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All Journal Switch: Jurnal Sains dan Teknologi Informasi Green Engineering: Journal of Engineering and Applied Science
Maulina Mukaromah
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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Rancangan Sistem Informasi Reservasi Dan Pemasaran Barbershop Berbasis Web Untuk Meningkatkan Efisiensi Operasional Ahmad Haidar; Maulina Mukaromah; Iqbal Ahmad Bukhari; Edy Susena
Switch : Jurnal Sains dan Teknologi Informasi Vol. 3 No. 4 (2025): Juli: Switch : Jurnal Sains dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/switch.v3i4.536

Abstract

Digitalization demands the MSME sector, including barbershops, to adopt technology in reservation management and marketing. Many barbershops still use manual systems, causing problems such as irregular queues, service delays, and suboptimal promotions. This study formulates how to design an information system that can integrate reservation and marketing functions into a single web-based platform. The objective is to produce an efficient, secure, and user-friendly information system design for both barbershop managers and customers. The method used is the Design Science Research approach with a waterfall system development model, involving needs analysis, system design, database design, and user interface design. The design results were validated by experts and users with a satisfaction rate of over 85%, indicating that the designed system aligns with the operational needs of barbershops. The conclusion is that the developed information system can enhance operational efficiency and customer experience, and is suitable for implementation in the barbershop service sector of SMEs.
Machine Learning Implementation for E-commerce Delivery Delay Prediction Using XGBoost Algorithm Stevanus Putra Lesmana; Dina Hermawati; Maulina Mukaromah; Iqbal Ahmad Bukhari; Norma Puspitasari
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 3 (2025): July : Green Engineering: International Journal of Engineering and Applied Scie
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i3.219

Abstract

Delivery delays pose a major challenge in the e-commerce industry, often leading to decreased customer satisfaction and negatively impacting business operations. In this study, the XGBoost (Extreme Gradient Boosting) algorithm is applied to predict delivery delays based on a dataset containing 96,476 records. These records include various features relevant to the delivery process, such as shipping distance, carrier performance, and order characteristics. The model achieves a high overall accuracy of 93.24%, indicating strong general performance. In particular, XGBoost demonstrates excellent results in predicting on-time deliveries, achieving a precision of 93% and a recall of 100%. However, the model struggles to correctly identify delayed deliveries. The recall for delayed deliveries is 0%, and the F1-score is extremely low at 0.01. This significant discrepancy reveals a critical limitation in the model's performance — the inability to detect minority class cases (delayed deliveries) due to class imbalance within the dataset. The results highlight the importance of addressing data imbalance in predictive modeling for delivery outcomes. When the dataset is dominated by on-time delivery records, the model tends to be biased toward that class, failing to learn the patterns associated with delays. To improve performance, the study recommends integrating class balancing techniques such as SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic samples of the minority class. Additionally, the use of alternative evaluation metrics beyond accuracy — such as precision, recall, and F1-score for each class — is suggested to provide a more comprehensive understanding of model effectiveness. Overall, the study provides valuable insights into the complexities of predicting delivery delays and outlines practical strategies for enhancing future models in e-commerce logistics analytics.
Co-Authors Ahmad Haidar Dina Hermawati Edy Susena Iqbal Ahmad Bukhari Norma Puspitasari Stevanus Putra Lesmana