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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 1 (2024)" : 8 Documents clear
Prediksi Jumlah Permintaan Darah UTD PMI Kota Pontianak Menggunakan ARIMA-Kalman Filter Lyra Mauditia; Nurfitri Imro'ah; Wirda Andani
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.v7i1.85958

Abstract

Ensuring a sufficient supply of blood is a crucial aspect of providing health services. However, the large demand for blood is sometimes difficult to fulfill for one of the work units in the Indonesian Red Cross (PMI), namely the Blood Transfusion Unit. Therefore, blood demand prediction is needed to assist the blod transfuse unit in preparing sufficient blood stock. This study uses the ARIMA-Kalman Filter model to anticipate the quantity of blood demand for Blood Transfusion Unit PMI. The observations modeled in this study are daily observations of the amount of blood demand with the period January 1 to December 26, 2023 as an in-sample of 360 observations and blood demand for the period 27 to 31 December 2023 which amounted to 5 observations as an out-sample used to evaluate the model. The analysis’s findings indicate that the model obtained for predicting the amount of blood demand is the ARIMA (0,0,2) model, then the model parameters are estimated using Kalman Filter. The model used fulfills the diagnostic test and obtained a MAPE value of 15.021% in predicting out-sample data. Thus it can be concluded that the model used is in the very good category and is suitable for prediction. Furthermore, predictions are made for the next three days on the number of blood requests at Blood Transfusion Unit PMI Pontianak City to help health services prepare blood stocks for patients in need.
Analisis Kemiskinan di Sulawesi Selatan dengan Regresi Nonparametrik Berbasis B-Spline Rafli Setiawan Nasir; Muhammad Agung Wahid; Domi Rico Arung Padang; Anna Islamiyati; Raupong Raupong
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.v7i1.80716

Abstract

Poverty is one of the problems faced by Indonesia, including in the province of South Sulawesi. This study aims to identify and understand the complex relationship between factors influencing poverty in South Sulawesi using nonparametric B-Spline regression. The data used are secondary data obtained from the publication of the Central Statistics Agency in the form of Data and Information on Poverty in Districts/Cities in Indonesia in 2022. The variables used are the percentage of poor people as the dependent variable, and the percentage of per capita expenditure on food, poverty depth index, and poverty severity index as independent variables. The best B-Spline model was obtained using order 2 for each independent variable, and one knot for each independent variable at a certain point. This model provides a Generalized Cross-Validation (GCV) value of 10.199728. The results of the analysis show that the measure of the goodness of the model obtained or R2 which means that the percentage of per capita expenditure on food, poverty depth index, and poverty severity index greatly influences the percentage of poor people in South Sulawesi. The relationship between the independent variables and the dependent variables is non-linear and varies. The B-Spline model can produce an accurate and flexible picture of the relationship pattern and variability in poverty data in South Sulawesi. This study can provide in-depth insights and recommendations for the government in the form of poverty alleviation policies based on local data and non-linear analysis, by targeting specific interventions according to the unique conditions of each region in South Sulawesi to increase the effectiveness of poverty alleviation.
Forecasting on Closing Stock Price Data Using Fuzzy Time Series Sri Subanti; Asti Rahmaningrum
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.v7i1.54309

Abstract

The stock prices move up and down during trading time which is obtained from time series data. Investors need to estimate the fluctuation of stock prices in the future day to make the best investment decision. Fuzzy time series can be used as an alternative by investors in making stock price predictions. The advantage of this forecasting method compared to others is that it can formulate a problem based on expert knowledge or empirical data. This research aims to apply fuzzy time series in estimating the future value of closing stock price on the LQ45 Index. Three different methods will be applied to the data which are Chen, Lee, and Cheng. The data of the LQ45 Index will be obtained during the period of January, 4th until April 30th, 2021. The LQ45 index is chosen by many investors because it has high returns. All three model were applied and has a different rule in the calculation stage. The results show that all three models give different forecasting values and different performance of accuracy. The Lee method has the lowest values of accuracy, meanwhile the Cheng method has the highest value of accuracy. It can be concluded that Lee method is the best model indicated by the lowest value of RMSE, MAD, and MAPE for estimating the closing stock price of the LQ45 index.
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.
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.
Pemodelan Data Kemiskinan di Pulau Sumatera dengan Regresi Multilevel Spline Linear Truncated Muhammad Ridzky Davala; Nurul Mutiara Annisa; Siswanto Siswanto; Anisa Kalondeng
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.v7i1.80768

Abstract

Poverty is one of the world's biggest challenges that is still a problem, both in developing and developed countries, including Indonesia. Around 27.5 million people live below the national poverty line in Indonesia. Because it is the largest archipelago, poverty problems in each region also vary, including on the Sumatra Island. One of the efforts to alleviate poverty can be done through identifying factors that affect the percentage of poor population using truncated linear spline multilevel regression model. Multilevel modeling is a statistical approach specifically used to analyze data with a two-level structure. This approach allows an understanding of the contribution of individual and group-level factors to the response variable. The predictor variables considered are per capita expenditure, open unemployment rate, and human development index at the district/city level (level-1), as well as population growth rate and economic growth rate at the provincial level (level-2). The results of this study show that the best multilevel regression model at level-1 uses three knot points, while at level-2 it uses two knot points. The factors that affect PPM in Sumatra Island in 2021 at level-1 are per capita expenditure and at level-2 are population growth rate and economic growth rate. The factors that affect percentage of poor population in Sumatra Island in 2021 are expected to provide a more in-depth view of the socio-economic conditions on the island of Sumatra.
Analisis Autoregressive Integrated Moving Average (ARIMA) dengan Intervensi Double Input pada Prediksi Harga Saham Gita Arinda Maulidya; Neva Satyahadewi; Nur'ainul Miftahul Huda
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.v7i1.85229

Abstract

Intervention analysis is the time series analysis used in a time series model that experiences an intervention event. Intervention is an event that can cause time series data to change patterns caused by external or internal factors such as changes in government policy, advertising promotions, environmental regulations, and others. This research uses the ARIMA analysis method of double input step function intervention with daily data on the closing share prices of PT Adaro Energy Indonesia for the period 7 March 2022 to 7 March 2023 because in that period there are two points that are thought to be interventions that have an impact on changes in the ADRO’s share prices over a long period of time. The aim of this research is to analyze the intervention ARIMA model and predict the closing price of PT Adaro Energy Indonesia for the next five-days period. The ARIMA analysis steps are based on the ARIMA model through the process of stationarity data (variance and mean), order identification, parameter estimation, and diagnostic examination. The best ARIMA model used to predict ADRO's closing share price is the ARIMA (2,1,2) model, which is obtained based on the smallest AIC, MAPE, and RMSE values. The prediction results in this research show that the predictions produced for the next five-days period are classified as very good because they have a MAPE value on training data of 1,96% and a MAPE value on testing data of 1,74%.
Comparing Monthly Rainfall Prediction in West Sumatra Using SARIMA, ETS, LSTM, and XGBoosting Methods Fadhil Muhammad Aslam; Fadhli Aslama Afghani
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.v7i1.83187

Abstract

The West Sumatra Province, serving as the trading center on the island of Sumatra, and boasting various attractive tourist destinations, is not immune to incidents of high precipitation leading to hydro-meteorological disasters such as floods and landslides. Therefore, the accurate prediction of monthly rainfall is crucial to minimize the impacts of high precipitation. This research aims to determine the best method for predicting monthly rainfall using data from 1992 to 2022, which can adequately represent its climatological conditions. The results indicate that the Extreme Gradient Boosting method outperforms the Seasonal Autoregressive Integrated Moving Average (SARIMA), Exponential Smoothing (ETS), and Long Short-Term Memory (LSTM) methods in West Sumatra Province, represented by three weather observation points from the BMKG (Climatology Station of West Sumatra, Maritime Meteorology Station of Teluk Bayur, and Minangkabau Meteorology Station). This method exhibits the lowest error values and the strongest correlation between predicted and actual data. This is evident from the Nash-Sutcliffe Efficiency (NSE) values, which are 0.188214535, 0.613823746, and 0.545734162 (unsatisfactory-satisfactory), as well as the obtained correlation values of 0.472103386, 0.795586268, and 0.743002591 (moderate-strong). However, this method is unable to perfectly capture outlier values. These outliers arise as a result of unusual conditions, such as natural disasters or climate changes, and atmospheric phenomena like El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), leading to exceptionally high or low precipitation.

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