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All Journal IPSSJ Green Engineering: Journal of Engineering and Applied Science
Stevanus Putra Lesmana
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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IMPLEMENTASI SISTEM INFORMASI MANAJEMEN STOK BERBASIS WEB UNTUK MENINGKATKAN EFISIENSI PENGELOLAAN INVENTORY SPAREPART KOMPUTER PADA RISKY COMP SOLO Stevanus Putra Lesmana; Dina Hermawati; Edy Susena
Integrative Perspectives of Social and Science Journal Vol. 2 No. 03 Juli (2025): Integrative Perspectives of Social and Science Journal
Publisher : PT Wahana Global Education

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Risky Comp Solo merupakan toko komputer yang mengalami kendala dalam pengelolaan stok sparepart komputer karena menggunakan sistem manual dan keterbatasan ruang yang mengharuskan penyimpanan barang di rumah. Masalah ini sering menyebabkan ketidakakuratan data stok dan kekecewaan pelanggan. Tujuan penelitian ini adalah mengimplementasikan sistem informasi manajemen stok berbasis web untuk meningkatkan efisiensi pengelolaan inventory. Metode pengembangan yang digunakan adalah waterfall dengan tahapan analisis kebutuhan melalui wawancara, desain sistem, implementasi menggunakan PHP dan MySQL, serta pengujian sistem. Hasil penelitian menunjukkan bahwa sistem berhasil memudahkan monitoring data barang masuk dan keluar, mengurangi kesalahan pencatatan, dan meningkatkan efisiensi operasional. Setelah implementasi selama satu minggu, pemilik toko memberikan feedback positif karena sistem memudahkan pengecekan stok sebelum mengambil barang di rumah. Sistem ini terbukti efektif dalam mengatasi masalah pengelolaan stok manual.
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 Dina Hermawati Edy Susena Iqbal Ahmad Bukhari Maulina Mukaromah Norma Puspitasari