Jurnal Informasi dan Teknologi

Analysis of Apache Hadoop Architecture in Supporting Large-Scale Data Processing

Teuku Nabil Muhammad Dhuha — 2025-12-30

The rapid development of information technology has led to the exponential growth of data generated from various sectors, such as healthcare services, social media, information systems, and other digital activities. This condition has given rise to the concept of big data, which cannot be optimally processed using conventional data processing technologies. Therefore, distributed computing platforms are required to efficiently handle large-scale data storage and processing. Apache Hadoop is one of the widely used big data technologies due to its distributed architecture that supports scalability, parallel processing, and fault tolerance. This study aims to analyze the architecture of Apache Hadoop and explain the role of each of its components in supporting large-scale data processing. The research method employed is a qualitative literature study, conducted through the review of books, scientific articles, and related publications on Hadoop. The results indicate that Hadoop consists of three main components: the Hadoop Distributed File System as a distributed storage system, MapReduce as a programming model for parallel data processing, and Yet Another Resource Negotiator, which functions in cluster resource management and scheduling. The integration of these components enables Hadoop to manage large-scale data in a reliable and distributed manner. However, Hadoop has limitations related to its batch-based processing model, which is less suitable for real-time processing needs, thus requiring consideration of complementary technologies according to application requirements.

Mapping Digital Financial Management in Developed and Developing Countries through Bibliometric Analysis

Muhammad Ijlal Siraj Muyassar — 2025-12-30

Digital financial management has become an important pillar of global economic progress, transforming the way individuals, businesses and countries manage finances. In developed countries, digital banking and payment systems are rapidly evolving, while in developing countries, the adoption of digital technologies continues to increase, as seen by the widespread access to fintech and mobile banking services. Since 2017, research on technology in financial management has shown an upward trend, with bibliometric analysis used to map trends, challenges and influencing factors across different countries. The early 2000s were a pivotal moment in the evolution of digital financial management, driven by the use of smartphones as the primary access to the internet. Research from 1706 to 2025 recorded exponential growth, especially since 2014. Researchers utilise platforms such as Google Scholar to disseminate information widely. Keywords such as “Fintech,” “big data,” and “digital finance” indicate further research opportunities. Fintech has revolutionised financial services with technologies such as blockchain and big data, increasing efficiency, transparency, and personalisation of services, and driving innovation in the global financial ecosystem.

Classification of Tourist Attractions in Central Aceh District using the C4.5 Decision Tree Algorithm

Amny Yasira — 2025-12-30

Central Aceh Regency is a region with rapidly growing tourism potential, characterized by lakes, mountains, and cultural sites typical of the Gayo people. Although the available tourist attractions are quite diverse, the presentation of unstructured information often makes it difficult for tourists to determine destinations that suit their needs and preferences. To address this problem, this study implemented the C4.5 decision tree algorithm to classify tourist attractions in Central Aceh Regency. The study used five main attributes: type of tourism, accessibility, facilities, ticket prices, and the Number of annual visitors. Data were obtained through field observations, interviews, and online reviews, with a total of 54 tourist attractions being sampled. The analysis process began with data preprocessing, entropy calculations, and information gain and gain ratio to construct a decision tree. The modelling results showed that the accessibility attribute produced the highest gain ratio and became the root node in the tree. Furthermore, the Number of visitors attributed became the dominant factor in the next branch, consistently distinguishing the classes. The classification system resulted in three recommendation categories: Highly Recommended, Recommended, and Not Recommended. Model evaluation using a confusion matrix showed 92% accuracy, 90% precision, and 90% recall, indicating that the C4.5 algorithm is effective at grouping tourist attractions based on their characteristics. This research contributes to a data-driven model that can help tourists obtain more systematic information, while also supporting local governments and tourism stakeholders in developing more targeted destination development strategies.