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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Innovation in Mechanical Engineering and Advanced Materials Cyber Security and Network Management
Abdul Hamid
Universiti Tun Hussein Onn Malaysia
Automotive Engineering Computer Science & IT Energy Engineering Materials Science & Nanotechnology Mechanical Engineering

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Development of Teak Wood Powder–Epoxy Composite for Motorcycle CVT Weight Rollers Application R. Dwi Pudji Susilo; Muhamad Fitri; Muhammad Sulthan Yafiq; Abdul Hamid; Dedik Romahadi
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 8 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v8i1.33422

Abstract

The development of sustainable materials for automotive components has become increasingly important due to environmental concerns associated with conventional synthetic materials. This study investigates the feasibility of teak wood powder (Tectona grandis L.F.) reinforced epoxy composites as an eco-friendly alternative to polytetrafluoroethylene (PTFE) for Continuously Variable Transmission (CVT) weight rollers. The composite was fabricated using a hot-press method with varying composition ratios (60:40, 70:30, and 80:20) and processing temperatures (160 °C, 170 °C, and 180 °C) under a constant pressure of 20 bar. Mechanical performance was evaluated through tensile testing in accordance with ASTM D3039. The results demonstrate that both composition and processing temperature significantly influence tensile strength. The optimal condition—60% teak wood powder and 40% epoxy resin processed at 180 °C—yielded the highest average tensile strength of approximately 25 MPa, surpassing the typical value of conventional PTFE-based rollers (~23 MPa). The improvement is attributed to enhanced matrix–filler bonding and better resin flow at elevated temperatures, resulting in more effective load transfer and reduced void formation. Conversely, higher filler content led to reduced performance due to insufficient matrix continuity and increased interfacial defects. This study provides a significant contribution by demonstrating that teak wood waste can be effectively utilized as a reinforcement material in structural automotive applications. The findings highlight a viable pathway toward cost-effective, sustainable composite design while maintaining competitive mechanical performance. Further investigation on tribological behavior and long-term durability is recommended to support real-world implementation.
Secure Cloud Native Microservices Architecture with Zero Trust Network Access Controls and Multi Layered Encryption for Resilient Distributed Systems Lukman Medriavin Silalahi; Imelda Uli Vistalina Simanjuntak; Hayadi Hamuda; Irfan Kampono; Agus Dendi Rochendi; Abdul Hamid
Cyber Security and Network Management Vol. 1 No. 1 (2026): February: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i1.14

Abstract

The increasing adoption of cloud native microservices has brought about significant improvements in scalability, flexibility, and resilience. However, these advancements also introduce substantial security challenges, particularly in distributed environments where traditional perimeter-based security models prove inadequate. This paper proposes a secure architecture for cloud native microservices that integrates Zero trust Network Access (ZTNA) and multi layered encryption techniques to address these security concerns. The architecture operates on the principle of "never trust, always verify," ensuring that access to resources is strictly controlled and continuously monitored. By incorporating multi layered encryption methods such as RSA and AES, the architecture ensures data protection both in transit and at rest, significantly reducing the risk of data breaches and unauthorized access. Through experimental evaluations, the proposed architecture demonstrated its effectiveness in preventing lateral movement, mitigating data leakage, and resisting common attack vectors such as man-in-the-middle (MITM) attacks and privilege escalation. Additionally, the performance of the system remained optimal, with minimal overhead despite the additional security layers. The architecture's scalability and robust security mechanisms make it a viable solution for real-world microservices environments, where both security and performance are crucial. This paper discusses the potential impact of this secure architecture on the broader field of distributed system security and offers recommendations for future work, including the integration of advanced machine learning techniques for real-time threat detection and automated responses, as well as the adaptation of the architecture for emerging technologies like edge computing and 6G networks.
Effect of Water Hyacinth Fiber Length and Content on the Torsional Strength of Epoxy Resin Composites Putratama Aziz Pramana; Muhamad Fitri; Abdul Hamid; Dedik Romahadi
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol. 6 No. 3 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v6i3.19701

Abstract

This study investigates the influence of water hyacinth fiber length and content on the torsional strength of epoxy resin composites. Utilizing an experimental design, specimens were prepared with varying fiber lengths (10 mm, 20 mm, 25 mm, and 135 mm) and content percentages (4%, 7%, and 10%) and subjected to torsional testing according to ASTM E-143 standards. The primary objective was to determine the optimal fiber configurations that enhance the composite's mechanical properties, particularly its resistance to torsional stress. Results indicated that shorter fiber lengths consistently yielded higher torsional strength, with the 20 mm fibers at a 7% content displaying the highest torque resistance, achieving a maximum of 1.418 Nm and a shear stress of 29.348 MPa. In contrast, longer fibers generally showed diminished performance, likely due to poorer resin penetration and fiber-matrix bonding. Regression analysis was employed to develop predictive models for the torsional behavior based on fiber dimensions and compositions, achieving high accuracy with coefficients of determination (R²) ranging from 0.95 to 1.00, suggesting excellent model fits. These findings underscore the potential of using water hyacinth fibers as effective reinforcement in epoxy composites, particularly at optimal lengths and concentrations. The study contributes to the broader utilization of natural fibers in composites, offering a sustainable alternative to synthetic fibers with beneficial mechanical properties and environmental impacts.
Feature selection to improve distributed denial of service detection accuracy using hybrid N-Gram heuristic techniques Andi Maslan; Abdul Hamid; Dedy Fitriawan; Anggia Dasa Putri; Tukino Tukino
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.24913

Abstract

Distributed denial of service (DDoS) attacks servers and computers in various ways, such as flooding traffic. There are three DDoS detection methods, namely anomaly-based, pattern-based and heuristic-based. However, pattern-based methods cannot detect recent attacks, while anomaly-based methods have low accuracy and relatively high false positives. This research proposes increasing accuracy using a heuristic-based DDoS detection method and a new feature. The combination of CSDPayload+N-Gram and CSPayload+N-Gram features is called hybrid N-Gram, which is analysed on four datasets: CIC2017, CIC2019, MIB-2016, and H2NPayload. Next, calculate Chi-square distance (CSD) and cosine similarity (CS) using the N-Gram frequency value results. Subsequently, compute Pearson Chi-square using the N-Gram frequency value results. Compare the CSDPayload+N-Gram and CSPayload+N-Gram, along with the Pearson Chi-square value, to classify it as either DDoS or not. Finally, feature selection based on weight correlation and payload classification employs machine learning algorithms: support vector machine (SVM), K-nearest neighbors (KNN), and neural network (NN). The average accuracy rate for detecting DDoS attacks across four datasets, utilising the CSDPayload+4-Gram and CSPayload+4-Gram features with the SVM algorithm, is 99.71%, which surpasses the accuracy achieved by using KNN (96.22%) and NNs (99.50%) imitation. Thus, the best algorithm for detecting DDoS is SVM with hybrid 4-Gram.
Co-Authors Agus Dendi Rochendi Andi Maslan Anggia Dasa Putri Dedik Romahadi Dedy Fitriawan Hamuda, Hayadi Imelda Uli Vistalina Simanjuntak Irfan Kampono Lukman Medriavin Silalahi Muhamad Fitri Muhammad Sulthan Yafiq Putratama Aziz Pramana R. Dwi Pudji Susilo Tukino Tukino