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All Journal Brilliance: Research of Artificial Intelligence
Ulfa Khaira
Universitas Jambi, Indonesia
Decision Sciences, Operations Research & Management Mathematics Other

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Back-End Geographic Information System Development for Linguistic and Literary Mapping in Jambi Province Cagivamito Tadashi Hutabarat; Pradita Eko Prasetyo Utomo; Ulfa Khaira
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7384

Abstract

Indonesia possesses a rich diversity of regional languages and literature, including 718 recorded languages and 965 literary works nationally. Jambi Province, with its seven local languages and abundant oral traditions, requires more effective preservation efforts. Currently, available information is limited to static physical maps that are difficult to access. This study aims to develop a web-based Geographic Information System to digitally, interactively, and flexibly map the distribution of languages, literature, and scripts in Jambi. The system was developed using the System Development Life Cycle (SDLC) approach with an Incremental model, allowing for gradual development and continuous adjustments. The primary focus was on back-end development, including the database, business logic, and server. Functional testing was conducted using black-box testing, while non-functional performance evaluation was carried out through load testing using k6 on the main features, simulating 50 Virtual Users (VUs). Test results indicated that the system was stable and responsive, with a 0.00% failure rate, average response times of 59.08–68.74 ms, and a P95 not exceeding 106 ms. The system was developed in two increments: a general user interface and an administrator dashboard, enabling efficient management of language, literature, script, announcement, and feedback data. The implementation of this digital platform enhances information accessibility, supports the Language Office of Jambi Province in data dissemination, and contributes to the preservation of regional cultural heritage for the public and researchers.
Front-End Development of a Geographic Information System for Language and Literature Mapping in Jambi Aldi Sukma Putra; Pradita Eko Prasetyo Utomo; Ulfa Khaira
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7388

Abstract

This study addresses the need for an interactive digital platform to support the preservation of linguistic and literary data in Jambi Province. Existing platforms developed by Balai Bahasa Provinsi Jambi provide textual information only and lack spatial visualization, limiting users’ ability to explore linguistic distributions. Geographic Information Systems (GIS) are suitable for linguistic documentation because dialect boundaries and speech communities are strongly related to geographic regions. This study aims to design and develop a front-end GIS interface for mapping linguistic and literary data using the Incremental Model and to evaluate its functional performance through Black-Box Testing. The system was built using HTML, CSS, JavaScript, the Laravel Blade templating engine, and the Leaflet library for interactive map visualization. The Incremental Model supported iterative development, allowing core features map visualization, search and filter functions, and detailed information pages to be refined based on continuous feedback. Data from Balai Bahasa Provinsi Jambi, including language names, literary descriptions, documentation files, and geographic coordinates, were used as input. The results show that the system meets all functional requirements, achieving a 100% success rate across 11 Black-Box test scenarios, and providing real-time response capabilities for search and filter functions. These technical outcomes demonstrate that incremental front-end development is effective for building modular and interactive GIS interfaces. This study contributes to digital cultural preservation efforts and provides a foundation for future GIS-based linguistic mapping initiatives, while further research is needed to enhance backend integration, expand datasets, and evaluate system performance at scale.
Support Vector Regression-Based Prediction of Rice Production Across Provinces in Sumatra Island Elton Elyon Sijabat; Ulfa Khaira; Mutia Fadhila Putri
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7429

Abstract

This study develops a Support Vector Regression (SVR)–based forecasting framework to model rice production across the ten provinces of Sumatra, a region whose agricultural output is highly sensitive to climate variability and land-use dynamics. Rising uncertainty in rainfall-dependent rice ecosystems underscores the need for more accurate predictive tools to support regional food-security planning. The objective of this research is to construct and evaluate a multivariate SVR model that integrates harvested area, rainfall, humidity, and temperature, while accounting for nonlinear temporal patterns and structural differences among provinces. The methodological approach includes extensive feature engineering, log-transformed SVR estimation with time-series cross-validation, a specialized year-over-year model for small and volatile provinces, and a stabilization procedure to ensure temporal consistency in the predictions. Results show that the blended–stabilized model performs strongly on the 2021–2024 test period, achieving SMAPE of 16.10%, MAE of 124,975.77, RMSE of 194,853.89, and R² of 0.9637, and generating three-year-ahead forecasts supported by bootstrap-based uncertainty intervals. These findings indicate that the proposed framework effectively captures heterogeneous production dynamics and provides reliable predictions for 2025–2027. The study concludes that SVR offers a robust and interpretable foundation for agricultural forecasting in data-limited environments, though future work should incorporate higher-frequency data, additional agronomic indicators, and hybrid machine-learning or deep-learning models to further improve long-term performance.
Co-Authors Aldi Sukma Putra Cagivamito Tadashi Hutabarat Elton Elyon Sijabat Mutia Fadhila Putri Pradita Eko Prasetyo Utomo