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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 8 Documents
 1 
Search results for , issue "Vol 7, No 2 (2024)" : 8 Documents clear
Analisis Pertumbuhan Ekonomi Provinsi Jawa Timur Beserta Faktor-Faktornya Menggunakan Model Robust Geographically Weighted Regression-Pendugaan M Halimah Nur Mushaharah; Rosita Kusumawati; Bayutama Isnaini
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.v7i2.90381

Abstract

Economic growth is a successfull benchmark of nation-building and plays a role in a nation’ welfare. Economic growth in districts/cities measured by gross regional domestic product at constant prices (GRDP constant prices). East Java is the second province after DKI Jakarta as the biggest contributor to the Indonesian economy in 2022 with  economic growth rate values calculated by GRDP constant prices. However, East Java’s economic growth has gaps. As 23 of 38 districts/cities in East Java have economic growth values lower than national economic growth. Glaring gaps could be identified as an outlier and it could happen by many factors. This study aims to model economic growth and its factors with a robust analysis model against an outlier called robust geographically weighted regression-M estimation (RGWR-M). This study used secondary data from BPS Provinsi Jawa Timur and KEMENKEU’s SIKD data portal namely HDI, public health center, population density, economic activity, capital expenditure, and GRDP constant prices in East Java Province in 2022. The result showed that RGWR-M is the best model when an outlier is detected compared to OLS and GWR in analyzing factors that are suspected to affect economic growth in East Java Province in 2022 with  MAD’s values. RGWR-M model produces six mapping groups of significantly influential variables and one group of uninfluential variables. The groups formed to have different parameter estimation results in each district/cities.Keywords: GRDP constant prices; outlier; RGWR-M.
Analisis Dampak Kebijakan Tarif Safeguard terhadap Impor Kain Tenun dari Kapas di Indonesia Tahun 2008-2022 dengan Pendekatan ARIMA Intervention Model Khoirunisa Maula Izzaty; Siskarossa Ika Oktora
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.v7i2.85689

Abstract

The increase in the price of cotton raw materials as well as the low quality of products and productivity of the domestic industry has increased imports of woven fabric from cotton in Indonesia. The increase in imports resulted in serious losses to the domestic industry. Indonesian Textile Association submitted a petition to investigate imports of cotton woven fabric and resulting in a decision to implement a safeguard tariff policy on imports of cotton woven fabric. This research aims to analyze the impact of safeguard tariffs and the elimination of safeguard tariffs on the volume of imports of cotton woven fabrics. The method used is ARIMA multi-input intervention with interventions in the form of a safeguard tariff policy for 2011-2014, elimination of safeguard tariffs for 2014-2020, and a safeguard tariff policy for 2020-2022. The research results show that the safeguard policy in 2011-2014 and 2020-2020 had an impact on reducing the volume of imports of woven cotton fabrics. Meanwhile, the elimination of the safeguard policy in 2014-2020 had the impact of increasing the volume of imports of cotton woven fabrics. The results of this research can be used as material for the government's evaluation regarding safeguard tariff policies in protecting the domestic cotton woven fabric industry. Keywords: ARIMA intervention, cotton, woven fabric
Analisis Kualitas Tidur Penduduk Usia Produktif di Indonesia dengan Model Regresi Logistik Ordinal Nadiya Azhar Mufid; Kismiantini Kismiantini
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.v7i2.86659

Abstract

The prevalence of poor sleep quality in the productive age population in Indonesia is quite high. This study aims to analyze factors that influence the sleep quality level of the productive age population in Indonesia by an ordinal logistic regression model based on the Fifth Indonesian Family Life Survey (IFLS5). In this study, the response variable used is sleep quality with an ordinal scale of 5 categories and 12 predictor variables with 1 continuous predictor variable that is age and 11 categorical predictors including education, job status, smoking habit, health, gender, marital status, physical activity, religious, depression level, life satisfaction, and economic level with data of 28.743 respondents. The results of this study indicated that the ordinal logistic regression model with proportional odds model was more suitable to be used to analyze the sleep quality level of productive age population in Indonesia than non-proportional odds model. Based on the analysis result, it was found that among 12 predictor variables, variables that had a significant effect on sleep quality level were education, job status, smoking habit, health, age, depression level, life satisfaction, and economic.Keywords: IFLS, sleep quality, ordinal regression, productive age population 
Application of the Mixed Geographically Weighted Regression Model to Identify Influencing Factors for Literacy Development Index of Indonesian Society's in 2022 Zulhijrah Zulhijrah; Ruliana Ruliana; Aswi Aswi
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.v7i2.88784

Abstract

The mixed geographically weighted regression (MGWR) method is a combination of a linear regression model and a geographically weighted regression (GWR) model. The MGWR model can produce parameter estimates that have global parameter estimates, and other parameters that have local parameters according to the observation location. This method can be used in epidemiological studies that are influenced by spatial heterogeneity. The aim of this research is to determine and model the factors that influence the Community Literacy Development Index (CLDI) in Indonesia based on MGWR modeling. The data used in this research is CLDI data in Indonesia in 2022 along with the factors that are thought to influence it. The results of this research indicate that the MGWR model outperforms both the linear regression and GWR models, as it yields the lowest Akaike information criterion (AIC) value and an ?² value of 96.54%. Based on the modeling results, several factors influencing CLDI were identified, including the percentage of libraries, the adequacy ratio of library collections, the average length of schooling, and the level of participation in organized learning. Keywords: Literacy; literacy development index; mixed geographically weighted regression; spatial
Grouping Indonesian Province Farmers’ Term of Trade Using Dynamic Time Warping Imtikhanah Anis Mahmudiati; Rohmatul Fajriyah
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.v7i2.94456

Abstract

This study employs dynamic time warping (DTW) to analyze the farmer’s terms of trade (FTT) across 34 provinces in Indonesia, aiming to identify patterns and cluster similarities in time series data. DTW is recognized for its effectiveness in measuring flexible similarities under time distortions, making it particularly suitable for time series classification across various fields. The FTT is utilized to assess farmers' purchasing power by comparing the prices they receive for their products to the prices they pay for goods and services. K-Medoid clustering techniques were applied to group provinces based on their DTW distances, revealing three distinct clusters. The silhouette score indicates that three clusters as the optimum cluster for the FTT data. The findings show that the first and third clusters have low mean of FTT and the second cluster has the highest mean FTT. These indicates disparities in farmers’ income and purchasing power across regions where the government needs to enhance agricultural strategies and improve economic conditions for farmers in the first and third clusters.Keywords: Clustering; Dynamic Time Warping; Farmers Term of Trade; K-Medoid.
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.  
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.
Analisis Spasial Kemiskinan di Pulau Jawa Tahun 2022 dengan Metode Geographically Weighted Regression (GWR) Wisly Ryan Eliezer
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.v7i2.87342

Abstract

Poverty is a multifaceted problem that poses a challenge for developing countries across the world, including Indonesia. Poverty is one of the global and national obligations stated in the first Sustainable Development Goals (SDGs), namely "Without Poverty". This study seeks to examine the factors that determine poverty in Java in 2022 while accounting for regional effects. Geographically Weighted Regression (GWR) is the methodology employed. The findings revealed that geographic/spatial characteristics had a substantial impact on poverty rates, and the GWR model generated a more accurate assessment measure than the global model. The structure of the parameters varies by location, with the variable coefficients of the health, salary, and credit indicators fluctuating, whilst the coefficients of the Education and Inflation Indicators remain similar throughout districts and cities. The government must implement proper measures to eliminate poverty not just nationally, but also in each district/city in Indonesia, particularly on Java Island. Policies might include improving human resources in education and health, monetary policies to sustain market pricing and determine minimum salaries, and policies to infuse credit assistance money into people's companies.Keywords: poverty, Geographically Weighted Regression (GWR), spatial analysis

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