Enterprise Resource Planning (ERP) is an integrated system widely used by large private companies. Its adoption in the public sector has enormous potential to improve operational efficiency and effectiveness. However, government organizations need to first assess their level of readiness to ensure the success of ERP implementation. Despite its importance, research on ERP readiness is still limited, especially in developing countries. This study develops and empirically tests an ERP adoption readiness model for government organizations in Indonesia using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) approach. To fill the gap in the literature, this study proposes the Technology, Organization, Environment, People, and Process (TOEPP) framework, which is an extension of the classic TOE model by adding the dimensions of People and Process. Based on data from 268 local government employees, the findings show that the five TOEPP constructs have a significant effect on ERP readiness. The theoretical contribution of this study lies in strengthening the TOEPP framework in the context of public sector digital transformation. Practically, this model serves as an evaluative guide for policymakers and IT managers in designing effective ERP adoption strategies in bureaucratic environments. This study emphasizes the importance of a holistic approach to assessing digital transformation readiness in the public sector. The results provide important insights to strengthen ERP initiatives, improve organizational readiness, and increase the chances of successful implementation. In addition, this study fills the research gap related to ERP pre-implementation preparation and offers a comprehensive, evidence-based framework for government agencies.

The COVID-19 pandemic has highlighted the importance of effective knowledge management in mitigating the impact of public health crises. Big data analytics can play a critical role in providing insights and informing decision-making during a pandemic. However, the challenges associated with collecting, analyzing, and managing the data, especially with privacy and security concerns, make it a complex task. This paper proposes a knowledge management framework for COVID-19 mitigation using a big data analytics approach. The framework includes a systematic process for data collection, analysis, and dissemination, as well as a set of best practices for knowledge management. Additionally, the framework complies with data protection and privacy regulations. The proposed framework aims to support public health officials and other stakeholders in effectively managing the COVID-19 pandemic by providing timely and accurate information. It can also be adapted and applied to other public health crises and be a useful tool for addressing the challenges associated with big data analytics in the context of public health. The paper presents the proposed framework in detail and provides components of how the framework can be applied to COVID-19 in Indonesia. Doi: 10.28991/ESJ-2023-SPER-015 Full Text: PDF

The large amount of coal production in Indonesia generates a significant amount of data that can be used to understand the rank of the coal. To effectively process and interpret this data, our study employs the use of big data techniques, including big data management and big data analysis. Big data management allows us to organize and understand the data patterns, while big data analysis is used to gain insights and knowledge about the data, such as coal rank analysis and identifying the type of coal. Our study uses a python-based approach to define variables and automatically classify the coal rank based on the threshold values obtained from the two basic analyses described earlier. Our results show that this method is able to accurately classify the coal according to the given threshold. We found that according to the Indonesian Coal Standardization based on Pusat Sumber Daya Mineral Batubara dan Panas Bumi (PSDBMP) standard, the calorific value (in adb) is dominated in low to medium calories for 14 boreholes. The coal rank in American Standard Testing and Material (ASTM) analysis is dominated by Lignite A and B for 14 boreholes. The last analysis, according to the atomic ratio, shows that the coal can be classified as Lignite and Subbituminous Coal. Thus, by implementing the big data concept, we can easily analyze the coal classification with comprehensive and large amount of data.

Counterfeit products have become a pervasive problem in the global marketplace, necessitating effective strategies to protect both consumers and brands. This study examines the role of cybersecurity in addressing counterfeiting issues, specifically focusing on a multi-level grayscale watermark-based authentication system. The system comprises a generator responsible for creating a secure 2D code, and an authenticator designed to extract watermark information and verify product authenticity. To authenticate the secure 2D code, we propose various features, including the analysis of the spatial domain, frequency domain, and grayscale watermark distribution. Furthermore, we emphasize the importance of selecting appropriate interpolation methods to enhance counterfeit detection. Our proposed approach demonstrates remarkable performance, achieving precision, recall, and specificities surpassing 84.8%, 83.33%, and 84.5%, respectively, across different datasets.

The requirement elicitation is the initial stage of requirement engineering where information collected from users. The process are significantly determined by the quality and quantity of information collected. The crowdsourcing is a method of information gathering from many users. The number and variety of users in the crowdsourcing are both advantages and challenges in the elicitation process. This study purposes a framework for user identification that consists of user mapping and architecture system. The identification process consists of 8 main states, start with defining context, user target and scope determination, data source determination, user data collection, data pre-processing, feature selection, data classification and user identification. The results of this study is an initial state for development of an automated tool for user identification to elicit requirement through crowdsourcing. By the framework can be generated the user classification, which can be used to apply the appropriate method for gathering information in elicitation process.