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Journal : Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control

Entropy-Based Feature Extraction and K-Nearest Neighbors for Bearing Fault Detection Hakim, Sinta Uri El; Bahiuddin, Irfan; Arifianto, Rokhmat; Ritonga, Syahirul Alim
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 1, February 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i1.1814

Abstract

Bearing failures in rotating machines can lead to significant operational challenges, causing up to 45-55% of engine failures and severely impacting performance and productivity. Timely detection of bearing anomalies is crucial to prevent machine failures and associated downtime. Therefore, an approach for early bearing failure detection using entropy-based machine learning is proposed and evaluated while combined with a classifier based on K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). Entropy-based feature extraction should be able to effectively capture the intricate patterns and variations present in the vibration signals, providing a comprehensive representation of the underlying dynamics. The results of the classification carried out by KNN-Entropy have an accuracy value of 98%, while the SVM-Entropy model has an accuracy of 96%. Hence, the Entropy-based feature extraction giving the best accuracy when it is coupled with KNN.
Bamboo Diameter Detection System Based on Image Processing as a Pre-Assessment for an Automated Bamboo Splitting Technology Hakim, Sinta Uri El; Arifianto, Rokhmat; Sugiyanto; Pratiwi, Ilham Ayu Putri; Bahari, Galuh; Krisnaputra, Radhian
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2170

Abstract

Bamboo is recognized for its eco-friendly attributes and rapid growth, serves as a promising sustainable alternative to wood. However, the high production cost of laminated bamboo remains a major challenge due to labor-intensive processes, particularly manual splitting, which affects efficiency and labor costs. To overcome this issue, this study presents an automated bamboo diameter measurement system that leverages Canny Edge Detection and Hough Transform to ensure precise and uniform slat dimensions. A dataset of 100 bamboo images with diameters ranging from 11 - 13 cm was utilized for training and testing. The system achieved a high accuracy, with a coefficient of determination (R²) of 0.973, demonstrating strong predictive reliability. Furthermore, Bayesian Optimization was applied to fine-tune parameters, resulting in an optimized configuration for both Canny Edge Detection and Hough Transform. The proposed system reduces dependence on manual labor, thereby lowering production costs and improving overall manufacturing efficiency. Automation in the bamboo splitting process ensures consistent and precise slat dimensions, supporting scalability and enhancing the economic feasibility of laminated bamboo production. The findings of this study provide a practical and sustainable solution to optimize production, making laminated bamboo a more viable and competitive material in the industry.