Advanced Systems Design and Analysis Journal

Analysis of the Effectiveness of Using Agile Methods in Developing the Attendance Information System of SMK SBS (Syntax Bussines School) Kuningan

2024-12-05T04:33:39+00:00

This study aims to analyze the effectiveness of the application of the Agile method in the development of an attendance information system at SMK SBS Kuningan. With a quantitative descriptive approach, this study evaluates system performance based on development time, end-user satisfaction, and system flexibility in dealing with changes. The study population included IT staff, teachers, and school administration officers who use the attendance system, with purposive sampling techniques used to select 30 relevant respondents. Data were collected through questionnaires, observations, and documentation, and analyzed using quantitative descriptive analysis techniques. The results showed that the Agile method was able to shorten development time by up to 30% compared to traditional methods, as well as increase user satisfaction in terms of ease of use, reliability, and flexibility. The iteration diagram shows Agile's ability to respond to changes effectively, minimize errors, and allow for feature adjustments according to user needs. This study reveals that the application of Agile in secondary education institutions is effective and can be a development model for other institutions that have limited resources but require high flexibility. This study fills the literature gap related to the application of Agile in the development of information systems in secondary schools, as well as providing practical contributions to more efficient and adaptive attendance management. Thus, it is hoped that the results of this study will be a reference for other educational institutions that wish to improve their information systems through Agile methods.

Big Data Approach to Product Demand Prediction Using Machine Learning Based Prediction Models

2024-12-06T03:59:30+00:00

This study explores the application of a machine learning-based big data approach to predict product demand, focusing on a Long Short-Term Memory (LSTM) model developed using big data from the manufacturing and distribution sectors. Improving the accuracy of product demand prediction is the main objective of this study, as prediction accuracy can significantly impact a company's supply chain efficiency and inventory management. Using historical data and real-time data obtained from sales history, search trends, and other external factors, a machine learning model is trained and evaluated using the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) metrics. The results show that the LSTM model excels in predicting fluctuating and seasonal demand patterns compared to other algorithms, such as linear regression and decision trees, with an accuracy rate of up to 90% on test data. The big data approach used in this study allows the analysis of external factors that influence product demand, making the prediction model more responsive to market changes. These findings provide significant contributions to supply chain management, reducing the risk of shortages and excess stock, and increasing company profitability. This research is expected to be a reference for companies and academics in developing more adaptive and responsive demand prediction models based on big data.

Advanced Systems Analysis: Design and Implementation Strategies to Support Digital Transformation

2024-12-06T04:28:29+00:00

This study aims to explore the design and implementation of advanced systems that support digital transformation through the integration of technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and cloud computing. The study uses a qualitative descriptive approach, involving in-depth interviews with IT managers, systems analysts, and decision makers from ten companies in the technology, finance, manufacturing, and healthcare sectors. The results show that advanced system integration has a significant impact on operational efficiency, increasing productivity by up to 25% and accelerating decision-making processes by an average of 30%. However, several obstacles were found, including infrastructure limitations and employee resistance to technology adaptation. The study also identified that data security is a priority in system design, with the use of multi-layer encryption as a protective measure. The integration model developed in this study strengthens real-time data management, allowing companies to adapt more responsively to market changes. These findings provide practical guidance for companies in optimizing the adoption of advanced systems and addressing digitalization challenges through a more sustainable strategic approach. The results of this study are expected to be an important contribution to the digital transformation literature, especially in the context of implementing comprehensive and secure advanced systems.

Information System Security: Maintaining Data Integrity in Service Companies (Case Study: PT. Syntax Corporation Indonesia

2024-12-06T04:39:28+00:00

Information security has become a major focus for service companies in the digital era, given the increasing threats to data integrity. This study aims to explore data security strategies at PT. Syntax Corporation Indonesia, by highlighting the policies and technologies implemented and the challenges faced in maintaining information security. The research method used is descriptive qualitative with a case study approach. Data were collected through in-depth interviews, observations, and documentation related to security policies, technical procedures, and user compliance with security protocols. The results of the study indicate that although PT. Syntax Corporation has implemented effective data access control and encryption, its implementation is still constrained by the lack of user training and compliance and limited budget for technology upgrades. These findings indicate the need for periodic security audits, policy updates, and routine training for employees to raise awareness of the importance of security protocols. In conclusion, service companies such as PT. Syntax Corporation Indonesia need a comprehensive security strategy, combining aspects of technology and user awareness to optimally maintain data integrity. These recommendations are expected to be a reference for other companies facing similar challenges in an effort to strengthen their information security systems

Systematic Risk Assessment and Mitigation Strategies in Cloud-Based Data Storage Systems

2024-12-09T02:57:56+00:00

This study explores the risks and mitigation strategies in cloud-based data storage systems, with the aim of identifying key security threats and effective mitigation solutions for cloud service providers in Indonesia. Cloud systems, despite offering high flexibility and scalability, face significant risks such as data security threats, vulnerability to cyberattacks, and user privacy issues. A qualitative descriptive research method was used with a case study approach involving interviews and questionnaires to several cloud service provider companies. Data collection was conducted through structured interviews with information security managers and internal users, who provided data on mitigation strategies such as encryption, access protection, and 24/7 monitoring. The results showed that the main threats to cloud systems consisted of data security risks (score 4.5), cyberattacks (4.7), user privacy (4.2), and service availability (4.3). The most effective mitigation strategies were data encryption and continuous monitoring, each showing high effectiveness with an average score of 4.8 and 4.6. These findings are in line with previous studies showing that encryption and layered monitoring systems are optimal methods for maintaining data security in cloud environments. In terms of practical implications, this study provides guidance for cloud service providers to implement multifactor authentication and stronger privacy policies to mitigate risks. For future research, it is recommended that AI-based security methods and advanced authentication be further explored to add layers of protection to user data in cloud systems. These findings reinforce the need for a systematic and multi-layered security approach in cloud-based data management in the increasingly digital era.