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Jurnal Informasi dan Teknologi
Published by Universitas Putra Indonesia YPTK Padang
ISSN : 27149730     EISSN : 27149730     DOI : https://doi.org/10.37034/jidt
Core Subject : Science,
Computer Science & IT
Jurnal Informasi & Teknologi media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
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Search results for , issue "2025, vol. 7, no. 4" : 5 Documents clear
Analysis of Apache Hadoop Architecture in Supporting Large-Scale Data Processing Teuku Nabil Muhammad Dhuha; Asrianda; Muhammad Fikry
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.711

Abstract

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; Erwin Budianto; Muhamad Wildan Maulana; Adi Setiawan
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.720

Abstract

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; Dahlan Abdullah; Cut Agusniar
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.715

Abstract

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.
Application of K-Means Clustering for Customer Segmentation on Sales Data in a Sheet Plastic Manufacturing Company Kristiano Moniaga; April Lia Hananto Moniaga; Tukino; Shofa Shofiah Hilabi
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.740

Abstract

Customer segmentation is applied to sales transaction data from a sheet plastic manufacturing company covering the 2019–2025 period and obtained from the company’s Enterprise Resource Planning (ERP) system. This study aims to identify heterogeneous customer characteristics and generate actionable market segments based on Recency, Frequency, and Monetary (RFM) values using the K-Means Clustering algorithm. The methodology comprises systematic data cleaning, transaction aggregation, RFM calculation, feature normalization, cluster modeling, and determination of the optimal number of clusters through the Elbow Method and Silhouette Score. The final dataset consists of 46,372 transactions involving 1,223 active customers, with a cumulative transaction value of IDR 722.4 billion. The findings reveal five optimal clusters, validated by a Silhouette Score of 0.513, indicating reasonably good clustering quality and meaningful separation among customer segments. The segmentation identifies five distinct customer groups: Low Engagement (50%), characterized by limited transaction activity and requiring targeted reactivation strategies; Churn (37%), representing long-inactive customers at significant risk of disengagement and requiring structured re-engagement programs; Potential (11%), comprising active customers with substantial transaction values and strong development opportunities; Key Account (less than 1%), representing strategically important customers with the highest business contribution and requiring prioritized relationship management; and High Value (2%), consisting of loyal, profitable customers who should be retained through personalized loyalty initiatives. These findings demonstrate that integrating RFM analysis with K-Means Clustering provides a practical, data-driven approach to understanding customer heterogeneity in manufacturing markets. The resulting segmentation framework offers a strategic foundation for targeted retention initiatives, personalized promotional campaigns, improved customer relationship management, optimized allocation of sales resources, and more effective managerial decision-making based on measurable customer behavior and long-term value
The Development of Research on Marketing Engagement a Bibliometric Analysis Syafira Khania; Milenia Inez Safitri; Adi Setiawan
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.746

Abstract

Engagement marketing has emerged as a strategic approach that emphasizes interactive, personalized, and value-driven relationships between brands and consumers. The rapid growth of digital technologies, social media, and smart devices has fundamentally transformed customer engagement, making it a central element of contemporary marketing strategies. This study aims to examine the evolution, research trends, and intellectual structure of engagement marketing literature, with particular emphasis on its relationship with digital transformation and small and medium-sized enterprises (SMEs). A qualitative historical approach combined with bibliometric analysis was employed to systematically evaluate publications indexed in Scopus, Emerald, Springer, ProQuest, Web of Science, and ScienceDirect. Article retrieval was conducted using the Publish or Perish software for the period 1956–2025, applying relevant keywords related to engagement marketing, internet marketing, business models, and brand engagement. After applying inclusion criteria, including English-language journal articles in management, business, economics, social sciences, information systems, and entrepreneurship, a total of 2,302 publications were analyzed. Bibliometric mapping and visualization were performed using VOSviewer to generate network, overlay, and density maps that reveal research collaborations, thematic evolution, and emerging research opportunities. The findings indicate a substantial increase in engagement marketing publications, particularly between 2016 and 2022, reflecting the accelerated adoption of digital technologies and electronic markets. Collaboration analysis demonstrates strong and expanding research networks among scholars, while overlay visualization highlights growing attention to business strategy and digital transformation. Density visualization further identifies several underexplored research themes, indicating promising opportunities for future investigation. This study provides a comprehensive scientific mapping of engagement marketing research and offers strategic directions for future studies focusing on digital transformation, customer engagement, innovation, and sustainable SME competitiveness

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