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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) INOVTEK Polbeng - Seri Informatika
Azhari Shouni Barkah
Amikom Purwokerto University
Automotive Engineering Computer Science & IT Control & Systems Engineering

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Analysis and Design of the Nusa Graha Module for Village Asset Management and Facility Booking on the NUSAEKA Multi-Tenant SaaS Platform Purnia Setiawati; Azhari Shouni Barkah; Rizki Cahya Putri; Intan Nur Sifa; Aulia Suryaning Tyas; Mayza Nurul Khasanatun Nisa; Sri Rahayu; Lina Nur Afifah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2331

Abstract

In most regions of Indonesia, village asset management and the process of booking village facilities are still carried out manually, which can lead to disorganized record-keeping, data loss, and a lack of access for village residents. This study was conducted to analyze and evaluate the Nusa Graha module as a component of the Nusaeka multi-tenant SaaS platform, focusing on village inventory management, automatic asset depreciation, and web-based village booking services. This research was conductes through a literature review and system analysis obtained through consultation with supervising lecturer as well as document analysis. The analysis results include business flowcharts, Data Flow Diagrams (DFDs) at levels 0 and 1, and Entity-Relationship Diagrams (ERDs), which consist of several main tables. The research findings indicate that the Nusa Graha module can support and streamline asset management and the structured process of facility rentals using multi-tenant data via tenant_id and a modular language. Additionally, the Nusa Graha module facilitates integration with the Nusa Artha financial module if the village subscribes to it.
Analysis of the Effect of RetinexNet-Based Image Preprocessing on Object Detection Performance Using YOLOv8 Under Low-Light Conditions Bihandoyo Masdhi; Giat Karyono; Azhari Shouni Barkah
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/6tnkh649

Abstract

The decline in performance in object detection systems based on deep learning is skewed to be meaningfully inclined when the system is used in low-lighting conditions, especially due to the decrease in visual quality of the image. In this study, the focus is directed to evaluate the effect of the application of RetinexNet-based image preprocessing on object detection performance using YOLOv8 in low-light environments. The experimental process was carried out to compare the detection results between models that used preprocessing and those that did not use preprocessing, based on evaluation metrics such as precision, recall, and mean average precision (mAP). The results indicate that improving the visual quality of the sword image is always followed by an increase in detection accuracy, because these changes can cause a shift in the distribution of visual features that have an impact on the model's generalization ability. In addition, the phenomenon of domain shift resulting from image changes using RetinexNet was also found, which had an effect on the consistency of YOLOv8 performance. The main contribution of this study is to provide empirical evidence that preprocessing strategies for low-light conditions not only need to focus on improving visual quality but also need to be adapted to the characteristics of the detection model in order to obtain a more adaptive pipeline under extreme lighting conditions.
Co-Authors Aulia Suryaning Tyas Bihandoyo Masdhi Giat Karyono Intan Nur Sifa Lina Nur Afifah Mayza Nurul Khasanatun Nisa Purnia Setiawati Rizki Cahya Putri Sri Rahayu