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This study aims to examine the implementation of Interpretation of Financial Accounting Standards (ISAK) 335 in the financial management of non-profit foundations in Tanah Datar Regency, particularly in Rambatan and Sungai Tarab Districts. The study employed a qualitative case study approach using semi-structured interviews with financial managers from four foundations: Yayasan SLB Az-Zahra, Yayasan Darul Ulum/Darul Hafazah, Yayasan Daarut Tahfidz Al-Sulaiman, and Yayasan Jabal Rahmah. Data were analyzed using thematic analysis to identify the level of ISAK 335 implementation and the factors affecting its adoption. The findings reveal that none of the foundations have implemented ISAK 335 in preparing their financial statements. The main obstacles include the limited availability of personnel with accounting expertise, simple bookkeeping practices focused only on cash inflows and outflows, inadequate understanding of accounting standards for non-profit entities, and dependence on a single source of operational funding. Consequently, the financial statements do not comply with applicable accounting standards, resulting in low levels of transparency, accountability, and financial information quality. This study recommends enhancing the capacity of financial managers through training, gradually adopting ISAK 335, utilizing digital bookkeeping systems, and diversifying funding sources to strengthen accountable financial governance and ensure organizational sustainability.

Breast tumors are classified into two types, namely benign and malignant tumors, the latter commonly referred to as breast cancer. Breast cancer is one of the major health problems affecting women worldwide, including in Indonesia. According to WHO data in 2022, there were 2.3 million breast cancer cases with 685,000 deaths globally, while in Indonesia, 396,914 cases and 234,511 deaths were reported. The high incidence rate is exacerbated by low public awareness in recognizing early symptoms and performing early detection, resulting in 60–70% of cases being diagnosed at an advanced stage, supported by findings that 65.6% of female students still have a low level of knowledge. Female students were selected as research subjects because they are in a vulnerable reproductive age group and have an important role in increasing awareness of early detection, yet they still have limited knowledge. Based on this, this study aims to design a web-based early detection system for breast tumor risk using the Forward Chaining method, which functions as a tool to identify early symptoms, assess risk levels, and provide information on prevention and initial management. This study employed the method with the Expert System Development Life Cycle (ESDLC) model, consisting of the stages of assessment, knowledge acquisition, design, testing, and documentation, along with the Forward Chaining inference method.

This research was conducted with the aim of developing an automation system for recording warehouse inventory based on Google Sheets to improve the effectiveness and efficiency of the stocktaking process at the Cresta Management warehouse. This research was based on problems in the semi-manual process that used logbooks and separate files for each group of goods, which caused problems such as data duplication, limited data access, lack of data transparency, and lengthy recapitulation process. This research used the Research and Development method using the ADDIE model (Analyze, Design, Develop, Implement, and Evaluate). Data were collected through observastion, interviews, questionnaires, and documentation. The result of this development is a system consisting of a login page, goods transcation recording, warehouse dashboard, stock reports, stock cards, and PDF report exports. This study also produced evaluation scores regarding the system’s feasibilty: 93,3% from the first media expert, 80% from the second media expert, 73,3% from the material expert, and 91,75% from users through User Acceptance Testing. These evaluation results indicate that the system is feasible for use and implementation in supporting warehouse operations, as it provides real time data updates, improves the efficiency of goods reporting, and enhances warehouse transactions.

In this era of ongoing digitalization, the use of information technology in records management is becoming increasingly crucial to ensure work efficiency and effectiveness. This study aims to examine the implementation of a dynamic archive storage system in the General Ledger Accounting Division of PT KPN Plantation. Specifically, this study identifies the obstacles encountered and analyzes the efforts made to overcome these challenges. Using a qualitative approach, primary data was collected through observation, interviews, and documentation studies with informants from General Ledger Accounting employees. Data analysis was carried out through a series of processes, including data collection, data reduction, data presentation, and conclusion drawing. The results of the study show that although the stages of archive management have been carried out systematically through a combination of physical procedures and digitization using E-Archive, its implementation still faces significant obstacles. The problems identified include managerial aspects such as high transaction volumes that cause document backlogs and space limitations, technical aspects such as the instability of the E-Archive system, and limited human resources with expertise in archiving. Collectively, these obstacles reduce the effectiveness and efficiency of the system, requiring a comprehensive and integrated improvement strategy to achieve reliable and sustainable archive management

Digital transformation (DT) has become a critical component for organizations aiming to enhance their operational efficiency, innovation, and competitiveness. However, many organizations struggle to achieve successful digital transformation due to the misalignment between their Enterprise Information Systems (EIS) and organizational strategic goals. This research seeks to design and validate a model for aligning EIS with digital transformation strategies to improve organizational effectiveness. By adopting the Design Science Research (DSR) approach, this study develops a practical model that integrates strategic planning, process management methodologies, and emerging technologies to facilitate alignment between IT and business strategies. The research includes key steps such as requirement analysis, artifact design, expert validation, and case study evaluation to ensure the model's robustness and applicability across different organizational contexts. Findings indicate that the proposed model significantly improves strategic-system alignment, enhances decision-making consistency, and facilitates better integration between business and IT units. The model also addresses common challenges such as resistance to change, skill gaps, and misalignment, fostering a supportive culture for digital transformation. In comparison to existing descriptive frameworks, the proposed model is more structured, adaptable, and actionable, providing organizations with a clear framework to guide their digital transformation efforts. This research contributes to the growing body of knowledge on EIS alignment and offers practical insights for organizations seeking to achieve successful digital transformation. Future research could explore the model's application in various organizational settings and examine its impact on long-term organizational growth and innovation.

This research investigates the development of IT-based Automatic Identification System (AIS) data surveillance models supporting maritime safety through integration of advanced information technology, maritime engineering principles, and human factors optimization. AIS technology generates vast real-time vessel movement data creating unprecedented opportunities for safety enhancement through systematic surveillance, collision risk detection, traffic pattern analysis, and incident prevention, yet effectiveness depends critically on intelligent data processing algorithms, reliable IT infrastructure, and competent personnel capable of interpreting surveillance outputs and taking appropriate actions. Through qualitative analysis involving maritime safety authorities, vessel traffic service (VTS) operators, port authorities, marine engineers, IT specialists, data scientists, and maritime training institutions, this study examines how IT-based surveillance models incorporating pattern recognition, anomaly detection, predictive analytics, and crew-centered interfaces can transform maritime safety management from reactive incident response toward proactive risk prevention. Results demonstrate that intelligent AIS surveillance can identify 75-90% of high-risk situations 15-45 minutes before critical events, reduce collision risks by 60-80%, improve traffic management efficiency by 35-55%, and enhance crew situational awareness by 45-65% when integrated with appropriate training programs developing personnel competencies in data interpretation, system operation, and coordinated response. Key implementation challenges include data quality and completeness issues, computational infrastructure requirements, algorithm development complexity, personnel competency gaps requiring substantial training investments, organizational coordination barriers, and privacy/security concerns. Findings reveal that successful AIS surveillance implementation requires holistic sociotechnical approaches integrating IT systems engineering, maritime domain expertise, and human capability development through coordinated design, deployment, and training strategies. This research contributes to maritime safety literature by providing integrated frameworks for IT-based surveillance systems incorporating technical capabilities, operational requirements, and human factors supporting evidence-based safety management.