Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI)

Accrual-Based Accounting Information System with Break Even Point (BEP) Approach

Kadek Nonik Erawati — 2026-02-20

The digital transformation of financial management is essential for modern business sustainability. However, traditional cash-based systems fail to represent true financial positions, while existing accounting software often lacks integrated managerial analytics tools. This creates a functional disconnect where managers must perform manual, error-prone calculations to determine profitability thresholds. To address this, this research develops a web-based Accrual Accounting Information System integrated with a dynamic Break Even Point (BEP) approach. The system automates the double-entry recording process and real-time classification of fixed costs from the general ledger to visualize safety margins instantly. The primary contribution of this study is the unification of professional accounting standards (PSAK) with strategic decision-support algorithms in a single platform. Evaluation using Black Box testing confirms the system achieves 100 percent accuracy in generating financial statements and BEP metrics, while usability analysis demonstrates that the responsive architecture significantly enhances workflow efficiency across devices. The results indicate that the system effectively transforms passive financial data into actionable insights, empowering proactive decision-making. Future work aims to incorporate machine learning for dynamic semi-variable cost analysis to further refine predictive capabilities.

Design of Accrual-Based Accounting Information System Using Full Costing Method

Dewa Ayu Giovany Angga Indrya — 2026-02-20

Accounting Information Systems (AIS) play an important role in supporting organizational decision-making by providing accurate and timely financial information. The adoption of accrual-based accounting is particularly essential for organizations engaged in production activities, as it enables more reliable recognition of revenues and expenses and supports transparent financial reporting. However, many organizations still rely on manual or cash-based accounting practices, which often lead to inaccurate cost calculations, incomplete financial information, and limited support for managerial decision-making. This research is motivated by the need to integrate accrual accounting principles with a comprehensive cost calculation approach to overcome these limitations. This study proposes the design and implementation of an accrual-based Accounting Information System that applies the Full Costing method to allocate both direct and indirect production costs, including direct materials, direct labor, and manufacturing overhead. The main contribution of this research lies in the integration of accrual-based transaction processing and Full Costing calculations within a unified system that supports accurate cost determination and financial transparency. The proposed system was evaluated through functional testing and user validation to assess its effectiveness in transaction recording, cost calculation, and financial reporting. The evaluation results indicate that the system improves data consistency, reduces manual processing errors, and generates more comprehensive cost and financial reports compared to conventional accounting practices. Despite these positive results, the current system implementation relies on static cost allocation rules. Future work will focus on enhancing the system by incorporating dynamic cost drivers, advanced analytical features, and integration with other enterprise systems to improve scalability and decision-support capabilities.

Development of Accrual-Based Accounting Information System for Financial Planning

Muslimin B — 2026-02-20

The increasing complexity of financial management in small and medium-sized enterprises (SMEs) requires the implementation of robust accounting systems. While accrual accounting provides more accurate financial insights by recognizing revenues and expenses when incurred, many SMEs still rely on cash-based accounting, hindering their financial decision-making. This research aims to develop an accrual-based accounting information system tailored for SMEs, integrating essential features such as cost control and forecasting. The proposed system automates key processes, from transaction entry to report generation, offering a comprehensive solution to enhance financial transparency and decision-making. The system is evaluated through real-world data simulations to assess its effectiveness in improving reporting accuracy and forecasting capabilities. The results demonstrate that the system improves financial planning and resource allocation, providing valuable insights for SMEs. Future work will focus on scaling the system for larger enterprises and incorporating machine learning techniques to improve financial forecasting and anomaly detection.

Crop Yield Prediction Using Random Forest Based on Soil, Climate, and Agronomic Factors

Putu Sugiartawan — 2026-02-20

Agricultural yield prediction plays a critical role in ensuring food security and optimizing farming practices. Traditional methods of crop yield estimation often rely on expert knowledge and historical data, which can be limited and inaccurate. Machine learning algorithms, particularly Random Forest, have shown promise in improving the accuracy of crop yield predictions by considering complex interactions between soil, climate, and agronomic factors. This study aims to develop a Random Forest-based model to predict crop yield using a diverse set of agricultural datasets. The model was trained and validated using data from multiple regions, focusing on soil properties, climatic conditions, and farming practices. The results demonstrated that the Random Forest model provided reliable predictions, with performance evaluated using metrics such as MAE, RMSE, and R². However, some discrepancies between actual and predicted values were observed, indicating room for improvement. Future work will focus on integrating real-time data, such as soil moisture and pest infestation, to enhance the model's accuracy. Additionally, exploring advanced machine learning techniques like deep learning could provide better handling of complex patterns in agricultural data. This research contributes to the growing field of agricultural data science and aims to provide a scalable solution for crop yield prediction across various regions.

Forecasting the Jakarta Composite Index (IHSG) Using the Moving Average Method

Heru Ismanto — 2026-02-20

Financial market indices play a crucial role in reflecting economic conditions and supporting investment decision-making. In Indonesia, the Jakarta Composite Index (IHSG) serves as a key benchmark for evaluating overall stock market performance. Due to its dynamic and volatile nature, accurate forecasting of IHSG movements remains a challenging task in financial time series analysis. Many recent studies employ complex machine learning and deep learning models, which often require substantial computational resources and lack interpretability, limiting their practical adoption. Motivated by the need for transparent and easily implementable forecasting approaches, this study investigates the use of the Simple Moving Average (SMA) method as a baseline model for forecasting the IHSG. The main contribution of this research lies in providing a systematic evaluation of the moving average method using different window sizes and standard error metrics. Historical IHSG data are preprocessed, analyzed descriptively, and divided into training and testing datasets. Short-term forecasts are generated by applying the SMA model with varying window configurations. The performance of the proposed approach is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results demonstrate that the moving average method is capable of capturing the general trend of the IHSG, with forecasting accuracy strongly influenced by the choice of window size. Future work may focus on integrating additional forecasting techniques, incorporating exogenous variables, and developing hybrid or adaptive models to further enhance prediction accuracy and robustness.