International Journal of Application Computer Science and Informatic Engineering

Accounting Information System Based on Accrual with Cash Budgeting Approach

2026-05-18T07:50:04+00:00

Digital transformation has reshaped the accounting landscape, yet many small and medium enterprises (SMEs) struggle with the limitations of traditional cash-basis systems that fail to provide a complete financial picture. While accrual accounting offers a comprehensive view of financial health, it often lacks focus on immediate liquidity, which is critical for short-term operational stability. Motivated by the need for more accurate financial forecasting and decision-making, this study proposes an integrated Accounting Information System (AIS) that harmonizes accrual accounting principles with a cash budgeting approach. The primary contribution of this research is the development of a hybrid framework that bridges the gap between long-term profitability tracking and short-term liquidity management within a single, scalable platform. The effectiveness of the system was evaluated through functional and usability testing using real-world SME data. Results indicate that the system achieved a high degree of forecasting accuracy with an error margin of less than 5% and received positive feedback for its intuitive workflow. Future work will focus on enhancing system scalability for larger datasets and integrating advanced machine learning techniques, such as external economic variable analysis, to further refine cash flow projections. This integrated AIS serves as a robust tool for SMEs to ensure long-term sustainability through improved financial governance.

Accrual-Based Accounting Information System Using the Audit Trail Method

2026-05-18T07:56:02+00:00

The digital transformation of financial management necessitates the implementation of systems that ensure both accurate reporting and high data integrity. Traditional cash-basis accounting often results in information asymmetry and lacks the precision required to represent an entity's actual financial position. This research is motivated by the urgent need for transparency and accountability in digital financial systems, where unauthorized data manipulation remains a significant risk. We propose a web-based Accrual-Based Accounting Information System (AIS) integrated with a sequential Audit Trail method to record every data lifecycle event. The contribution of this study is the development of a secure framework that bridges accounting logic with cybernetic control mechanisms. Evaluation results indicate that the system achieves 100 percent accuracy in generating PSAK-compliant financial statements and successfully captures tamper-evident logs for all user interventions. Furthermore, the implementation effectively mitigates the risk of internal fraud by providing definitive forensic evidence. Future work will focus on integrating blockchain technology for decentralized immutability and artificial intelligence for proactive anomaly detection. This research is crucial for organizations seeking a verifiable and robust digital environment for financial governance.

Accrual-Based Accounting Information System with Cost Control Approach

2026-05-18T07:58:57+00:00

The rapid development of information technology has encouraged organizations to adopt Accounting Information Systems (AIS) to improve the accuracy and timeliness of financial information for managerial decision-making. However, many organizations still rely on cash-based or semi-manual accounting practices that limit their ability to monitor costs effectively and reflect financial performance accurately. This condition motivates the need for an accrual-based AIS that not only supports proper revenue and expense recognition but also integrates cost control mechanisms. This research proposes and implements an accrual-based Accounting Information System with a cost control approach designed to support transaction recording, cost classification, and managerial evaluation. The main contribution of this study lies in integrating accrual accounting principles with systematic cost control features, including cost center allocation, budget versus actual analysis, and managerial dashboards within a single system. The system is evaluated through functional testing and transaction simulations to assess accuracy, consistency, and reliability of accounting processes and cost reports. The evaluation results indicate that the system successfully maintains accounting balance, generates accurate accrual-based financial statements, and provides meaningful cost control information to support managerial decision-making. As future work, the system can be enhanced by incorporating advanced cost variance analysis, predictive cost forecasting, and the use of real operational data to further improve its analytical capability and applicability in real-world organizational environments.

Predicting Urban Green Energy Score Using AdaBoost Regression Based on Environmental Inputs

2026-05-18T08:01:47+00:00

Urban areas are facing growing challenges related to energy consumption, environmental degradation, and climate change. Predicting urban sustainability and energy performance is crucial for effective urban planning and policy-making. This research aims to develop a predictive model for the Urban Green Energy Score using AdaBoost regression, a powerful ensemble learning technique. The model incorporates various environmental factors, such as air quality, land surface temperature, and weather-related parameters, to estimate the energy sustainability of urban areas. The key contributions of this study include the development of a robust prediction framework, the application of AdaBoost regression for sustainable urban energy forecasting, and an empirical evaluation based on real-world datasets. The model was evaluated using metrics such as R², RMSE, and MAE, with results indicating high predictive accuracy and robustness. Future work could focus on integrating real-time data, applying deep learning techniques, and extending the model to other urban environments to further improve prediction accuracy and generalization capabilities.

Urban Green Energy Score Prediction Using XGBoost Based on Climate and Geographic Factors

2026-05-18T08:33:43+00:00

Urbanization has led to significant challenges in energy sustainability, with cities facing growing energy demands and environmental pressures. Traditional models often fail to incorporate complex interactions between climatic and geographic factors, limiting their accuracy in predicting urban energy performance. This research proposes a machine learning-based framework using XGBoost to predict the Urban Green Energy Score, a metric that integrates climate and geographic factors to assess urban energy sustainability. The model leverages data such as temperature, precipitation, land use, and urban structure, offering a comprehensive approach to evaluating energy sustainability. The contributions of this study include the development of an interpretable predictive model, the integration of diverse environmental data, and the application of advanced machine learning techniques. The model is evaluated using performance metrics such as RMSE, MAE, and R², with results demonstrating its effectiveness in predicting energy sustainability across multiple urban environments. Future work could explore incorporating real-time energy consumption data, socioeconomic factors, and deep learning techniques to further improve prediction accuracy and model generalization. This research provides a valuable tool for urban planners to optimize energy consumption and reduce environmental impact, contributing to the development of more sustainable cities.