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All Journal International Journal of Electronics and Communications Systems Journal of Security, Computer, Information, Embedded, Network and Intelligence System
Suherman, Muhammad Ilham
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Sistem E-Commerce untuk Meningkatkan Penjualan Alat Elektronik Menggunakan Metode Extreme Programming Suherman, Muhammad Ilham; Maryani, Desi; Amir, Ilmawati; Angraeni, Reny
Journal of Security, Computer, Information, Embedded, Network, and Intelligence System Vol. 2, No. 2 (Desember 2024)
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/scientist.v2i2.20245

Abstract

This study aims to develop and implement an efficient and effective e-commerce-based electronic goods sales system using the Extreme Programming (XP) system development method. The main focus of this research is to enhance the promotion and sales processes in small and medium-sized stores to be competitive in the digital era. XP was chosen due to its iterative and incremental approach and customer involvement at every stage of software development to ensure the final product meets user needs and expectations. The results indicate that the developed system can expand customer reach and improve the operational efficiency of these stores.
Optimizing Student Graduation Prediction Using XGBoost with SMOTE-ENN, Hyperparameter Tuning, and Threshold Adjustment Fadillah, Nur; Safira, Wahyuni Edsa; Suherman, Muhammad Ilham; Surianto, Dewi Fatmarani; Zain, Satria Gunawan
International Journal of Electronics and Communications Systems Vol. 6 No. 1 (2026): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v6i1.28867

Abstract

Predicting students at risk of delayed graduation is essential for enabling timely academic intervention, yet educational datasets are often characterized by class imbalance that limits predictive performance. This study proposes and evaluates an optimized XGBoost framework that integrates SMOTE-ENN, hyperparameter tuning, and decision threshold adjustment for student graduation prediction. A quantitative machine learning approach was conducted using academic records from 315 alumni across multiple Indonesian universities. Six classification algorithms were systematically compared to identify the most suitable baseline model before optimization. Model performance was assessed using multiple classification metrics to ensure comprehensive evaluation. The findings demonstrate that XGBoost consistently outperformed the competing algorithms and achieved its strongest predictive performance after integrating all three optimization strategies. Compared with applying each optimization technique individually, the combined framework produced more balanced classification results, improved minority-class recognition, and reduced prediction bias caused by imbalanced data. Feature analysis further revealed that academic variables, particularly cumulative grade point average, accumulated credits, and course repetition history, were the strongest predictors of timely graduation, whereas social and non-academic variables contributed comparatively less. These findings provide an effective and replicable machine learning framework for early identification of students at risk of delayed graduation and offer practical support for data-driven academic intervention and decision-making in higher education
Co-Authors Amir, Ilmawati Angraeni, Reny Dewi Fatmarani Surianto Maryani, Desi Nur Fadillah, Nur Safira, Wahyuni Edsa