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All Journal JOIV : International Journal on Informatics Visualization Journal of Applied Science, Engineering, Technology, and Education
Boj, Eva
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The date predicted 200.000 cases of Covid-19 in Spain Ahmar, Ansari Saleh; Boj, Eva
Journal of Applied Science, Engineering, Technology, and Education Vol. 2 No. 2 (2020)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (483.471 KB) | DOI: 10.35877/454RI.asci22102

Abstract

The aim of this study is to predict 200.000 cases of Covid-19 in Spain. Covid-19 Spanish confirmed data obtained from Worldometer from 01 March 2020 – 17 April 2020. The data from 01 March 2020 – 10 April 2020 using to fitting with data from 11 April – 17 April 2020. For the evaluation of the forecasting accuracy measures, we use the mean absolute percentage error (MAPE). Based on the results of SutteARIMA fitting data, the accuracy of SutteARIMA for the period 11 April 2020 - 17 April 2020 is 0.61% and we forecast 20.000 confirmed cases of Spain by the WHO situation report day 90/91 which is 19 April 2020 / 20 April 2020.
Time Series Innovation: Leveraging BetaSutte Models to Enhance Indonesia's Export Price Forecasting Ahmar, Ansari Saleh; Boj, Eva
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3831

Abstract

This study introduces a novel application of the Modified Trend-Augmented α-Sutte Indicator (BetaSutte) model for forecasting Indonesia's export prices and compares its performance with the traditional ARIMA approach. Accurate export price forecasting is crucial for economic planning, trade policy formulation, and business strategy development in Indonesia's dynamic and globally connected economy. Using monthly export value data from January 2022 to September 2024 obtained from Indonesia's Central Bureau of Statistics (BPS), we examined whether the BetaSutte model's decomposition of trend and residual components offers enhanced predictive accuracy over the conventional ARIMA methodology. Results show that while the ARIMA(0,1,0) model demonstrated superior in-sample performance (Training MAPE: 7.71% vs. 80.78%), the BetaSutte model achieved better out-of-sample forecasting accuracy (Testing MAPE: 11.22% vs. 11.61%). The BetaSutte model's linear trend component identified a negative slope (coefficient: -158.4), indicating a systematic decline in Indonesia's export values over the study period, which has important implications for trade policy. Furthermore, the model successfully captured the volatility in export prices through its residual forecasting component. These findings suggest that the BetaSutte model's explicit modeling of trend components provides meaningful advantages for export price forecasting, despite its more complex implementation. This research contributes to the growing literature on hybrid forecasting methodologies and offers practical guidance for stakeholders interested in Indonesia's international trade dynamics. For policymakers, the results highlight potential challenges for Indonesia's export competitiveness and suggest the need for targeted interventions to address the identified downward trend in export values.
Application of Neural Network Time Series (NNAR) and ARIMA to Forecast Infection Fatality Rate (IFR) of COVID-19 in Brazil Ahmar, Ansari Saleh; Boj, Eva
JOIV : International Journal on Informatics Visualization Vol 5, No 1 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.1.372

Abstract

Forecasting is a method that is often used to view future events using past time data. Past time data have useful information to use in obtaining the future. The aim of this study was to forecast infection fatality rate (IFR) of COVID-19 in Brazil using NNAR and ARIMA. ARIMA and NNAR are used because (1) ARIMA is a simple stochastic time series method that can be used to train and predict future time points and ARIMA also capable of capturing dynamic interactions when it uses error terms and observations of lagged terms; (2) the Artificial Neural Network (ANN) is a technique capable of analyzing certain non-linear interactions between input regressor and responses, and Neural Network Time Series (NNAR) is one method of ANN in which lagged time series values were used as inputs to a neural network. Data included in this study were derived from the total data of confirmed cases and the total data of death of COVID-19. The data of COVID-19 in Brazil from February 15, 2020 to April 30, 2020 were collected from the Worldometer (https://www.worldometers.info/coronavirus/) and Microsoft Excel 2013 was used to build a time-series table. Forecasting was accomplished by means of a time series package (forecast package) in R Software.  Neural Network Time Series and ARIMA models were applied to a dataset consisting of 76 days. The accuracy of forecasting was examined by means of an MSE. The forecast of IFR of COVID-19 in Brazil from May 01, 2020 to May 10, 2020 with NNAR (1,1) model was around in 6,85% and ARIMA (0,2,1) was around in 7.11%.
Co-Authors Ahmar, Ansari Saleh