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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING ESTIMASI: Journal of Statistics and Its Application
Yunita, Andi Isna
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Perbandingan Metode Seasonal ARIMA dan Extreme Learning Machine dalam Prediksi Produksi Padi di Sulawesi Selatan Jamal, Rini; Baso, Andi M Alfin; Andi Febriyanti; Sitti Sahriman; Siswanto, Siswanto; Yunita, Andi Isna; Angriany, A. Muthiah Nur; Rahim, Rahmiati; Fadil, Muhammad
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.45821

Abstract

South Sulawesi is one of the provinces that significantly contributes to national rice production. Therefore, accurate forecasting of rice production is crucial for food security planning and agricultural policy-making. This study aims to compare the performance of the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Extreme Learning Machine (ELM) methods in predicting rice production in South Sulawesi. SARIMA is a statistical forecasting method effective for data with seasonal patterns, while ELM is a machine learning approach capable of handling complex relationships among variables with high computational speed. Rice production data from the Central Statistics Agency (Badan Pusat Statistik) were used to evaluate the accuracy of both methods. The evaluation was conducted using forecasting error metrics such as Mean Absolute Percentage Error (MAPE). The results show that the SARIMA(1,1,0)(1,1,0)12 model outperformed ELM in predicting rice production in South Sulawesi. This is indicated by a lower MAPE value of 19.937%, compared to 21.632% for the ELM method.
Comparison of FEM-LSDV Panel Regression with Classical Panel Regression Models in Analyzing Economic Growth in Indonesia Andi, Harismahyanti A; Alimatun, Najiha; Yunita, Andi Isna; Ratmila, Ratmila; Nur'eni, Nur'eni
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10318

Abstract

This study evaluates the performance of multiple panel regression approaches in modeling the determinants of regional economic growth in Indonesia. It specifically compares three classical panel models: the Common Effect Model (CEM), the Random Effect Model (REM), and the Fixed Effect Model (FEM), alongside the Fixed Effect Model with the Least Squares Dummy Variable (FEM LSDV) approach. The analysis is based on panel data covering 34 provinces from 2019 to 2023, using key macroeconomic indicators such as inflation, investment, exports, money supply, open unemployment rate, and participation in the national health insurance program (JKN). The models are assessed using formal statistical tests, including the Chow and Hausman tests, and evaluated through performance metrics such as RMSE, AIC, and R-squared. The results show that the FEM LSDV model offers the best performance, with an R-squared value of 0.7039, RMSE of 0.5442, and an AIC of 365.55. Notably, the model identifies North Maluku Province as contributing positively and significantly to economic growth, while the year 2020 shows a significant negative impact, likely due to the economic disruptions caused by the COVID-19 pandemic. These findings demonstrate the effectiveness of the FEM LSDV approach in capturing both spatial and temporal heterogeneity in regional economic analysis and support its application in policy-oriented research.
A MODIFIED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODELING ON OPEN UNEMPLOYMENT RATE IN SOUTH SULAWESI Siswanto, Siswanto; Sunusi, Nurtiti; Yunita, Andi Isna; Davala, Muhammad Ridzky; Baso, Andi M. Alfin; Nurfadilah, Nurfadilah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1099-1110

Abstract

The Open Unemployment Rate (OUR) in Indonesia is still a challenge despite a decline, namely 4.82% in February 2024 and around 7.2 million unemployed people. The main cause of the OUR is the imbalance between the number of the workforce and the availability of jobs. This issue is directly related to the Sustainable Development Goals (SDGs), especially Goal 8 which focuses on the creation of decent jobs and economic growth. South Sulawesi Province has experienced a spike in the OUR in the last five years, especially due to the Covid-19 pandemic which caused the poverty rate to decline to 6.31% in 2020. Along with economic recovery, this figure decreased to 4.19% in August 2024. Although low, the thickness of the layer remains a concern because 4 out of 100 people have not been absorbed in the labor market. Therefore, it is important to identify the factors that influence the OUR in South Sulawesi in order to design a reduction strategy. Various factors that influence the OUR include the human development index, percentage of poor people, average length of schooling, life expectancy, population density, and regional gross domestic product. To analyze the influence of these factors, this study uses the Geographically and Temporally Weighted Regression (GTWR) method which can capture spatial and temporal variations. Modifications are made using the Mahalanobis distance to consider inter-regional correlation and the Locally Compensated Ridge (LCR) approach to overcome high collinearity in the data. The data used comes from the Central Statistics Agency of South Sulawesi Province. Meanwhile, partial testing obtained each observation of the influencing factors varying from 2020 to 2023. In general, the factors that significantly influence the open poverty rate in South Sulawesi in 2020-2023 are the human development index, percentage of poor people, average length of schooling and life expectancy.
Co-Authors Andi Febriyanti Angriany, A. Muthiah Nur Baso, Andi M Alfin Baso, Andi M. Alfin Davala, Muhammad Ridzky Harismahyanti A., Andi Jamal, Rini Muhammad Fadil Nur'eni, Nur'eni Nurfadilah Nurfadilah NURTITI SUNUSI Rahim, Rahmiati Ratmila, Ratmila S Siswanto Sitti Sahriman