Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
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This Author published in this journals
All Journal Startupreneur Business Digital (SABDA Journal) AI, Innovation, and Resilience for the Environment Journal
Sipho Dlamini
Mfinitee Incorporation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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The Role of Application Programming Interface in Transforming Restaurant Delivery Operations Cicilia Sriliasta Bangun; Solahudin Solahudin; Asep Sutarman; Sipho Dlamini
Startupreneur Business Digital (SABDA Journal) Vol. 5 No. 1 (2026): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sabda.v5i1.1066

Abstract

The rapid growth of online food delivery services has transformed the restaurant industry, requiring efficient and scalable solutions to meet increasing customer demands. However, traditional restaurant management systems often struggle with integration challenges, operational inefficiencies, and customer dissatisfaction due to delays and miscommunication. This study aims to analyze the role of Application Programming Interfaces (API) in optimizing restaurant delivery operations by enhancing order management, inventory tracking, and real-time customer interactions. Using a mixed-method approach, we conducted case studies on multiple restaurant platforms and surveyed industry professionals to assess the effectiveness of API integration. The findings reveal that API-driven systems significantly improve delivery accuracy, reduce processing time, and enhance customer experience by automating order workflows and enabling seamless third-party service connections. The results indicate that restaurants leveraging API can achieve a higher level of efficiency and scalability, minimizing operational bottlenecks while maintaining service quality. This research concludes that API play a crucial role in transforming restaurant delivery operations, providing a competitive advantage in the evolving digital marketplace. Future studies should explore the impact of emerging technologies such as AIpowered API and blockchain integration for further optimization.
Feature Engineering for Improved Digital Business Model Accuracy Qurotul Aini; Sipho Dlamini; Shofiyul Millah; Sausan Raihana Putri Junaedi
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 1 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i1.120

Abstract

The rapid growth of digital businesses has intensified the demand for accurate and robust business models capable of predicting market behavior and optimizing operational efficiency. The Background of this study highlights the challenges faced by digital enterprises in handling high-dimensional, heterogeneous data, which often compromise model accuracy and predictive reliability. The Object of this research is to examine how feature engineering techniques can enhance the performance and precision of digital business models, providing more reliable insights for decision-making. Employing a mixed-methods approach as the Method, the study integrates quantitative evaluation using machine learning algorithms on diverse digital business datasets alongside qualitative analysis of feature selection, transformation, and extraction processes. The Result demonstrates that strategic feature engineering significantly improves model accuracy, reduces overfitting, and enhances interpretability, with models incorporating engineered features outperforming baseline models by measurable margins in predictive metrics. Furthermore, specific feature selection and transformation methods show a notable impact on optimizing model performance across various digital business scenarios. The Conclusion emphasizes that feature engineering is a critical component in developing high-accuracy digital business models, serving as a bridge between raw data and actionable insights. By systematically applying feature engineering techniques, digital enterprises can enhance forecasting reliability, operational strategy design, and overall business agility, contributing to sustainable growth in competitive digital markets. This study underscores the importance of integrating advanced data preprocessing strategies as part of the digital business modeling pipeline to maximize predictive efficiency and strategic decision support.
Co-Authors Asep Sutarman Bangun, Cicilia Sriliasta Qurotul Aini Sausan Raihana Putri Junaedi Shofiyul Millah Solahudin Solahudin