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All Journal Sciencestatistics: Journal of Statistics, Probability, and Its Application
Rian Kurnia
Institut Teknologi Sumatera, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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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.
Co-Authors Fitri Nurjanah Indah Suciati Vina Nurmadani