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All Journal Brilliance: Research of Artificial Intelligence
Mutia Fadhila Putri
Universitas Jambi, Indonesia
Decision Sciences, Operations Research & Management Mathematics Other

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Operational Data Integration with Pureshare Dashboard for Unified Service Unit Hana Silvanov Rhamadani; Pradita Eko Prasetyo Utomo; 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.v5i2.7412

Abstract

Public institutions in digital era increasingly require integrated data to support decision-making and performance monitoring. The development of the Electronic Unified Service Units (ULT-E) at the Jambi Language Office responds to this need by establishing a mechanism capable of consolidating operational data. The objective of this research is to design and develop a service dashboard using Pureshare as a guiding framework for identifying requirements, planning visual structures, and organizing information elements. The key performance indicators are presented as operational indicators across operational service data, including service requests, complaints, and public satisfaction. The development process includes requirement user, operational indicators, visual design, data integration through ETL procedures. The results show that the dashboards produced in this research present key performance indicators as operational indicators across three main areas, service requests, complaints, and public satisfaction surveys. The visual components consist of drill-down and time-range features for data exploration. The integration of these dashboards into the operational web interface indicates that the system is ready to support the institution’s digital service environment. The average System Usability Scale (SUS) score of 72.50 represents that users were able to follow the interaction flow and understand the visual components provided. The conclusion is that dashboard development can enhance service management efficiency, even when data conditions differ across modules, making operational information more accessible.
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 Elton Elyon Sijabat Hana Silvanov Rhamadani Pradita Eko Prasetyo Utomo Ulfa Khaira