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All Journal Brilliance: Research of Artificial Intelligence
Lidya Rosnita
Universitas Malikussaleh, Indonesia
Decision Sciences, Operations Research & Management Mathematics Other

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Usability-Driven Development of an IoT-Based Salted Fish Quality Detection Application Using the QUIM Model Muhammad Ikhwani; Lidya Rosnita; Nanda Sitti Nurfebruary; Asih Makarti Muktitama; Fidyatunnisa Fidyatunnisa; Muhammad Zuhdi
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.v5i2.6209

Abstract

Salted fish is a vital component of Indonesia’s coastal economy, supporting numerous fishing households and local micro, small, and medium enterprises (MSMEs). However, maintaining product quality during household freezer storage remains a significant challenge. Temperature and humidity fluctuations in shared freezers often lead to quality degradation, discoloration, and the risk of microbial contamination by pathogens such as Salmonella and Staphylococcus aureus. To address these issues, this study developed an Internet of Things (IoT)–based monitoring system that integrates temperature and humidity sensors with an Android application to provide real-time data visualization and automated risk notifications. Recognizing that usability is critical for technology adoption among food-related MSMEs, the Quality in Use Integrated Measurement (QUIM) framework was applied to evaluate system performance across ten dimensions: effectiveness, productivity, satisfaction, efficiency, learnability, flexibility, error tolerance, safety, accessibility, and sustainability. The system was designed using human-centered principles and implemented with an ESP32 microcontroller and DHT22 sensors. A 14-day pilot trial demonstrated that the application could reliably detect environmental fluctuations, with usability scores reflecting high effectiveness (4.1/5) and user satisfaction (4.3/5). Although minor issues with internet connectivity and error message clarity were noted, iterative improvements were successfully incorporated. These findings demonstrate the feasibility of combining IoT technology with QUIM-based evaluation to enhance food storage practices and support quality management in salted fish processing among MSMEs.
Interactive Visualization of Food Security Trends in North Aceh with a Business Intelligence Dashboard Lidya Rosnita; Muhammad Ikhwani; Hafizh Al Kautsar Aidilof; Muhammad Muaz Munauwar
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.v5i2.7190

Abstract

Food security in North Aceh Regency faces multifaceted challenges, including production fluctuations, price instability, and fragmented monitoring data across various institutions. These issues often hinder timely decision-making and the formulation of effective policies. Therefore, this study aims to develop a comprehensive Business Intelligence (BI) dashboard that can interactively visualize food security trends in North Aceh to support data-driven and evidence-based decision-making. The research methodology involves integrating data from multiple sources such as the Central Bureau of Statistics (BPS) and the Department of Agriculture using the ETL (Extract, Transform, Load) process to ensure consistency and accuracy. A data warehouse was then designed to store and manage the consolidated datasets efficiently, followed by the development of an interactive visual dashboard as the main analytical tool. The resulting dashboard is capable of visualizing six key parameters of food security through thematic maps, trend graphs, and comparative charts that allow users to observe temporal and spatial patterns. Advanced interactive features such as filtering, drill-down analysis, and cross-filtering provide users with the flexibility to independently explore data from different perspectives. The analysis demonstrates that the BI dashboard effectively integrates fragmented datasets, simplifies complex information, and enhances analytical capabilities for stakeholders. Overall, the findings indicate that implementing an interactive BI dashboard is a strategic and innovative solution to transform food security monitoring in North Aceh into a more proactive, integrated, and adaptive governance system, thereby strengthening regional resilience and policy responsiveness.
Spatial Analysis of Residential and Vacant Land using Mean Shift Clustering in Lhokseumawe City Hafiz Al Kautsar Aidilof; Munirul Ula; Rinaldi Mirsa; Lidya Rosnita; Marhaban Almaula; Muji Budiman
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.7279

Abstract

Uncontrolled urban growth in medium-sized Indonesian cities like Lhokseumawe has created spatial disparities through uneven settlement distribution and numerous underutilized vacant lands, demanding adaptive and data-driven planning approaches. This study aims to analyze spatial patterns of settlement and vacant land distribution in Lhokseumawe City by implementing the Mean Shift Clustering algorithm and to develop spatial planning recommendations supporting sustainable development. The research employs a quantitative approach using spatial data from OpenStreetMap (OSM), Sentinel-2 satellite imagery (for NDVI calculation), and Detailed Spatial Planning (RDTR) data. The non-parametric, density-based Mean Shift Clustering algorithm was applied to adaptively group areas without predetermined cluster numbers, analyzing both city-wide and Blang Mangat District levels. Results show Mean Shift successfully identified 62 spatial clusters across Lhokseumawe City and 28 clusters (OSM-based) plus 37 clusters (RDTR-based) in Blang Mangat District. Analysis revealed linear-transitional urban development patterns from the west (dense urban core) to northeast (transition areas and vacant land), identifying zones of dense settlements, transition zones potential for infill development, and vacant land suitable for green spaces or planned development. Findings also revealed discrepancies between factual spatial patterns from clustering and RDTR zoning plans. The study concludes that Mean Shift Clustering effectively reveals natural spatial structures of settlements and vacant land, providing credible data for spatial policy revision with main recommendations focusing on vertical development, transportation system development along main corridors, and conservation area designation in eastern and northern zones to support Lhokseumawe's sustainable urban planning.
Co-Authors Asih Makarti Muktitama Fidyatunnisa Fidyatunnisa Hafiz Al Kautsar Aidilof Hafizh Al Kautsar Aidilof Marhaban Almaula Muhammad Ikhwani Muhammad Ikhwani Muhammad Muaz Munauwar Muhammad Zuhdi Muji Budiman Munirul Ula Nanda Sitti Nurfebruary Rinaldi Mirsa