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All Journal eProceedings of Applied Science Electronic Integrated Computer Algorithm Journal
Hizbullah, Fauzi
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences

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Journal : Electronic Integrated Computer Algorithm Journal

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Agile-Based Application Architecture Design for Billet Management in Industrial Manufacturing Ramadhani, Rafian; Hizbullah, Fauzi; Auliya Rahman, Ilham; Ahyar Harizillah, M.; Noorachmad Muttaqin, Alif; Saidi Lubis, Fahdi
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.96

Abstract

This study presents the planning and iterative development of an enterprise application architecture for the Billet Stacker Rail system in an aluminum manufacturing environment. The system is designed to enhance the management of billet logistics, including receiving, inspection, stacking, and transfer processes. Using the Agile methodology, particularly the Scrum framework, the development team collaborated closely with operational stakeholders to capture requirements and validate functionality through a series of Sprints. The process included modeling workflows, designing class and entity diagrams, and creating interactive user interface mockups. The system architecture was developed incrementally to support modularity, traceability, and real-time data recording. Each component from billet tracking to user management was prototyped and refined based on continuous feedback. The Agile approach facilitated rapid adjustments to changing requirements, reduced development risk, and supported a user-centered design process. The result is a robust and scalable application blueprint that aligns with the industrial environment’s needs for efficiency, reliability, and transparency in billet management operations.
Heart Attack Risk Prediction Using Machine Learning: A Comparative Study of Decision Tree and K-Nearest Neighbors Hizbullah, Fauzi; Noorachmad Muttaqin, Alif; Andiharsa Sih Setiarto, Rahardian; Aulia Hakim, Rizki; Abdulmana, Sahidan
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.98

Abstract

Heart disease, particularly heart attacks, is a leading cause of death worldwide, highlighting the importance of early detection and risk prediction. This study develops and evaluates machine learning models to predict heart attack risk using seven health-related attributes: age, marital status, gender, body weight category, cholesterol level, participation in stress management training, and stress level. The dataset, processed with the Orange Data Mining platform, was divided into training (66%) and testing (34%) sets. Two supervised algorithms, Decision Tree and K-Nearest Neighbors (K-NN), were implemented without extensive hyperparameter tuning. Model performance was evaluated using accuracy, precision, recall, and F1 score. The Decision Tree achieved the best results with 84.78% accuracy, 88.52% precision, 79.41% recall, and 83.72% F1 score, indicating its effectiveness in identifying at-risk individuals. Key predictors included age, stress level, and cholesterol, aligning with established medical findings. While the results are promising, limitations include a small dataset and limited algorithm scope. Future research should expand the dataset, include additional clinical features, and explore advanced algorithms to improve accuracy and reduce false negatives, enhancing applicability in preventive healthcare.
Co-Authors Abdulmana, Sahidan Ahyar Harizillah, M. Andiharsa Sih Setiarto, Rahardian Aulia Hakim, Rizki Auliya Rahman, Ilham Noorachmad Muttaqin, Alif Purna Kurniawan, Ady Ramadhani, Rafian Saidi Lubis, Fahdi Zani, Tafta