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Sciencestatistics: Journal of Statistics, Probability, and Its Application
Published by Universitas Muhammadiyah Metro
ISSN : 29642884     EISSN : 29639875     DOI : https://doi.org/10.24127
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
Sciencestatistics: Journal of Statistics, Probability, and Its Application is an Open Access journal in the field of statistical inference, experimental design and analysis, survey methods and analysis, research operations, data mining, statistical modeling, statistical updating, time series and econometrics, multivariate analysis, statistics education, simulation and modeling, numerical analysis, algebra, combinatorics, and applied mathematics.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 38 Documents
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Application of the Double Exponential Smoothing Brown Method in Forecasting the Number of Poor Population Intan Utami; Ana Istiqomah; Sangidatus Sholiha
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 4 No. 1 (2026): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v4i1.11069

Abstract

Poverty is a socio-economic issue that requires policy planning based on accurate forecasting. This study uses the Brown Double Exponential Smoothing method to forecast the number of poor people in Metro City for the period 2026-2030, using data from 2005-2025 obtained from BPS. The analysis was conducted using a trial and error method for the alpha (α) parameter ranging from 0.1 to 0.9 based on the smallest MAD, MSE, and MAPE values. The study results indicate that the optimal alpha parameter is α = 0.5 with a MAPE of 15.91231%, which indicates good accuracy. The forecast shows an increasing trend from 321.90 thousand people (2026) to 338.87 thousand people (2030), with an average increase of 4.24 thousand people per year. The results of this study can be used as a basis for planning poverty alleviation programs in Metro City.
Modeling and Predicting Indonesia’s Inflation Using the ARIMA Model Linda Rassiyanti; Rohmi Dyah Astuti; Yuliana
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 4 No. 1 (2026): JANUARY
Publisher : Universitas Muhammadiyah Metro

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Abstract

Inflation is one of the most important macroeconomic indicators used to evaluate the stability and performance of a country's economy. This study aims to model and predict Indonesia’s monthly inflation rate using the Autoregressive Integrated Moving Average (ARIMA) approach. The dataset consists of monthly inflation observations from January 2010 to December 2025 obtained from Bank Indonesia. The analysis begins with testing the stationarity of the series using the Augmented Dickey–Fuller (ADF) test, followed by model identification through the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots. Several candidate models are estimated, including ARIMA (0,1,1), ARIMA (1,1,0), and ARIMA (1,1,1). Model comparison based on the Akaike Information Criterion (AIC) indicates that the ARIMA (0,1,1) model provides the lowest AIC value and is therefore selected as the most appropriate model. The forecasting results suggest that Indonesia’s inflation rate is expected to remain relatively stable at around 3.63% over the next six periods. However, the prediction intervals become wider as the forecasting horizon increases, reflecting growing uncertainty in longer-term predictions.
Time Series Analysis in Forecasting Nickel Prices Using the ARIMA and Double Exponential Smoothing Methods Vina Nurmadani; Rian Kurnia; Indah Suciati
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 4 No. 1 (2026): JANUARY
Publisher : Universitas Muhammadiyah Metro

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Abstract

Nickel is one of the strategic commodities that plays an important role in global industries, particularly as the primary raw material in the production of stainless steel and electric vehicle batteries. The increasing demand for nickel, driven by technological advancements and the need for more environmentally friendly energy sources, causes nickel prices to fluctuate, making it necessary to employ methods capable of forecasting future price movements. This study aims to forecast nickel prices using the Autoregressive Integrated Moving Average (ARIMA) method and the Double Exponential Smoothing method, as well as to compare the performance of both methods. The data used in this research consist of secondary daily nickel price data with 62 observation periods. The research stages include data preprocessing, stationarity testing, modeling, and model evaluation using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The results show that the best ARIMA model is ARIMA(2,1,1), which produces an MSE of 0.2797 and an RMSE of 0.5288. Meanwhile, the Double Exponential Smoothing method results in an MSE of 0.1299 and an RMSE of 0.3604. Based on these evaluation results, the Double Exponential Smoothing method demonstrates better performance than ARIMA in forecasting nickel prices in this study. This method is able to produce more accurate and stable predictions that follow the trend patterns of the data. Therefore, the Double Exponential Smoothing method is recommended as a more optimal approach for nickel price forecasting
Application of Fuzzy C-Means with Variations in Weighting Exponent for Clustering the Human Development Index Indah Suciati; Rian Kurnia; Vina Nurmadani; Fitri Nurjanah
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 4 No. 1 (2026): JANUARY
Publisher : Universitas Muhammadiyah Metro

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Abstract

Human development is commonly measured using the Human Development Index (HDI), which reflects the quality of life across regions. In Indonesia, disparities in HDI values indicate uneven development, requiring appropriate analytical approaches. This study aims to cluster Indonesian provinces based on HDI indicators using the Fuzzy C-Means (FCM) method with variations in the weighting exponent. The data consist of 38 provinces in 2025, including life expectancy, expected years of schooling, average years of schooling, and adjusted real expenditure per capita. The clustering results were evaluated using the Partition Coefficient Index (PCI). The optimal configuration was obtained at and , with a PCI value of 0.716399. The results show that provinces are grouped into clusters with relatively lower HDI, which are predominantly located in eastern Indonesia, and clusters with higher HDI, which are mostly found in western Indonesia. These findings demonstrate that FCM is effective in identifying regional development patterns.
Single Exponential Smoothing for Forecasting Medium Rice Retail Prices in Lampung Province Tuti Maynur Cahya; Bernadhita Herindri Samodera Utami; Felicia Andrade Paskalia Marpaung; Dwi Herinanto
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 4 No. 2 (2026): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v4i2.10290

Abstract

Forecasting the price of medium grade rice is a strategic effort to support decision-making in maintaining food price stability in Lampung Province. This study aims to apply the Single Exponential Smoothing (SES) method in forecasting medium grade rice’s retail price in 2023 by evaluating the performance of the model using Mean Absolute Error (MAE). The data used is monthly retail price data for medium grade rice obtained from Dinas Ketahanan Pangan, Tanaman Pangan, dan Horticultura of Lampung Province. To obtain optimal forecasting results, the forecasting process involves determining the smoothing factor (α) parameters. The results show that the SES method can provide accurate forecasting with low MAE values. These findings suggest that the Single Exponential Smoothing method is feasible to be applied as a tool in food price control and policy planning in Lampung province.
Bayesian Prior Sensitivity in Psychological Decision Modeling: Evidence from Loss Aversion Estimation Under Prospect Theory Aflah Zakinov Irta; Rizal Kurniawan; Anindra Guspa
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 4 No. 2 (2026): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v4i2.11561

Abstract

Prior specification is a critical yet frequently neglected decision in Bayesian inference, with potentially severe consequences for behavioral research conclusions, particularly in nonlinear psychological decision models where likelihood surfaces are often flat and parameters are weakly identified. This study presents a simulation-based framework for assessing prior sensitivity in Bayesian psychological decision modeling, using loss aversion estimation under Prospect Theory as a case study. Synthetic binary choice data were generated from the Tversky-Kahneman utility function across four true loss aversion values (λ ∈ {1.5, 2.0, 2.5, 3.0}) and three sample sizes (n ∈ {100, 200, 500}), fitted under three prior specifications: weakly informative diffuse prior, moderate informative, and strongly informative, yielding 1,080 total model fittings from 360 synthetic datasets via Laplace approximation with importance-weighted resampling. Performance was evaluated via posterior mean bias, RMSE, credible interval width, and directional probability P(λ > 2). Three findings emerged. First, diffuse default priors failed to recover the loss aversion parameter when the likelihood was insufficiently informative, regardless of sample size. Second, strongly informative priors introduced systematic bias that persisted independently of sample size when the true parameter deviated from the prior mean. Third, prior choice produced meaningful disagreements in directional behavioral conclusions that larger samples could not eliminate. These findings demonstrate that prior sensitivity is a substantive methodological concern in Bayesian psychological decision modeling that cannot be resolved by increasing sample size alone, and researchers are encouraged to treat prior specification as an explicit analytical choice supported by routine sensitivity analysis.
Gold Price Forecasting Using Hybrid ARIMA-IGARCH Fitriani Agustina; Hasya Nur Auliya; Dadan Dasari
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 4 No. 2 (2026): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v4i2.11694

Abstract

Gold is an important investment and hedging instrument, with highly volatile price movements that are difficult to accurately predict. Previous studies have generally used ARIMA-GARCH models to forecast financial time series, but these models have not fully captured the persistent volatility of gold prices. Therefore, this study proposes an ARIMA-IGARCH approach to simultaneously model the mean and persistent volatility patterns of gold price movements. Daily gold closing price data from November 2022 to August 2025 was analyzed using Python, with 90% used for training and 10% for testing. The ARIMA model was used to capture the mean structure, while the IGARCH model was used to represent the long-term volatility persistence. The results showed that the proposed model achieved high forecasting accuracy, with a MAPE of 9.38%, indicating strong predictive performance. These findings indicate that the ARIMA-IGARCH model can serve as an alternative approach for gold price forecasting and financial market volatility analysis
Gaussian Mixture Models for Human Development-Based Regional Clustering of East Java Didik Bani Unggul; Muhammad Rusli Baharuddin; Miftah Fahira; Muhammad Zulfadhli
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 4 No. 2 (2026): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v4i2.11781

Abstract

Regional disparities in human development require an analytical approach that can identify latent patterns in development achievements. This study applies the Gaussian Mixture Model (GMM) to cluster regencies and cities in East Java based on the Human Development Index (HDI). GMM was chosen because it offers a probabilistic and distribution-based clustering framework, assigns regions using posterior membership probabilities, and provides interpretable parameters such as mixing proportions, component means, and variances. Univariate GMMs with two, three, and four components were fitted to HDI data from 38 regencies/cities in East Java at two time points, 2015 and 2025, which represent a ten-year interval. Model selection was conducted using internal cluster-validity measures, namely the Silhouette Coefficient and the Davies–Bouldin Index. The results show that the two-component GMM is selected as the best model for both years. The selected model produces the same membership structure in 2015 and 2025, forming a larger cluster of 31 regencies/cities with relatively lower and more variable HDI values and a smaller cluster of 7 regencies/cities with higher and more homogeneous HDI values. Over the ten-year interval, HDI increased in all regions, while the two-cluster structure remained evident. These findings can support regional development planning, policy evaluation, and the formulation of more targeted development strategies across regencies and cities.

Page 4 of 4 | Total Record : 38

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