https://tecnoscientifica.com/journal/gisa <p><strong><em>Green Intelligent Systems and Applications (Green Intell. Syst. Appl.) (ISSN 2809-1116) </em></strong><strong> </strong>with a short form of <strong>GISA </strong>is an<strong> Open Access Refereed Journal </strong>that publishes <strong>research articles, reviews, and short communication </strong>on all aspects of green technologies and intelligent systems.</p> <p><strong>GISA </strong>is published online with a frequency of two (2) issues per year in <strong>October and April </strong>with <strong>FREE </strong>of Article Processing Charge (APCs) and Articles Submission Charges (ASCs). Besides that, special issues of GISA will be published non-periodically from time to time. </p> Tecno Scientifica Publishing en-US Green Intelligent Systems and Applications 2809-1116 <p>Authors shall retain the copyright of their work and grant the Journal/Publisher rights for the first publication with the work concurrently licensed under the <a href="https://creativecommons.org/licenses/by/4.0/"><strong>Creative Commons Attribution 4.0 International License (CC BY 4.0)</strong></a>.</p> <p>Under this license, authors who submit their papers for publication by <em>Green Intelligent Systems and Applications</em><em> </em>agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source is properly cited, anyone may copy, redistribute, reuse and transform the content.</p> <p>This broad license intends to facilitate free access, as well as the unrestricted use of original works of all types. This ensures that the published work is freely and openly available in perpetuity.</p> https://tecnoscientifica.com/journal/gisa/article/view/1282 <p>The raw input data exhibited severe structural heterogeneity, manifesting as seven distinct structural formats (Formats A–G) characterized by inconsistent header placement, nested merged cells, variable string representations, and missing values. To address these challenges without relying on static rule-based scripts, an intelligent adaptive parser was developed using dynamic regular expressions (regex) and token-density schema mapping based on normalized Levenshtein distance. The adaptive pipeline automatically identified table structures and standardized heterogeneous column definitions into a unified relational database, achieving a data-cleaning accuracy of 99.4%. To enhance the analytical value of conventional demographic data, a spatial feature enrichment pipeline was implemented by integrating three external Application Programming Interfaces (APIs): OpenStreetMap Nominatim for high-precision geocoding, the Google Maps Distance Matrix API for calculating actual road-network distances and travel times, and the Open-Meteo Elevation API for extracting elevation data. This study contributed to public sector data engineering by demonstrating that the integration of automated adaptive parsing with geospatial multi-API enrichment successfully transformed fragmented public documents into a high-fidelity, multidimensional data repository suitable for robust spatial–demographic policy analysis.</p> Arya Pratama Tarigan Mohammad Andri Budiman Ade Candra Copyright (c) 2026 Arya Pratama Tarigan, Mohammad Andri Budiman, Ade Candra https://creativecommons.org/licenses/by/4.0 2026-07-31 2026-07-31 6 2 195–206 195–206 10.53623/gisa.v6i2.1282 https://tecnoscientifica.com/journal/gisa/article/view/1294 <p>Oil palm plantations are one of the main sectors contributing to regional economic development in North Sumatra Province. This study aimed to analyze the performance of the Multivariate Multiple Linear Regression (MMLR) algorithm in modeling the relationship between oil palm plantation expansion and regional economic indicators. Secondary data from five oil palm-producing regencies covering the period 2013–2023 were obtained from the Central Statistics Agency (BPS). The independent variables consisted of plantation area and oil palm production, whereas the dependent variables were Gross Regional Domestic Product (GRDP) and per capita income. The dataset was divided into training (2013–2019) and testing (2020–2023) subsets. Model performance was evaluated using the coefficient of determination (R²), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results showed that the MMLR model was able to capture the relationship between oil palm plantation expansion and regional economic indicators, although its predictive performance varied across regencies. These findings indicated that the model provided useful insights into the contribution of the oil palm plantation sector to regional economic development in North Sumatra.</p> Afridayani Erna Budhiarti Nababan Baihaqi Siregar Copyright (c) 2026 Afridayani, Erna Budhiarti Nababan, Baihaqi Siregar https://creativecommons.org/licenses/by/4.0 2026-08-06 2026-08-06 6 2 207−216 207−216 10.53623/gisa.v6i2.1294