Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi

Unveiling the Collective Wisdom: A Review of Swarm Intelligence in Problem Solving and Optimization

Hewa Majeed Zangana , Zina Bibo Sallow, Mohammed Hazim Alkawaz, Marwan Omar — Fri, 10 May 2024 00:00:00 +0700

Swarm intelligence, inspired by the collective behaviour of natural swarms and social insects, represents a powerful paradigm for solving complex optimization and decision-making problems. In this review paper, we provide an overview of swarm intelligence, covering its definition, principles, algorithms, applications, performance evaluation, challenges, and future directions. We discuss prominent swarm intelligence algorithms, such as ant colony optimization, particle swarm optimization, and artificial bee colony algorithm, highlighting their applications in optimization, robotics, data mining, telecommunications, and other domains. Furthermore, we examine the performance evaluation and comparative studies of swarm intelligence algorithms, emphasizing the importance of metrics, comparative analysis, and case studies in assessing algorithmic effectiveness and practical applicability. Challenges facing swarm intelligence research, such as scalability, robustness, and interpretability, are identified, and potential future directions for addressing these challenges and advancing the field are outlined. In conclusion, swarm intelligence offers a versatile and effective approach to solving a wide range of optimization and decision-making problems, with applications spanning diverse domains and industries. By addressing current challenges, exploring new research directions, and embracing interdisciplinary collaborations, swarm intelligence researchers can continue to innovate and develop cutting-edge algorithms with profound implications for science, engineering, and society.

The Influence of Artificial Intelligence Technology, Infrastructure and Human Resource Competence on Internet Access Networks

Muryan Awaludin, Verdi Yasin, Fitria Risyda — Wed, 29 May 2024 00:00:00 +0700

The influence of artificial intelligence technology, infrastructure, and human resource competence on Internet access networks has been examined in a scientific publication, which is the outcome of a literature review in the information systems sector. The purpose of this study is to generate an influence hypothesis related to factors that can be applied in further investigations. The research's subjects include academic media, Google Scholar, Mendeley, and online libraries. Publicly accessible e-books and e-journals are the source of the research methodology that makes use of library institution searches. The following is a descriptive qualitative analysis of this article's findings: The influence of artificial intelligence technology on networks for Internet access, the influence of infrastructure on networks for Internet access, and the Influence of HR Competency on Internet Access Networks. Research findings regarding the impact of advances in infrastructure, human resource competence, and artificial intelligence technology on internet network access include improving user experience, extensive network scalability, increasing network efficiency and security, as well as increasing human resource knowledge and developing network architecture research findings regarding the impact of advances in infrastructure, human resource competence, and artificial intelligence technology on internet network access include improving user experience, extensive network scalability, increasing network efficiency and security, as well as increasing human resource knowledge and developing network architecture.

Sentiment Analysis of Student Complaint Text for Finding Context

Mon, 03 Jun 2024 09:27:19 +0700

Students are one of the users of services provided by institutions. Student complaint questionnaires were distributed to find out various complaints related to institutional services. The problem is how to interpret student complaints so that institutional services can be improved according to what is needed. Therefore, this research aims to analyze the sentiment of student complaints. Apart form that, this research also aims to find the context of each student's complaint regarding institutional services. This research uses sentiment analysis with Indonesian text. The method starts from data collection, data cleaning and labelling of complaints, pre-processing of complaint text, term frequency (TF) method used to extract the content, and accuracy measurement. The labeling process was carried out twice to compare the accuracy, precision, recall, and f1-score of the models. Based on the results of the sentiment analysis of student complaints, the accuracy rate reached 76.1%. Additionally, a precision of 65.2% and recall of 85.2% indicate that labeling is more balanced, although there is still room for improvement.