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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Statistika dan Komputasi (STATKOM)
Anggakusuma, Aurellia Calista
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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MODELING AND FORECASTING THE TOTAL VOLUME OF GOODS TRANSPORTED BY RAIL IN INDONESIA USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) Syahzaqi, Idrus; Sediono, Sediono; Anggakusuma, Aurellia Calista; Wieldyanisa, Ezha Easyfa; Oktavia, Sabrina Salsa
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp829-842

Abstract

Transportation has an important role in supporting the mobility of people in Indonesia. Trains are included in the most widely used transportation category because they are effective and efficient, not only transporting passengers, trains also have a role in the distribution of goods. This study aims to model and forecast total volume of goods transported through rail transportation in Indonesia using the Seasonal Autoregressive Integrated Moving Average (SARIMA) Method because the data has seasonal trend. The data used comes from the Central Statistics Agency (BPS) from January 2013 to April 2024. The results were obtained that the SARIMA (0,1,1)(0,1,1)12 model is the best model with a MAPE value of 0.96% which is included in the category of accurate model. In addition to being an additional insight, this research can also be a reference in the management of railway transportation considering the number of uses both passengers, the distribution of goods that continue to increase, and can be recommendation for other research that same topic with it.
Peramalan Jumlah Barang Kereta Api di Indonesia Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) Syahzaqi, Idrus; Sediono, Sediono; Oktavia, Sabrina Salsa; Anggakusuma, Aurellia Calista; Wieldyanisa, Ezha Easyfa
Jurnal Statistika dan Komputasi Vol. 4 No. 1 (2025): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v4i1.4424

Abstract

Background: Freight transportation is an important part of the business run by PT Kereta Api Indonesia. To support effective strategic planning and infrastructure development, an accurate prediction of the amount of goods to be transported in the future is required. Therefore, historical data-based forecasting methods such as Seasonal Autoregressive Interated Moving Average (SARIMA) can be a relevant approach to predict the number of railway goods in Indonesia. Objective: Obtain a suitable model to forecast the number of goods transported by rail transportation in Indonesia, and to determine the results of the forecasting. Methods: This research uses the time series method with the Seasonal Autoregressive Integrated Moving Averang (SARIMA) model approach based on data characteristics that show seasonal patterns. SARIMA itself is able to integrate seasonal pattern components in the data and is able to effectively capture periodic and structural dynamics in seasonal data. Results: The best model obtained is probabilistic SARIMA(0,1,1)(0,1,1)12, using secondary data sourced from the Central Bureau of Statistics (BPS) in the range of January 2013 to March 2024. Forecasting for the next 12 months (April 2023 to March 2024) shows a Mean Absolute Percentage Error (MAPE) value of 8.03% which indicates that the level of forecasting accuracy is very good. Conclusion: The probabilistic ARIMA(0,1,1)(0,1,1)12 model can be used as a reliable reference in predicting the amount of goods transported through rail transportation in Indonesia.
Modeling Risk Factors of Acute Respiratory Infections using Logistic Regression and Multivariate Adaptive Regression Splines Kurniawan, Ardi; Fauziah, Nathania; Mahadesyawardani, Arinda; Gunawan, Syifa’ Azizah Putri; Anggakusuma, Aurellia Calista
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33833

Abstract

Acute Respiratory Infections (ARI) remain a leading cause of morbidity among toddlers, partic ularly in regions with limited healthcare access. This study aimed to model the risk factors of ARI in toddlers using Binary Logistic Regression and Multivariate Adaptive Regression Splines (MARS). Using secondary data from Southeast Aceh, seven predictor variables were analyzed, including ma ternal characteristics, breastfeeding status, and household conditions. Both models were statisti cally significant in identifying key predictors. Logistic regression showed superior performance with 86.96% accuracy, 85.00% precision, 91.89% recall, 81.25% specificity, and 88.30% F1-score. In contrast, MARS achieved a higher recall (97.30%) but lower specificity (62.50%), indicating higher sensitivity but a greater likelihood of false positives. Exclusive breastfeeding, home ventilation, and housing density were significant predictors in both models. Overall, logistic regression was found to be the more reliable and interpretable method, offering better balance in classification metrics. These f indings support the use of logistic regression for identifying ARI risk factors in similar contexts and contribute to improved data-driven public health strategies aimed at reducing ARI incidence among vulnerable populations.
Co-Authors Ardi Kurniawan Fauziah, Nathania Gunawan, Syifa’ Azizah Putri Idrus Syahzaqi Mahadesyawardani, Arinda Oktavia, Sabrina Salsa Sediono, Sediono Wieldyanisa, Ezha Easyfa