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All Journal UNEJ e-Proceeding Indonesian Journal of Applied Statistics
Isnandar Slamet
Program Studi Statistika, Universitas Sebelas Maret, Surakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science

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PENGELOMPOKAN NEGARA BERDASARKAN KASUS STUNTING DENGAN MODEL FINITE MIXTURE NORMAL MENGGUNAKAN PENDEKATAN BAYESIAN Adella Okky Herashanti; Irwan Susanto; Isnandar Slamet
UNEJ e-Proceeding 2022: E-Prosiding Seminar Nasional Matematika, Geometri, Statistika, dan Komputasi (SeNa-MaGeStiK)
Publisher : UPT Penerbitan Universitas Jember

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Abstract

Stunting (short stature) is a chronic condition characterized by stunted growth due to malnutrition over a long period of time. Stunting needs special attention because it can hamper children's physical and mental development and is associated with an increased risk of illness and death. The high incidence of stunting in the world is still a health problem that is still being faced by WHO [7]. Therefore, it is necessary to group countries based on stunting cases in order to facilitate appropriate policy making in overcoming and preventing stunting. In this study, 151 countries were grouped based on stunting cases with a normal finite mixture model. The model is estimated using Bayesian estimation through Birth-Death Markov Chain Monte Carlo (BD-MCMC) method. The results of the study show that there are five groups of countries based on stunting cases, where the first group consists of 30 countries and the second group consists of 32 countries which are groups with low stunting rates. While the third group has 33 members, the fourth group has 34 countries and the fifth group has 22 countries which is a group with a high stunting rate. Keywords: BD-MCMC, Finite Mixture, Pengelompokan, Stunting.
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.  
Co-Authors Adella Okky Herashanti Afita Ulya Pratiwi Etik Zukhronah Irwan Susanto