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Hasih Pratiwi
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Study Program of Statistics, Universitas Sebelas Maret, Surakarta 57126, Indonesia
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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 123 Documents
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Front Matter Vol 6 No 1 (2023) Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 6, No 1 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i1.83587

Abstract

Estimator Cramer Von Mises bagi Parameter Distribusi Kumaraswamy-Lindley Bagus Arya Saputra; Zani Anjani Rafsanjani
Indonesian Journal of Applied Statistics Vol 7, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i1.79911

Abstract

The Kumaraswamy-Lindley (KL) distribution is a combination of the Lindley distribution and the Kumaraswamy distribution. The KL distribution is widely used to examine lifetime data. The importance of the application of the KL distribution in explaining lifetime data makes it necessary to estimate distribution parameters well. Therefore, this research will discuss the Cramer Von Mises Estimator (ECM) for the Kumaraswamy-Lindley distribution parameters. The formula for the ECM is obtained and the simulation is carried out using the same initial parameters with different generation sample sizes. The simulation results show that for the same initial parameters, estimation with a larger sample size has better results.
Modeling and Classification Multicollinear Variables using Multinomial Ridge Logistic Regression Aprroach Giatma Dwijuna Ahadi; Ismaini Zain; Santi Puteri Rahayu
Indonesian Journal of Applied Statistics Vol 8, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i1.85795

Abstract

Multinomial Logistic Regression is a method used to find relationships between nominal or multinomial response variables (Y) with one or more predictor variables. Logistics Regression is a classic method that is often used to solve classification problems. Assumptions on Logistics Regression are models containing multicollinearity. Ridge Logistic Estimator (RLE) is methods to solve multicollinearity cases in Logistic Regression. Wu & Asar proposed a new ridge value that can also reduce bias in parameter estimation. Therefore, this research will discuss about Multinomial Ridge Logistic and selection the best of ridge constant values. The performance test of the ridge value will be applied to the Iris Dataset in R software. The best criteria for improvement ridge constant value by looking at the smallest standard error. The calculation results show that the Wu-Asar approach is the best ridge constant and Wald individual test shows significant results. Based on the result, show that the Wu-Asar Ridge constant value on Multinomial Ridge Logistic Regression are very good performance in estimated smaller standar error. The classification for dataset shows high results with 98% global accuracy.Keywords: multinomial; ridge logistic regression; Wu-Asar; standard error; classification
Analisis Perbandingan Metode Hierarchical dan Non-Hierarchical dalam Pembentukan Cluster Provinsi di Indonesia Berdasarkan Indikator Women Empowerment Pikata Aselnino; Arie Wahyu Wijayanto
Indonesian Journal of Applied Statistics Vol 6, No 1 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i1.68876

Abstract

The focus on improving the quality of women’s live to lessen discrimination and gender inequality is set in the fifth’s goals of SDGs. In Indonesia, the RPJMN 2020-2024 contains measure to improve the contribution of women to equitable development. The Central Bureau of Statistics has developed several indicators related to gender, including Gender Development Index (GDI) an Gender Empowerment Index (GEI), which contain women’s improvement on education and health as well as their participation in economic and political fields. The Ministry of Women’s Empowerment and Child Protection did a quadrant analysis to split Indonesia’s 34 provinces into four categories based solely on GDI and GEI using the national average as a constraint. This study compares the Hierarchical, K-Means, and Fuzzy C-Means method to form number of clusters in Indonesia based on the gender development and empowerment in 2021 in order to complement the quadrant analysis. To choose the number of optimum cluster, Elbow method and Calinski-Harabasz Index were used and the best k value is five. From the validation with Silhoutte Index, K-Means was chosen as the best clustering model.Keywords: clustering; fuzzy; k-means; hierarchical; women empowerment
Perbandingan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) dan Extreme Learning Machine (ELM) pada Peramalan Peredaran Uang Kartal di Indonesia Afita Ulya Pratiwi; Etik Zukhronah; Isnandar Slamet
Indonesian Journal of Applied Statistics Vol 7, No 2 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i2.83128

Abstract

Money is generally accepted as legal tender in fulfilling an obligation. Money circulation is very important to be considered and controlled, to have a positive impact on the economy. Control of money circulation is usually emphasized on the type of cash, which is in the form of paper and metals. One of the ways that can help in controlling cash is by forecasting. This study aims to compare the accuracy of forecasting results on cash circulation data using the SARIMA and ELM methods. The data used is the circulation cash from January 2011 to April 2022. The SARIMA method is a method for forecasting time series data containing seasonality, while the ELM method is a method on artificial neural networks that can do forecasting. The best SARIMA model obtained is SARIMA (1,1,0)(0,1,0)12. The best ELM architecture obtains 12 input layer neurons, 45 hidden layer neurons, and 1 output layer. The measure of forecasting error to determine the best model is using MAPE. The results show that the SARIMA method has a training data MAPE of 2,3270% and testing data of 2,2772%, while the ELM method has a training data MAPE of 4,2548% and testing data of 3,8615%. Therefore, the SARIMA method is better than the ELM method at forecasting the circulation of cash in Indonesia.Keywords: cash; extreme learning machine; seasonal autoregressive integrated moving average.  
Exploring Statistical Power and Mediation Analysis: Understanding the Impact of Antecedent-Mediator-Outcome Relationships Szilárd Nemes
Indonesian Journal of Applied Statistics Vol 8, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i2.93376

Abstract

This paper explores the phenomenon of statistical power stagnation and decline in mediation analysis, specifically focusing on the interplay between the antecedent variable, mediator, and outcome. Mediation analysis is a critical statistical tool used to understand the causal pathways between variables. However, statistical power may not always increase with stronger relationships between the antecedent and mediator, often stagnating or even declining due to variance inflation caused by multicollinearity. We provide a in detail examination of this issue, including key theoretical concepts, the mathematical foundations of variance inflation, and the impact of mediator-antecedent correlations on power. A simulation study further illustrates how varying these correlations affects statistical power, variance estimates, and possible bias in mediation effects. Our findings indicate that while increasing the strength of the relationship between the antecedent and mediator improves mediation detection initially, beyond a certain threshold, it results in inflated variance estimates, leading to decreased precision and power. Variance inflation of the mediated effect is more accentuated than variance inflation of regression coefficients.Keywords: mediation; variance inflation; Sobel test
Klasifikasi Menggunakan Algoritma K-Nearest Neighbor pada Imbalance Class Data dengan SMOTE. (Studi Kasus: Nasabah Bank Perkreditan Rakyat “X”) Salsabilla Rizka Ardhana; Tatik Widiharih; Bagus Arya Saputra
Indonesian Journal of Applied Statistics Vol 6, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i2.79389

Abstract

Rural Banks (Bank Perkreditan Rakyat/BPR) provide financial services to micro-businesses and low repayment communities, especially in rural areas. The main activity of the bank is lending. Customer credit classification is expected to assist BPR in anticipating potential bad loans. K-Nearest Neighbor classify current and potential bad credit status based on customer data from BPR “X” in Central Java in October 2022. K-Nearest Neighbor is effective against a large amount of training data and works based on the nearest neighbor. There is an imbalance class data which causes the classification process to focus more on the majority class. Imbalance class data is handled using Synthetic Minority Oversampling Technique (SMOTE) as an oversampling approach. Classification with the addition of SMOTE can improve the evaluation of classification accuracy, especially G-mean. G-mean is the most comprehensive measurement in term of  accuracy, sensitivity and specificity in evaluating classification performance on imbalance class data. The results of this research were able to increase g-mean to 58.55% and sensitivity to 45.46% by implementing SMOTE. Based on the classification results, it is concluded that K-Nearest Neighbor with SMOTE at k = 19 and a proportion of training data to test data of 70:30 is a more appropriate classification model to use for customer credit status. Keywords: Credit Status; K-Nearest Neighbor; Imbalance Class Data; SMOTE
Analisis Faktor-Faktor Penyebab Inflasi di Indonesia Menggunakan Regresi Ridge, LASSO, dan Elastic-Net Husna Afanyn Khoirunissa; Andreas Rony Wijaya; Bayutama Isnaini; Kiki Ferawati
Indonesian Journal of Applied Statistics Vol 7, No 2 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i2.96921

Abstract

The economic condition of a country can be measured using one of the indicators, the inflation rate. Therefore, the inflation needs to be maintained so that its rate can be controlled. To support this, it is necessary to pay attention to several factors that influence the inflation rate. These factors include the amount of exports, imports, narrow money (M1), broad money (M2), the rupiah exchange rate against the USD, interest rates, rice prices in wholesale trade, farmer exchange rates (NTP), world crude oil prices, bank investment credit, GDP, and foreign exchange reserves. In this study, we analyze the significant factors influencing the inflation rate in Indonesia using the best model of the Ridge regression, LASSO regression, and Elastic-Net methods. In this modeling, the γ and λ values from the three methods are optimized first. The data used in this study consist of inflation data in Indonesia and its factors for 2020-2024, sourced from the BPS. Among the three high-dimensional data methods, the LASSO regression is the best method with the smallest MSE for modeling inflation data in Indonesia. The LASSO regression model produces 8 predictor variables that significantly influence inflation data, i.e., imports, M1, interest rates, and world crude oil prices with positive coefficient signs, as well as rice price variables in wholesale trade, NTP, GDP, and foreign exchange reserves with negative coefficient signs.Keywords: inflation; ridge regression; lasso regression; elastic-net.
Back Matter Vol 6 No 1 (2023) Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 6, No 1 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i1.83588

Abstract

Peramalan Ekspor Migas di Indonesia Menggunakan Pendekatan Seasonal Autoregressive Integrated Moving Average with Exogenous (SARIMAX) Eka Nurhasanah; Yuana Sukmawaty; Maisarah Maisarah
Indonesian Journal of Applied Statistics Vol 7, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i1.84934

Abstract

Based on Republic of Indonesia Law No. 22 of 2001, oil and natural gas are vital commodities that play an important role in the country's economy. However, the export of Indonesian oil and gas has been fluctuating, making it necessary to have a strategic plan to prevent minimal exports in the future. This planning can be initiated by first gathering the necessary information. The aim of this research is to forecast oil and gas exports in Indonesia using the best possible model. The data used include the value and volume of Indonesian oil and gas exports. The method begins with determining the ARIMA model, followed by incorporating seasonal elements. ARIMA and SARIMA modeling will tentatively include exogenous variables. Subsequently, parameter estimation, significance tests, diagnostic tests, and the determination of the best model are performed. The research findings indicate that the best model is SARIMAX (1,1,0)(0,1,1)12. The forecast results show that the value of Indonesia's oil and gas exports will continue to increase until July 2024, followed by a and slow down after that. It is hoped that the government can prepare sufficient supply for export to prevent a deficit during that period.

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