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Menampilkan 1–8 dari 8 artikel
Explainable Artificial Intelligence Framework for Interpretable Fault Diagnosis and Remaining Useful Life Prediction in Smart Industrial Rotating Machinery
Suyahman Suyahman
; Deny Prasetyo
; Ahmad Budi Trisnawan
; Ardy Wicaksono
; Muhamad Furqon
International Journal of Mechanical, Industrial and Control Systems Engineering
Vol 1
, No 1
(2026)
Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Var...
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Adaptive Cyber Secure Software Engineering Practices for Big Data Platforms With Dynamic Access Control and Differential Privacy Mechanisms
Ahmad Budi Trisnawan
; Priyo Wibowo
Big Data Analytics and Data Science
Vol 1
, No 1
(2026)
Big data platforms face significant challenges related to cybersecurity and privacy due to the vast volume, variety, and velocity of data they manage. Traditional static security measures often fail to address the dynamic and complex nature of big data environments. This research proposes an adaptive cybersecurity framework that integrates dynamic access control and differential privacy mechanisms to enhance both the security and privacy of big data platforms. The dynamic access control mechanis...
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Analyzing the Role of Enterprise Information Systems in Driving Organizational Innovation: A Multi Method Study
Ahmad Budi Trisnawan
; Muhammad Sholikhan
; Iwan Koerniawan
Information System Analysis, Design and Development
Vol 1
, No 1
(2026)
This study investigates the role of Enterprise Information Systems (EIS) in driving innovation within organizations. The research employs a mixed-method approach, combining survey-based structural analysis and in-depth organizational case studies to explore how different EIS capabilities influence organizational innovation. The study focuses on four key EIS capabilities: functional capabilities such as workforce management and customer value creation; technological capabilities including ERP sys...
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Algorithmic Simulation for Optimization in Combinatorial Mathematics Using Heuristic Techniques
Ahmad Budi Trisnawan
; Syed Asif Ali
; Erlita Sulistiati
International Journal of Applied Mathematics and Computing
Vol 2
, No 3
(2025)
This research explores the effectiveness of heuristic techniques for solving combinatorial optimization problems, with a particular focus on the Traveling Salesman Problem (TSP). Combinatorial optimization is a critical area of study, especially in fields like computer science, engineering, and economics, where finding optimal solutions from a finite set of possibilities is crucial. However, the NP-hard nature of many combinatorial problems, such as the TSP, makes traditional exact methods like...
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Adaptive Edge-AI Framework for Real-Time Cyber-Physical Systems in Smart Cities with Resource-Constrained IoT Devices
Benny Martha Dinata
; Ahmad Budi Trisnawan
; Eram Abbasi
Journal of Information Technology and Computer Science
Vol 1
, No 2
(2025)
This research focuses on the development and evaluation of an Adaptive Edge-AI framework designed to optimize real-time data processing and decision-making in resource-constrained environments, specifically within smart city infrastructures. The primary problem addressed is the challenge of minimizing latency, reducing energy consumption, and ensuring the reliability of Cyber-Physical Systems (CPS) when using Internet of Things (IoT) devices. The objective of the study is to assess the effective...
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Sustainable Precision Agriculture Irrigation System Using Edge Computing and Renewable Energy Integration for Water Conservation and Climate Adaptation
Agus Wantoro
; Ferly Ardhy
; Fahlul Rizki
; Ahmad Budi Trisnawan
; Yulaikha Mar’atullatifah
; Rachmat Setiabudi
International Journal of Engineering and Applied Science
Vol 2
, No 2
(2025)
The integration of solar powered IoT irrigation systems in precision agriculture offers a sustainable solution to address water scarcity and enhance crop productivity. By leveraging real time data from soil sensors, weather APIs, and machine learning algorithms, these systems optimize irrigation schedules and improve water use efficiency. This research explores the potential of integrating renewable energy sources, such as solar power, with edge computing in smart irrigation systems to promote s...
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Digital Twin Driven Real Time Performance Optimization of Smart Factory Production Systems Using Edge Computing and Industrial Internet of Things Architecture
Suyahman Suyahman
; Dwi Utari Iswavigra
; Helmi Wibowo
; Ahmad Budi Trisnawan
; Ardy Wicaksono
; Dwi Atmodjo WP
International Journal of Industrial Innovation and Mechanical Engineering
Vol 1
, No 2
(2024)
Background: The rapid advancement of Industry 4.0 has accelerated the integration of digital technologies such as the Industrial Internet of Things (IIoT), edge computing, and Digital Twin systems in smart manufacturing environments. However, many existing implementations remain fragmented and heavily dependent on centralized cloud infrastructures, resulting in latency constraints, limited scalability, and suboptimal real-time decision making. Objective: This study aims to develop and validate a...
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Design and Evaluation of Federated Deep Learning Framework for Privacy Preserving Healthcare Data Analytics Across Heterogeneous IoT Networks
Simon Simarmata
; Panser karo-karo
; Rino Ferdian Surakusumah
; Ahmad Budi Trisnawan
; Suyahman Suyahman
; Bentar Priyopradono
International Journal of Computer Technology and Science
Vol 1
, No 2
(2024)
The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly in medical data prediction and classification. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework for multi-modal healthcare data analysis, integrating medical imaging, structured electronic health records (EHRs), and IoT-generated time-series physiological signals. The proposed architecture combines spatial feature extraction thr...
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