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INDONESIA
Indonesian Journal of Statistics and Its Applications
Published by Institut Pertanian Bogor
ISSN : 25990802     EISSN : 25990802     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics Other
Indonesian Journal of Statistics and Its Applications (eISSN:2599-0802) (formerly named Forum Statistika dan Komputasi), established since 2017, publishes scientific papers in the area of statistical science and the applications. The published papers should be research papers with, but not limited to, the following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education. All papers were reviewed by peer reviewers consisting of experts and academicians across universities and agencies
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 201 Documents
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PENGGEROMBOLAN DERET WAKTU DENGAN PENDEKATAN UKURAN KEMIRIPAN PICCOLO UNTUK PERAMALAN CURAH HUJAN PROVINSI BANTEN Sarah Fadhlia; I Made Sumertajaya; Anik Djuraidah
Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i2.607

Abstract

Time series data modeling can be done by modeling each object one by one. Monthly rainfall data is an example of time series data. The purpose of time series analysis is to find patterns of past data and then forecast the future characteristics of data. The data used in this study is the Banten Province rainfall data which contained 19 rainfall stations. So it will require 19 models to forecast the rainfall data. The pattern of time series data in Banten Province monthly rainfall data in several locations has similarities. So that the similarity of this pattern can be considered in the clusters. In time series clustering, the idea is to investigate the similarity of time series in a cluster. The accuracy of distance similarity size measurements is performed on the generation data generated from 3 models, namely AR (1), AR (2), and AR (3). The piccolo method has an average accuracy of 0.62. While the maharaj method has an average accuracy of 0.41. This means that the Ward hierarchical clustering method using the Piccolo distance approach has a greater accuracy value than the Maharaj distance approach. Furthermore, the Piccolo method can be used as an alternative to the excellent distance method for grouping time series data in case data. The Banten Province rainfall station has 3 optimal clusters. Modeling individual level and cluster level has accuracy values that are not much different.
KAJIAN SIMULASI PERBANDINGAN METODE REGRESI KUADRAT TERKECIL PARSIAL, SUPPORT VECTOR MACHINE, DAN RANDOM FOREST Asep Andri Fauzi; Agus M. Soleh; Anik Djuraidah
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i1.610

Abstract

Highly correlated predictors and nonlinear relationships between response and predictors potentially affected the performance of predictive modeling, especially when using the ordinary least square (OLS) method. The simple technique to solve this problem is by using another method such as Partial Least Square Regression (PLSR), Support Vector Regression with kernel Radial Basis Function (SVR-RBF), and Random Forest Regression (RFR). The purpose of this study is to compare OLS, PLSR, SVR-RBF, and RFR using simulation data. The methods were evaluated by the root mean square error prediction (RMSEP). The result showed that in the linear model, SVR-RBF and RFR have large RMSEP; OLS and PLSR are better than SVR-RBF and RFR, and PLSR provides much more stable prediction than OLS in case of highly correlated predictors and small sample size. In nonlinear data, RFR produced the smallest RMSEP when data contains high correlated predictors.
ANALISIS FAKTOR-FAKTOR YANG MEMENGARUHI EKSPLOITASI PEKERJA ANAK DI INDONESIA MENGGUNAKAN REGRESI LOGISTIK BINER Lissa Octavia Wardana; Liza Kurnia Sari
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i3.616

Abstract

Every child has human rights to grow and develop as a whole, both physically and mentally. The government prohibits employers from employing children to protect children's rights. In reality, children begin to participate in economic activities as workers. The issue of child labor is very close to exploitation. This research aims to find general facts about exploitation on child laborers and to identify variables that influence exploitation on child laborers in Indonesia in 2018. Data of National Social and Economic Survey (Susenas) in 2018 were analyed through binary logistic regression. The result shows that most of child laborers in 2018 are exploited. Provinces with the highest percentage of child laborers exploitation are DKI Jakarta, Banten, and Central Java. Area of residence, child labor sector, gender of child, and education of household head in the category of junior high school, elementary school, or not graduate from school significantly influence the exploitation of child labor. Child laborers who live in urban areas, male, work in the formal sector, and has a household head who graduate from junior high school or elementary school or doesn’t graduate at all are more likely experience exploitation.
GROWTH EXTERNALITIES ON THE ENVIRONMENTAL QUALITY INDEX OF EAST JAVA INDONESIA, SPATIAL ECONOMETRICS MODEL OF STIRPAT Rahma Fitriani; Herman Cahyo Diartho; Septya Hadiningrum
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i1.628

Abstract

East Java has shown strong economic growth, which negatively affects its environmental quality. Analysis of the functional relationship between economic growth and environmental quality is important to direct the growth without further deteriorate the environmental quality in this area. It is assumed that growth produces some externalities on environmental quality. The spread of technological information, economic productivity, population growth or investment, can be the source of the growth externalities. The objective of this study is to test the significance of the involved growth externalities on East Java’s environmental quality. Using spatial data, the externalities are accommodated in a spatial version of the STIRPAT model. It is estimated using per city/regency 2015 data. The analysis indicates that local density, local agricultural productivity, neighboring density, and neighboring mining activity significantly affect the local environmental quality. The latter two are the main sources of the growth externalities.
Handling of Overdispersion in the Poisson Regression Model with Negative Binomial for the Number of New Cases of Leprosy in Java: Penanganan Overdispersi pada Model Regresi Poisson dengan Binomial Negatif untuk Jumlah Kasus Baru Kusta di Jawa Yopi Ariesia Ulfa; Agus M Soleh; Bagus Sartono
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i1p1-13

Abstract

Based on data from the Directorate General of Disease Prevention and Control of the Ministry of Health of the Republic of Indonesia, in 2017, new leprosy cases that emerged on Java Island were the highest in Indonesia compared to the number of events on other islands. The purpose of this study is to compare Poisson regression to a negative binomial regression model to be applied to the data on the number of new cases of leprosy and to find out what explanatory variables have a significant effect on the number of new cases of leprosy in Java. This study's results indicate that a negative binomial regression model can overcome the Poisson regression model's overdispersion. Variables that significantly affect the number of new cases of leprosy based on the results of negative binomial regression modeling are total population, percentage of children under five years who had immunized with BCG, and percentage of the population with sustainable access to clean water.
EVALUASI KINERJA METODE CLUSTER ENSEMBLE DAN LATENT CLASS CLUSTERING PADA PEUBAH CAMPURAN Debora Chrisinta; I Made Sumertajaya; Indahwati Indahwati
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i3.630

Abstract

Most of the traditional clustering algorithms are designed to focus either on numeric data or on categorical data. The collected data in the real-world often contain both numeric and categorical attributes. It is difficult for applying traditional clustering algorithms directly to these kinds of data. So, the paper aims to show the best method based on the cluster ensemble and latent class clustering approach for mixed data. Cluster ensemble is a method to combine different clustering results from two sub-datasets: the categorical and numerical variables. Then, clustering algorithms are designed for numerical and categorical datasets that are employed to produce corresponding clusters. On the other side, latent class clustering is a model-based clustering used for any type of data. The numbers of clusters base on the estimation of the probability model used. The best clustering method recommends LCC, which provides higher accuracy and the smallest standard deviation ratio. However, both LCC and cluster ensemble methods produce evaluation values that are not much different as the application method used potential village data in Bengkulu Province for clustering.
PENGARUH TINDAK KORUPSI TERHADAP KEMISKINAN DI NEGARA-NEGARA ASIA TENGGARA DENGAN MODEL PANEL DATA Aditya Firman Baktiar; Herpanindra Fadhilah; Margareth Dwiyanti Simatupang; Mula Warman; Salsa Vira; Rani Nooraeni
Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i2.634

Abstract

Poverty is still being an issue all over the world. It also happens in Southeast Asia that mostly consists of developing countries that identic with high poverty rates. Countries in the world have tried to eradicate the problem of poverty, it's just that it can be hampered due to the high level of corruption. This study aims to look at suitable models and the relationship between corruption and poverty. The data source in this study is secondary data from ten countries in Southeast Asia from 2015 to 2018. Analysis of the data used in this study is panel data. The result obtained is a panel data regression model that is more suitable for modeling the effect of corruption on poverty in Southeast Asian countries is a fixed effect model. Based on the model, the corruption represented by Corruption Perception Index (CPI) and the poverty represented by Human Development Index (HDI) is directly proportional which means every increase in one unit of CPI will also increase the HDI score by 0.001443 unit.
ON HALF EXPONENTIAL POWER MODEL FOR THE FIRST TIME FAILURE OF POWER DISTRIBUTION TRANSFORMERS IN NIGERIA Akinlolu Olosunde; Rowland Benjamin Ekpo
Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i2.640

Abstract

Transformer failure is a major problem confronting the Nigerian power sector, hindering the transmission and distribution of electric power to various households, institutions, and industries. Many of these transformer developed problem due to the old age of the transformers, overloading, in-availability of technical expertise, poor maintenance culture, manufacturer's faults, just to mention few. The present research focuses on providing half exponential power model for the failure of already installed transformers, with respect to years of installation up to the time of the first failure, using secondary data from the south western part of Nigeria as a case study. The results obtained showed that half exponential power performed better in modeling the first time failure of power transformers. This was possible because of the present of shape parameter which gives flexibility to half exponential power when compared with a half normal distribution.
SOME PROPERTIES OF BETA TRANSMUTED DAGUM DISTRIBUTION WITH APPLICATIONS Ahmed Ali Hurairah; Saeed A. Hassen
Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i2.646

Abstract

In this paper, we introduce a new family of continuous distributions called the beta transmuted Dagum distribution which extends the beta and transmuted familys. The genesis of the beta distribution and transmuted map is used to develop the so-called beta transmuted Dagum (BTD) distribution. The hazard function, moments, moment generating function, quantiles and stress-strength of the beta transmuted Dagum distribution (BTD) are provided and discussed in detail. The method of maximum likelihood estimation is used for estimating the model parameters. A simulation study is carried out to show the performance of the maximum likelihood estimate of parameters of the new distribution. The usefulness of the new model is illustrated through an application to a real data set.
PENGGUNAAN PROPENSITY SCORE STRATIFICATION-SUPPORT VECTOR MACHINE UNTUK MENGESTIMASI EFEK PERLAKUKAN AKTIVITAS OLAHRAGA PADA PENDERITA DIABETES MELITUS Ernawati Ernawati; Bambang Widjanarko Otok; Sutikno Sutikno
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i3.653

Abstract

Randomized Controlled Trial (RCT) is not possible to do in observational studies, mainly in health cases, because it is directly related to human life. Actually, good randomization is needed to make the treatment and control groups have no large differences in the observed variables, so it results from unbiased treatment One alternative method that is increasingly used in statistical analysis in the field of health is the Propensity Score (PS). If the propensity score had estimated using the SVM method and divided into groups of strata that have a similar propensity score, it is known as the Propensity Score Stratification (PSS-SVM). The purpose of the PSS-SVM is to balance the observed variables between the treatment group and the control group by dividing them into several strata groups so that a balanced trend is obtained or the propensity score is called balance. This eliminates the influence of the confounding variables and unbalance of the treatment and control groups and obtain an unbiased estimation of the treatment effect. In this Research, the PSS-Method applied in case of disease complication in patients with Diabetes Mellitus Type 2 at the Regional Public Hospital of Pasuruan with respondents who counted 96 patients. The purpose of using PSS-SVM, in this case, is to reduce the confounding effects (sports activity) that influence disease complications. In strata of 4 reduce the largest bias with the percent bias reduction (PBR) is 86.39% with the smallest standard error is 0.103.

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