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All Journal International Journal of Information Technology and Computer Science Applications (IJITCSA)
Elizade, Akkord
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Analyzing SME's Data Visualization, Business Challenges, and Solutions: A Seven Stars Review Elizade, Akkord
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 2 (2024): May - August 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i2.146

Abstract

This comprehensive report encapsulates a thorough analysis conducted on the extensive Seven Stars dataset. Leveraging advanced data visualization techniques, the analysis has been meticulously executed to extract meaningful insights and elucidate intricate patterns within the dataset. By delving deep into the data, the report identifies a spectrum of potential business challenges that the organization may encounter, both in the present and future landscapes. These challenges encompass diverse realms such as market fluctuations, resource allocation, and operational inefficiencies. In response to the identified challenges, a robust set of solutions is proposed, tailored to address each issue methodically. Drawing upon best practices and industry expertise, these solutions aim to bolster the organization's resilience and competitiveness in the dynamic business environment. Moreover, to facilitate seamless data management and decision-making processes, a bespoke dashboard has been meticulously crafted. This intuitive dashboard serves as a centralized platform, enabling stakeholders to effortlessly manipulate and analyze data from disparate sources, thereby fostering informed decision-making and strategic planning. In essence, this paper serves as a comprehensive roadmap for the organization's data-driven journey, guiding it towards sustainable growth and success in an ever-evolving business landscape. Through diligent analysis, strategic foresight, and proactive problem-solving, the organization can chart a course towards long-term prosperity and resilience.
Hadoop Ecosystem Enhances Data Analytics for Music Streaming: A Case Study of User Behavior in the Last FM Dataset Elizade, Akkord
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 3 (2024): September - December 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i3.166

Abstract

This paper proposed a big data pipeline to analyze user behavior on Last.fm which aims to make data-driven recommendations for improving user engagement and attracting new users. The comprehensive analysis of user behavior in the music streaming industry using the Hadoop ecosystem and data analytics techniques. Specifically, the study focuses on Last.fm, a popular music streaming platform that collects large amounts of user activity data. The article proposes a new data pipeline utilizing Hadoop Distributed File System (HDFS) for data storage and Apache Pig for data transformation, leading to improved data preprocessing and analysis. Various analyses are conducted, including identifying the most listened to artists, top users based on song consumption and social connections, artist popularity by tags, and the most recently tagged artists. The findings provide valuable insights into user preferences, current trends, and opportunities for enhancing the recommendation algorithm and user engagement. The article concludes by offering recommendations for personalized marketing strategies and curated playlists to increase user satisfaction and revenue.
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