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All Journal INOVTEK Polbeng - Seri Informatika
Hendy Kurniawan
Universitas Dian Nuswantoro
Computer Science & IT

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The Implementation of AWS Cloud Technology to Enhance the Performance and Security of the Pharmacy Cashier Management System Hendy Kurniawan; L. Budi Handoko; Valentino Aldo
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/x0rctv54

Abstract

 This study examines the implementation of Amazon Web Services (AWS) in the MEKATEK pharmacy cashier management system to address the limitations of traditional systems, such as slow transaction processing, data loss risks, and challenges in handling transaction surges. The prototyping method was employed, involving user requirements analysis through interviews and observations, followed by iterative development of core features like inventory management, transactions, reporting, and data backups. Black box testing demonstrated a 100% success rate for core functionalities. Performance analysis recorded stable CPU utilisation below 5% under normal workloads and the ability to handle throughput up to 2532 packets/minute. System optimisation reduced AWS operational costs to IDR 150,000–160,000 per month. AWS implementation improved operational efficiency, strengthened data security through encryption and role-based access control, and minimised human errors. Initial user feedback indicated faster workflows, although adjustments are needed for users with limited technical backgrounds. This study recommends further development, including AI-based analytics and digital payment integration, to enhance MEKATEK’s functionality and competitiveness in the future.
Comparison of Effectiveness of Machine Learning Methods in Predicting Chemical Compound Toxicity Enhance Pharmaceutical Product Safety Dufan Yuwana; Pulung Andono; Hendy Kurniawan
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/emkzcz13

Abstract

This study compares the effectiveness of machine learning methods in predicting the toxicity of chemical compounds using a dataset containing 5,000 samples with 14 key features. The dataset underwent preprocessing, including normalization, missing data handling, and oversampling to address data imbalance. The models used include Decision Tree, Random Forest, Extra Trees, and Gradient Boosting, validated using k-fold cross-validation. Evaluation based on accuracy, precision, recall, and F1-score showed that Gradient Boosting achieved the best performance with 92.3% accuracy, though it still faces challenges such as overfitting and interpretability limitations. Compared to in vitro and in vivo methods, machine learning is more efficient but still requires further experimental validation. This study recommends optimizing models through ensemble learning and explainable AI to improve prediction reliability.
Co-Authors Dufan Yuwana L. Budi Handoko Pulung Andono Valentino Aldo