Publication Search

The relatively high volatility of Robusta coffee prices creates uncertainty for farmers, business actors, and policymakers in making economic decisions. This study aims to analyze the price movement patterns of Robusta coffee, determine the most appropriate Autoregressive Integrated Moving Average (ARIMA) model, and conduct short- to medium-term price forecasting for Robusta coffee. The data used consist of monthly Robusta coffee price data from January 2023 to September 2025, sourced from the World Bank Commodity Price Data. The analytical method employed is ARIMA using EViews software, beginning with stationarity testing using the Augmented Dickey-Fuller (ADF) test, model identification through ACF and PACF, parameter estimation, and residual diagnostic testing. The results show that Robusta coffee price data are non-stationary at the level but become stationary at the first difference, indicating integration of order one I(1). Based on model identification and diagnostic testing, the ARIMA (0,1,0) model is found to be the most appropriate and satisfies the white noise assumption. Forecasting results indicate that Robusta coffee prices are projected to remain relatively stable with a moderate upward trend through December 2026. These findings are expected to serve as a reference for decision-making by farmers, business actors, and the government in responding to Robusta coffee price dynamics.

This study aims to forecast beef prices in Palembang City and at the national level in Indonesia using the Autoregressive Integrated Moving Average (ARIMA) method. The data used are the monthly average beef prices for the period January 2019 to December 2024. The analysis involves stationarity tests using Augmented Dickey-Fuller (ADF), model identification through Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, parameter estimation with Maximum Likelihood Estimation (MLE), and residual diagnostics with the Ljung-Box and Jarque-Bera tests. The results show that beef prices at both regional levels are not stationary at the level but become stationary after the first differencing (I(1)). The best ARIMA models obtained are ARIMA(0,1,1) for Palembang City and ARIMA(1,1,0) for the national level. Both models successfully predict price fluctuations with a low error rate and show a moderate price increase trend. These findings provide practical implications for price stabilization policy making and beef-related business planning. The forecast results state that beef prices in Palembang City and nationally are predicted to tend to rise in 2025 from January to December.  

Effective management of fixed assets plays a crucial role in maintaining the reliability and transparency of a company’s financial reporting. Errors in the capitalization process can lead to misstatements in financial statements and affect investment decisions. This study aims to analyze and forecast asset capitalization trends using the Autoregressive Integrated Moving Average (ARIMA) model. The research utilizes monthly recap data of asset capitalization recorded during the Settlement to Fixed Asset process from January 2021 to August 2025. The data were processed through several stages, including stationarity testing, model identification, parameter estimation, and model accuracy evaluation. The findings indicate that the data are stationary without differencing (d = 0). From several candidate models, ARIMA(0,0,3) was identified as the best model based on the lowest AIC value of 39.76. The selected model was then applied to predict asset capitalization values for the next ten periods, resulting in forecasts ranging from 1.12 to 1.56 trillion rupiah. Model evaluation showed a MAPE of 29.01%, which implies a moderate forecasting accuracy. Consequently, the ARIMA model can be considered a suitable analytical tool for monitoring and forecasting asset capitalization quantitatively.

Rainfall potential analysis plays a critical role in the management of air resources, mitigation of hydrometeorological disasters, and agricultural activity planning. Accurate estimation of rainfall patterns is essential to ensure effective decision-making in irrigation systems, water resource management, and disaster risk reduction strategies. This study aims to model the probability of rainfall occurrence using a statistical approach based on historical data obtained from the Bureau of Meteorology. The data spans a multi-year period and captures seasonal and regional variability in rainfall events. To characterize rainfall patterns, various probability distributions are tested, including the exponential distribution and the Weibull distribution, which are commonly applied in hydrological studies. Furthermore, the Markov chain method is employed to assess the likelihood of rainfall occurrence on a given day based on the conditions of the preceding day, thereby capturing temporal dependencies. Parameter estimation is conducted using Maximum Likelihood Estimation (MLE), a robust statistical method that enhances the precision of the model. The suitability of each probability distribution in representing the observed rainfall data is evaluated through goodness-of-fit tests such as the Kolmogorov-Smirnov test. The findings reveal that certain distributions align more closely with the local rainfall characteristics, demonstrating the importance of regional analysis in climate modeling. The combination of probabilistic modeling, Markov analysis, and rigorous statistical testing provides a reliable framework for forecasting rainfall. These results are expected to serve as a scientific basis for stakeholders in agriculture, environmental planning, and disaster preparedness, offering insights that support sustainable water resource utilization and risk management.

One of the most crucial subjects in the analysis of statistical models  is the identification of important variables. Therefore, the search for best variable selection methods is a good in obtaining best estimators. The Lasso method is considered the most effective approach for variable selection and parameter estimation in building statistical models with high explanatory power in representing the studied phenomenon. Therefore, using the Lasso method to estimate the parameters of a regression model that contains a dependent variable with data that is censored at zero can be achieved through the use of    Lasso tobit principal component regression, it has attractive properties in estimating the parameters of this model. The our proposed method is illustrated via simulation scenario and a new real data .

This research applies the Monte Carlo simulation method to predict the movement of Apple Inc.'s stock price over a long period of time. Using historical data of Apple's stock price from 12 December 1980 to 24 March 2022, this study aims to generate a probability distribution of the future stock price. The method involves several steps, including data collection, log return calculation, parameter estimation, and simulation of the stock price path through random iterations based on the log return distribution. The simulation results show that the closing price of Apple stock can be predicted by following the historical trend, although there are differences with the real data due to the stochastic nature of the Monte Carlo technique. This research also applies a variance reduction method to improve simulation efficiency. The findings provide a valuable perspective for investors and financial analysts in identifying investment risks and opportunities through an in-depth understanding of the dynamics of stock price movements using Monte Carlo simulation. Suggestions for future research include the use of VaR methods with historical variance and covariance approaches, as well as considering longer data periods and more stock indices for more comprehensive results.

The Erlang distribution is a special case of the Gamma distribution with the k shape parameter and the λ rate parameter. In this study, the parameter estimation of the Erlang distribution was carried out using the Maximum Likelihood method. In maximizing the function, an implicit and non-linear form is obtained, then it is solved using the Newton Raphson algorithm. Apart from Newton Raphon, the estimation of parameters was also carried out using the Fisher Scoring algorithm. The Fisher Scoring algorithm is similar to the Newton Raphson algorithm, the difference is that Fisher Scoring uses an matrix information. The result of parameter estimation in Erlang distribution using Newton Raphson algorithm which is applied to outgoing telephone call data that generated by Matlab R2010a software cannot be done simultaneously. Therefore, the parameter assessment is carried out on the k parameter first, then followed by the λ parameter estimation and the parameter and  = 0.6886812 are obtained. Meanwhile, the parameter estimation using the Fisher Scoring algorithm produces an equation that is not different from the Newton Raphson algorithm