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All Journal Sistemasi: Jurnal Sistem Informasi International Journal of Digital Entrepreneurship and Business (IDEB)
Elvi Fetrina
UIN Syarif Hidayatullah
Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Social Sciences Other

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Analysis of Higher Education Alumni Careers using LinkedIn Web Scraping and K-Means Clustering Qurrotul Aini (SCOPUS ID: 54974128700); Eri Rustamaji; Denina Nastiti Putri Amani; Elvi Fetrina
SISTEMASI Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6437

Abstract

The use of alumni data to support curriculum evaluation continues to face challenges due to the limitations of conventional data collection methods, such as manual surveys, which often result in low response rates. Meanwhile, LinkedIn provides relatively comprehensive and up-to-date alumni career information; however, its potential for supporting tracer studies remains underutilized. This study aims to analyze the career patterns of higher education alumni using LinkedIn data collected through web scraping and analyzed with the K-Means clustering algorithm within the Knowledge Discovery in Databases (KDD) framework. The proposed approach applies the KDD process to generate a data-driven mapping of alumni career patterns as a complement to conventional tracer studies. The dataset consisted of 133 alumni profiles, which were processed through the stages of data selection, preprocessing, transformation, clustering, and evaluation. The results indicate that the majority of alumni are employed in the technology sector and occupy mid-level or specialist positions. The K-Means algorithm identified three distinct career clusters, representing career tendencies in business process and operations, systems and technology development, and data utilization and software quality assurance. These findings reveal the distribution of alumni competencies across business, data, and technology domains. However, the clustering quality was relatively low, as indicated by a Silhouette Score of 0.0321 and a Davies-Bouldin Index of 3.0487, suggesting limited separation among the identified clusters. Therefore, the clustering results should be interpreted as an initial mapping of alumni career patterns rather than definitive classifications. Overall, this study demonstrates the potential of professional social media data as a valuable resource for supporting data-driven alumni career analysis and complementing traditional tracer study practices.
Digital Business Tranformation Through XGBoost-Based Sales Forecasting for Fast Food Franchise Inventory Meinarini Utami; Mim Hanifah Permana; Elvi Fetrina
International Journal of Digital Entrepreneurship and Business Vol 7 No 1 (2026): International Journal of Digital Entrepreneurship and Business (IDEB)
Publisher : Universitas Jakarta Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52238/ideb.v7i1.416

Abstract

The rapid growth of the digital economy has pushed fast food franchise businesses to adopt data-driven decision-making to remain competitive, particularly in production and inventory planning. The Company, an Indonesian fast-food franchise operating more than 300 outlets, currently lacks an accurate sales forecasting system, resulting in frequent overstock (approximately 30 products per day) and understock incidents that trigger negative customer reviews and threaten business sustainability. This study develops a digital sales forecasting model to determine daily sales estimates, safety stock levels, and reorder points for two of the Company's outlets, referred to as Outlet A and Outlet B to preserve business confidentiality. Daily sales data from January 2023 to June 2024, combined with national holiday data, were processed using the XGBoost algorithm in Python and evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE); safety stock and reorder points were then calculated in Microsoft Excel. The model produced an average daily sales prediction of 435 products at Outlet A (RMSE 61.89, MAPE 10.2%) and 360 products at Outlet B (RMSE 56.2, MAPE 14.1%), both categorized as good forecasts. The resulting safety stock and reorder point values were 98 and 968 for Outlet A, and 120 and 840 for Outlet B. These findings demonstrate that digital forecasting technology can support more efficient inventory control and provide franchise businesses with a practical, replicable basis for reducing overstock and understock problems through the digital transformation of their operations
Co-Authors Denina Nastiti Putri Amani Eri Rustamaji Meinarini Utami Mim Hanifah Permana Qurrotul Aini (SCOPUS ID: 54974128700)