This study delves into the impact of employee recruiting within the dynamic and fiercely competitive realm of information technology (IT), focusing on the role of mobile software developers in a software development company situated in Bandung, Indonesia. Given that the quality of employees and their alignment with organizational needs are pivotal drivers of productivity and overall performance, the recruitment process assumes paramount importance. However, this process is riddled with complexity and challenges, stemming from the need to define precise criteria and navigate decision-making amidst uncertainty and ambiguity. To confront these challenges, this research advocates for the utilization of the Fuzzy Analytic Hierarchy Process (F-AHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS). The F-AHP method, employing Chang's extent analysis approach, assists in establishing weights for uncertain criteria. Meanwhile, F-TOPSIS is leveraged to evaluate alternatives based on predefined criteria. The focal point of this study is the selection of mobile software developers within a software development company in Bandung, Indonesia. Decision-makers, drawing insights from policy documents and assessment forms, identified pertinent criteria and sub-criteria. Utilizing F-AHP, they determined the weights for criteria and sub-criteria through paired comparisons using fuzzy numbers. Subsequently, F-TOPSIS was applied to rank 10 mobile software developer candidates, culminating in the identification of alternative-7 (CK-7) as the top mobile software developer candidate. In essence, the application of F-AHP and F-TOPSIS methods presents an effective approach to navigate the complexity of Multi-Criteria Decision Making (MCDM) in employee selection, particularly within the competitive landscape of the information technology industry. This study's findings underscore the significance of employing advanced decision-making techniques to enhance the efficiency and effectiveness of employee recruitment processes, thereby bolstering organizational performance and competitiveness.

The use of indicators is increasingly common, as they allow the visualization and analysis of useful data to assist in decision-making and planning. In this context, this article aimed to evaluate the process of automating indicators in the area of innovation in a multinational company located in Brazil, a transformer manufacturer. To this end, the themes of indicators and the application of Business Intelligence (BI) and process automation for project manager functions were conceptualized. The action research methodology developed automated indicators to facilitate decision-making and improve the productivity of the project team. These results also allowed for the improvement of visual and systemic management, by presenting all current projects and information on the occupancy rate of each team member, in addition to real-time control of whether goals are being achieved in the company. Thus, it was evident that centralizing data helps managers make more assertive decisions and the case reported can be replicated to other industrial sectors. In the academic sphere, the research also contributed to the evolution of the topic of project integration, automation and visual management of indicators using the Power BI tool.

The evolution of the Internet of Things (IoT) into the Social Internet of Things (SIoT) involves the integration of social networking features into smart devices. In this paradigm, smart devices emulate human social behavior by forming social relationships with other devices within the network. These relationships are leveraged for service discovery, emphasizing the need for robust security to foster collaboration and cooperation among devices. Security is paramount in the SIoT landscape, as malicious messages from devices can disrupt service functionality, impacting service quality and reliability. These challenges are particularly pronounced in social networks, introducing unique considerations such as heterogeneity and navigability. This study introduces a Security Framework for the Social Internet of Things, adopting a Relativity Strength Approach to enhance the security and reliability of IoT devices within social network contexts. The framework incorporates a relativity-based security model, utilizes Q-learning for efficient device navigation, and employs decision tree classification for assessing service availability. By optimizing hop counts and considering the strength of relationships between devices, the framework enhances security, resource utilization, and service reliability. The proposed security framework introduces a” Relationship key” derived from device-to-device relationships as a central element. This key, coupled with a standard 256-bit Advanced Encryption Standard (AES) algorithm, is employed for encryption and decryption processes. The relationship key technique ensures data protection during transmission, guaranteeing confidentiality and service integrity during network navigation. The system demonstrates an overall security effectiveness of 88.75%, showcasing its robustness in thwarting attacks and preventing unauthorized access. With an impressive overall communication efficiency of 91.75%, the framework minimizes errors and delays, facilitating optimal information trans- mission in smart environments. Furthermore, its 97.5% overall service availability assures a continuous and reliable user experience, establishing the framework’s capability to deliver secure, efficient, and highly accessible smart services.

The goal of the question-answering system is to respond to user queries expressed in natural language. Unlike search engines, the closed domain question answering systems are specialized to specific domains, providing concise and precise answers often derived from structured data. This paper focuses on a question-answering system tailored for crime events, capable of addressing both statistical and contextual inquiries. In terms of crime statistics, the fine-tuned GPT-3 model outperforms the USE, TAPAS, TAPEX, and GPT-3 models, while for context-based crime-related queries, the fine-tuned RoBERTa model surpasses the BERT and RoBERTa models. This system is capable of providing the responses in natural language format, supplemented with relevant data visualizations. The models are train on Q2A and NewsQA datasets while it is tested on NCRB and NewsTimes datasets. The Q2A and NCRB datasets are used for statistical queries while NewsQA and NewsTimes datasets are used for contextual inquiries. The paper presents an analysis of various models and showcases results for sample case studies. Such a system can prove valuable in applications where users seek to study criminal cases or gather pertinent insights for specific cases. Furthermore, it can assist in understanding patterns and trends in criminal events, particularly concerning geospatial information. Linking crime event-based question-answering systems to geospatial information facilitates exploration of niche areas and furnishes precise details about local crime with minimal hype and hence worth exploring.

The purpose of the research is to develop mathematical models, solution methods and layouts of tools for problems solving of integrating information resources and creation of intelligent systems of business analytics based on effective models. These problems can be solved by automating the business processes execution and introducing artificial intelligence components into the business processes management systems. It can be said that the essence of the modern stage of the business processes modelling systems development is the transition from mainly manual (or with the use of auxiliary software) methods of business processes analysis to mainly automatic management of the business processes execution, construction of intelligent business processes networks in the interconnected conceptual models’ set form that encapsulate knowledge about the structure, the business processes features, system events, limitations and dependencies and are processed by machine. Decision-making powers are delegated to such information system in clearly defined (most often simple, routine) situations. So, in this way, it is possible to form the information resource of intelligent systems of business analytics as a single coherent set of data, suitable for use in solving a wide range of multifaceted problems. The integration approach of forming information resources has certain advantages over other approaches, in particular, regarding the information resources of intelligent systems of business analytics. The use of integration as a means of forming a set of consistent data has certain advantages, namely, it allows: combine data of different formats, content and origins in a single, consistent set; combine data without converting them to a single format, which is especially important when such conversion is difficult or impossible; creates virtual custom images of data that do not depend on their real appearance; creates opportunities to operate both real physical and virtual data in their combination; dynamically supplement, change and transform both the data itself and their descriptions; to provide uniform methods and technologies of perception and application of a large amount of various data.

Currently attendance is still done manually by recapping each lecturer's attendance sheet. The method used is the prototype method and testing using the User Acceptant Test (UAT) method. This takes a long time, even misinterpretations of existing absences sometimes cause problems when giving salary receipts to lecturers, besides that, reporting to campus management also takes time. The lecturer attendance system can help the finance department to calculate lecturer teaching attendance faster and more easily, which can be used to calculate salaries and evaluate lecturer attendance. The system at the implementation stage and during implementation the average teaching attendance of lecturers is easier to control. Some of the features in this attendance system include Check-in, Lecturer, profile, History, Schedule, Help, Tutorial and Chat Group. Apart from that, the main menu also provides information regarding the check-in time limit, waiting time for the next check-in, campus information, and application updates. The system was built with a QR Code and is Android-based to make things easier for lecturers, admin, and the finance department.