadrant III of the regency/city distribution plots with high EG and low HDI, and Quadrant IV of the regency/city distribution plots with low EG and HDI. This study concludes that the position of cities in Central Java in general is in line with the Quadrant I group, the HDI of regency/city in the area of the ex-Semarang and ex-Surakarta residency is in Quadrant I. Other regencies/cities are spread in Quadrant II, III, and IV.Keywords : human development index, economic growth, Central Java, distribution plot 
Back Matter Vol 3 No 1 2020 Hasih Pratiwi
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.v3i1.43193

Abstract

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.
Application of K-Means Clustering in Mapping of Central Java Crime Area Retno Tri Vulandari; Wawan Laksito Yuly Saptomo; Danar Wijaya Aditama
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.v3i1.40984

Abstract

Crimes occur in many places and cause complex problems that have widespread impacts on all levels of society. Crime is related to several factors including crime index, the ratio of the number of police to the population, population density and poverty rates. In this study trying to develop an information system that is able to display and map crime-prone areas in Central Java. Based on these factors, it is used to classify regions in Central Java, namely the category of safe, quite vulnerable, vulnerable and very vulnerable. K-Means clustering method, is very suitable to be used in predicting and grouping which areas are included in the 4 categories. The formulation of the problem is to find out areas prone to crime in Central Java. Based on the results, there are 11 regions with safe categories, 4 areas with quite vulnerable categories, 13 regions with vulnerable categories and 6 regions with very vulnerable categories.Keywords : K-Means clustering, mapping, Central Java,  criminality, crime area.
Pemodelan Indeks Keparahan Kemiskinan di Indonesia Menggunakan Analisis Regresi Robust Melva Hilda Stephanie Situmorang; Yuliana Susanti
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.v3i1.40838

Abstract

Poverty is one indicator to see the success of development in a country. The Poverty Severity Index can be used as one measure of the magnitude of poverty in an area. In the Poverty Severity Index data in Indonesia, in 2018 there were some outliers, so to analyze it used robust regression. The purpose of this study is to determine the significant factors on the Poverty Severity Index in Indonesia using robust regression with the M-estimation method. The results showed that the Poverty Severity Index model in Indonesia using robust regression was influenced by Gini Ratio, Percentage of Poor Population, and Pure Participation Rate with R-square = 94,8%.Keywords: Poverty Severity Index, robust regression.

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