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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) International Journal Software Engineering and Computer Science (IJSECS)
Ahmad Bilal Almagribi
Universitas Teknologi Digital Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Apache Spark for Business and Financial Data Engineering: A Systematic Literature Review Ahmad Bilal Almagribi; Bambang Purnomosidi Dwi Putranto
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5419

Abstract

This paper is an SLR that maps the application of Apache Spark in data engineering in the business and finance domains. Practitioners and researchers alike would find it interesting to know how Apache Spark has been applied to solve big data problems as organizations continue to deal with large volumes of data. By analyzing publications from the Scopus database for 2021-2025, we try to find trends and methodologies currently in use as well as gaps in research existing in the field. It was found that Apache Spark is primarily used for sentiment analysis and trend analysis on social media, particularly Twitter, since its real-time processing capability can help understand market dynamics and consumer behavior. The platform carries out predictive tasks like predicting customer churn or pricing financial assets (stocks, bonds, options), proving its versatility across different business applications. Also, this platform is popular for anomaly detection such as transaction fraud with efficiency and cost being the main drivers of adoption. The landscape is not monolithic since some studies propose alternative platforms indicating that Apache Spark may not be the best option for every scenario. Based on our findings, we suggest future research directions that would push the boundaries of the field: using social media data sources other than Twitter for more general market sentiment, applying more varied algorithms to improve prediction accuracy, and extending Spark's application into new areas like currency exchange rate forecasting, credit risk analysis, Anti-Money Laundering (AML) detection as well as Data Lakehouse architecture implementation. These recommendations are meant to steer researchers toward uncharted territories where significant value could be unlocked for business and finance with the help of Apache Spark.
Mapping Research on the Use of Algorithms in Commerce: A Bibliometric Analysis Based on Scopus Ahmad Bilal Almagribi; Fikky Ardianto; Anas Taufan; Domy Kristomo
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3441

Abstract

Algorithms had garnered widespread attention across various scientific disciplines, including the commercial sector. According to data from Scopus, over 600 documents exploring the application of algorithms in commerce were identified. However, no comprehensive bibliometric analysis had been conducted to deeply examine the implementation of algorithms within this sector. This research aimed to fill this gap by analyzing the contributions of authors, affiliations, countries, and journals within the literature on commercial algorithms. Employing bibliometric methods on 645 Scopus-indexed documents, this study revealed that 2022 marked the peak of publications with 112 documents, indicating significant growth in this area. Li, Y. from Wuhan College, China, was recognized as the most productive author. Additionally, several universities in China were noted as the most productive affiliations. The ACM International Conference Proceeding Series was the most prolific source on this topic. The study also identified Computer Science, Engineering, and Mathematics as the most popular subject areas. These results indicate a need for further research into aspects such as data privacy, User Experience (UX), Dynamic Pricing Algorithms, and blockchain technology to enhance efficiency and security in commercial applications. This research paves the way for a broader understanding of algorithm utilization in commerce and provides recommendations for future studies.
Apache Spark for Business and Financial Data Engineering: A Systematic Literature Review Ahmad Bilal Almagribi; Bambang Purnomosidi Dwi Putranto
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5419

Abstract

This paper is an SLR that maps the application of Apache Spark in data engineering in the business and finance domains. Practitioners and researchers alike would find it interesting to know how Apache Spark has been applied to solve big data problems as organizations continue to deal with large volumes of data. By analyzing publications from the Scopus database for 2021-2025, we try to find trends and methodologies currently in use as well as gaps in research existing in the field. It was found that Apache Spark is primarily used for sentiment analysis and trend analysis on social media, particularly Twitter, since its real-time processing capability can help understand market dynamics and consumer behavior. The platform carries out predictive tasks like predicting customer churn or pricing financial assets (stocks, bonds, options), proving its versatility across different business applications. Also, this platform is popular for anomaly detection such as transaction fraud with efficiency and cost being the main drivers of adoption. The landscape is not monolithic since some studies propose alternative platforms indicating that Apache Spark may not be the best option for every scenario. Based on our findings, we suggest future research directions that would push the boundaries of the field: using social media data sources other than Twitter for more general market sentiment, applying more varied algorithms to improve prediction accuracy, and extending Spark's application into new areas like currency exchange rate forecasting, credit risk analysis, Anti-Money Laundering (AML) detection as well as Data Lakehouse architecture implementation. These recommendations are meant to steer researchers toward uncharted territories where significant value could be unlocked for business and finance with the help of Apache Spark.
Co-Authors Anas Taufan Bambang Purnomosidi Dwi Putranto Domy Kristomo Fikky Ardianto