Jurnal Teknologi Informasi dan Pendidikan

Architecture of the Village Budget Reporting Information System Utilizing the CodeIgniter Framework and a Multi-User Role-Based Access Control Approach

I Gusti Agung Oka Sudiadnyani — 2026-09-12

Digital transformation in village financial management requires a reporting system with high data integrity to support transparent supervisory functions. In Karangasem Regency, particularly in the pilot locus of Sibetan Village, the management of the Village Revenue and Expenditure Budget (APBDesa) faced challenges with data synchronization, administrative errors, and barriers to conventional audits. This study developed an audit-oriented APBDesa reporting information system architecture that integrates Multi-User Role-Based Access Control (RBAC) and automated logging mechanisms. System requirements were identified and validated through direct observation involving three key stakeholders representing the main reporting roles: the Head of Financial Affairs, the Village Head, and the External Auditor. System development followed four sequential Waterfall phases (requirements, design, implementation, and verification). The system was implemented using the CodeIgniter framework with a Model-View-Controller architecture. Black-box evaluation by five technical testers was organized into seven principal module-level test cases corresponding to authentication, dashboard, revenue management, expenditure management, verification and approval, RBAC, and external-auditor monitoring; all achieved a 100% functional success rate. The system enforced segregation of duties among the Head of Financial Affairs, the Village Head, and the External Auditor while supporting a structured digital audit workflow. These results indicate that embedded role controls and audit stages can strengthen data integrity, traceability, and accountability in APBDesa reporting.

Design and Development of a Web-Based Laundry Information System Featuring Balance Top-Up Payments and Membership Management at Pondok Pesantren Assalaam Surakarta

Dahlia Dewi — 2026-09-12

The Laundry Unit of Assalaam Islamic Boarding School, Surakarta, continues to encounter challenges in managing member data, laundry transactions, payment processing, and report generation because these operational activities are handled separately. This fragmented process increases the risk of recording errors and reduces service efficiency. This study aims to develop a web-based Laundry Information System that integrates data management, balance top-up payments, pending transaction processing, and membership management to improve the effectiveness and efficiency of laundry services. The system was developed using the Waterfall software development methodology, consisting of requirements analysis, system design, implementation, testing, and maintenance. Laravel was employed as the development framework, with PHP as the programming language, MySQL as the database management system, and Unified Modeling Language (UML) for system modeling. The developed system successfully integrates student member management, laundry transaction processing, balance top-up payments, pending transaction handling, laundry staff management, receipt generation, and reporting into a single web-based application. System functionality was evaluated using Black Box Testing, which confirmed that all features operated according to the specified requirements. Furthermore, User Acceptance Testing (UAT) produced a user acceptance rate of 94.07%, indicating a high level of user satisfaction with the developed system. The implementation of the proposed system improves service speed, enhances the accuracy of data and transaction management, and increases the efficiency of report generation. These findings demonstrate that the system is suitable for implementation at the Laundry Unit of Assalaam Islamic Boarding School, Surakarta, and has the potential to support more effective and reliable operational management.

Synthetic Data Quantity in Animal Classification

Fieter Brain Pasaribu — 2026-09-16

The need for large datasets remains a challenge in deep learning-based classification models such as Convolutional Neural Networks. Limited data can lead to overfitting, where the model performs well on training data but poorly on testing data. To address this, numerous studies have been conducted using both traditional augmented data and synthetic data generated by Generative Adversarial Networks (GANs). GANs have proven effective across a wide range of dataset domains. However, no research has focused on the effect of the amount of synthetic data on model classification accuracy, leading to confusion about how much should be used to avoid overfitting. Therefore, this study will review the effectiveness of GANs by examining the effect of synthetic data ranging from small amounts to 20 times the original dataset size. The synthetic image results from GANs were then tested using them as supplementary datasets for MobileNetV2 and EfficientNetB0. The results show that the effectiveness of synthetic data fluctuates significantly. At certain amounts, the use of synthetic data can improve accuracy, such as in MobileNetV2, which increased from 73% to 77%. However, at certain amounts, the use of synthetic data also decreases accuracy from 73% as the baseline to 66%. This fluctuating condition is caused by the synthetic data from GANs not being consistent in representing the original data well. The lack of quality of GAN is caused by the architecture used, which is still too simple to capture data patterns with a wide variety of perspectives

Integrating TOE Framework and UTAUT to Explain Online Supply Chain Finance Platform Adoption: Evidence from C2FO Users

Asif Khan — 2026-09-17

Online supply chain finance platforms allow suppliers to convert approved receivables into cash before invoice maturity, and participation among Taiwanese small and medium-sized enterprises has not been examined at the level of the adopting firm. This study integrates the technology-organization-environment framework with the unified theory of acceptance and use of technology, extended with trust, and specifies the three contexts as antecedents of user perceptions rather than as direct predictors of adoption intention. Survey data from 200 managers of Taiwanese enterprises using the C2FO platform were analyzed, and the model supported ten of the twelve hypothesized relationships. The environmental context produced the largest coefficient on performance expectancy, effort expectancy, and trust, and the technological context influenced all three perceptions. The organizational context influenced trust but neither performance expectancy nor effort expectancy. Performance expectancy and trust had comparable effects on adoption intention, whereas effort expectancy had a smaller effect. All three contexts exerted significant indirect effects on intention through the perceptual constructs. The findings specify the perceptual route through which contextual conditions reach organizational adoption, establish trust as a required extension in supply chain finance, and identify a boundary condition on the organizational context in small firms.