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Mapping Research on the Use of Algorithms in Commerce: A Bibliometric Analysis Based on Scopus Almagribi, Ahmad Bilal; Ardianto, Fikky; Taufan, Anas; Kristomo, Domy
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

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.

How is Software Engineering Linked to Business? A Scopus-Based Bibliometric and Visualization Almagribi, Ahmad Bilal; Ardianto, Fikky; Taufan, Anas; Kristomo, Domy
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4590

Software engineering has significantly impacted various fields, including business, yet there had been no comprehensive bibliometric analysis of its interaction with business until this study. This research filled the gap by examining key contributors, affiliations, countries, and leading journals using a bibliometric analysis of 100 Scopus-indexed documents. The study revealed a peak in publications in 2008 with 11 documents, highlighting Gruhn, V. from Universität Leipzig, Germany, as the most prolific author. Both Germany and the United States were prominent, contributing 21 documents each. "Lecture Notes in Computer Science," along with its subseries, emerged as the most cited reference. The keyword analysis identified four main clusters focusing on the integration of IT, practical business applications, the role of education, and project management. The study recommends expanding research into Agile methodologies, UX Design, and technologies like AI, Cloud Computing, and IoT, providing insights into integrating software engineering into business strategies for future challenges.

Current Trends and Future Directions of Big Data in Commerce: A Bibliometric Analysis Based on Scopus Almagribi, Ahmad Bilal; Putranto, Bambang Purnomosidi Dwi
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 2 (2025): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i2.6098

Big data provides significant benefits across various sectors, including commerce. However, there remained a gap in bibliometric studies examining big data within the context of commerce, leaving research development in this field unclear. This study aimed to address this gap by conducting a bibliometric investigation into researchers' contributions to big data in commerce, including their affiliations and countries of origin. Additionally, the study sought to identify the most productive journals and highlight relevant and under-researched topics within this field. A bibliometric analysis approach was employed, analyzing 396 Scopus-indexed documents and using VOSviewer visualization to identify major recurring issues in the literature. The findings revealed that in 2021, the number of publications on big data in commerce peaked at 97 documents. Maalla, A., from Guangzhou College of Technology and Business, China, emerged as the most prolific author, while China led in publication output with 308 documents. The Journal of Physics Conference Series was identified as the most productive source. Computer Science was the most explored discipline, indicating a strong integration of technology with commerce. Keyword analysis divided research focus into four main clusters: analytical technology, platform optimization, supply chain management, and marketing strategy optimization. These findings provide a foundation for future research to explore areas such as Customer Experience Management, Blockchain Technology, Cloud Computing, Predictive Analytics, and Customer Segmentation, thereby enriching the academic literature and offering practical contributions to data-driven commerce.

Mapping Research on the Use of Algorithms in Commerce: A Bibliometric Analysis Based on Scopus Almagribi, Ahmad Bilal; Ardianto, Fikky; Taufan, Anas; Kristomo, Domy
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

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.

Tips for Maintaining Philanthropic Motivation in a Social Institution During a Pandemic Almagribi, Ahmad Bilal; Muslimah, Muslimah; Erawati, Desi
Jurnal BAABU AL-ILMI: Ekonomi dan Perbankan Syariah Vol 7, No 1 (2022): Islamic economics and banking research
Publisher : Universitas Islam Negeri Fatmawati Sukarno Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29300/ba.v7i1.2826

This study aims to explore information about tips by philanthropists in maintaining their philanthropic motivation during the COVID-19 pandemic, conditions that are burdensome for philanthropy, the intensity of philanthropy, and how they invite others to philanthropy. This study uses a qualitative approach with data collection methods in the form of observation, interviews, open questionnaires, and documentation. The analysis was carried out using data triangulation techniques. The results showed that some of their main tips in maintaining their philanthropic motivation were: a strong commitment to sharing sustenance, believing that alms can keep away all problems, philanthropy has become a necessity and joining alms management groups. Conditions or things that have the potential to burden philanthropists: when there is an urgent need, when income is decreasing, the location is remote, and when the institution is no longer trustworthy. The majority of philanthropy levels remain even during the pandemic. As for how to invite others to do philanthropy, such as inviting them directly together, explaining the virtues of alms including through social media, giving examples of philanthropy, distributing brochures and orphanage calendars.

Clustering and Classification of Retail Sales Data: A Big Data and Data Mining Analysis Almagribi, Ahmad Bilal; Redjeki, Sri
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.303

In the evolving retail landscape, data-driven decision-making has become essential for understanding customer behavior and predicting sales trends. This study integrates clustering and classification techniques to analyze retail sales data comprising 1,000 transactions obtained from Kaggle. Using the K-Means algorithm, three optimal customer clusters were identified through the Elbow Method, achieving an average within-centroid distance of 25,272.635 and a Davies–Bouldin Index of 0.443, indicating clear cluster separation. The subsequent classification phase compared the predictive performance of three algorithms—Naïve Bayes, Decision Tree, and Random Forest—on 70:30 training-to-testing data partitions. The Naïve Bayes algorithm attained 94.67% accuracy, while both Decision Tree and Random Forest achieved perfect classification accuracy of 100%. These findings highlight the robustness and adaptability of tree-based models for complex retail datasets, outperforming probabilistic methods in terms of accuracy and generalization. The results suggest that the integration of clustering and classification provides retailers with a powerful analytical framework for identifying high-value customer segments, optimizing marketing strategies, and enhancing inventory management. Despite achieving strong outcomes, the study acknowledges dataset limitations and recommends future research involving larger and more diverse datasets, as well as additional features, to expand model scalability and predictive precision.

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