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All Journal INOVTEK Polbeng - Seri Informatika
I Gede Aris Gunadi
Pendidikan Ganesha University
Computer Science & IT

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Multimodal ECG-PPG Clinical Fusion for Myocardial Infarction Classification Using Ensemble Learning I Gede Angga Candrawibawa; I Gede Aris Gunadi; Luh Joni Erawati Dewi
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/s7j9qt66

Abstract

This study presents a comparative analysis of multimodal ECG, PPG, and clinical feature fusion for myocardial infarction (MI) classification using four ensemble learning algorithms: Random Forest, XGBoost, LightGBM, and CatBoost. The experiments were conducted in two classification scenarios: binary classification for normal vs. MI and multi-class classification for normal, STEMI, NSTEMI, and old MI. Five feature scenarios were evaluated, including clinical-only, ECG-only, PPG-only, ECG + PPG, and ECG + PPG + clinical. The results show that ECG features were the most dominant modality for MI classification. In binary classification, XGBoost with ECG-only features achieved perfect performance with accuracy, macro F1-score, macro recall, and MCC of 1.0000. For multi-class classification, the best result was obtained by CatBoost using ECG + PPG + clinical features, achieving an accuracy of 0.9000, a macro F1-score of 0.5394, and an MCC of 0.6912. These findings indicate that multimodal fusion is more beneficial for MI subtype classification, while ECG-only features are highly effective for binary MI detection
Knowledge Management System Development Using Tiwana Roadmap and Agile Scrum at HP Service Center Denpasar Ni Wayan Astuti; I Gede Aris Gunadi; Luh Joni Erawati Dewi
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/9z4bmv04

Abstract

This study aims to design and develop a Knowledge Management System (KMS) at HP Service Center Denpasar by integrating the Tiwana Roadmap framework and the Agile Scrum development method. HP Service Center Denpasar faces challenges in knowledge retention and dissemination, where valuable knowledge assets, both tacit and explicit knowledge, are still stored individually. Therefore, this study focuses on documenting all knowledge possessed by various operational units, ranging from Senior PC/Laptop Technicians and Printer Technicians to logistics staff, in order to prevent knowledge loss and accelerate the knowledge transfer process. In the development process, the Tiwana Roadmap was selected as the main framework because of its ability to provide structured, systematic, and gradual guidance for identifying, managing, auditing, and optimally utilizing organizational knowledge. Meanwhile, to execute the blueprint into software form, the Agile Scrum method was implemented to support a system development process that is flexible, adaptive, and responsive to changing requirements and dynamic field conditions through iterative sprint cycles. After the system has been developed, the testing phase will be conducted using two comparative approaches: the User Experience Questionnaire (UEQ) to quantitatively measure user experience aspects, and Cognitive Walkthrough / Cognitive Task Analysis (CTA) to evaluate cognitive efficiency and system usability when used by staff. The expected final result of this study is a KMS platform that is not only technically valid but also interactive and capable of improving operational efficiency at HP Service Center Denpasar.
Co-Authors I Gede Angga Candrawibawa Luh Joni Erawati Dewi Ni Wayan Astuti