2026-04-22T10:03:53Z https://tecnoscientifica.com/journal/amms/oai

oai:oai.tecnoscientifica.com:article/667 2025-12-09T10:39:40Z amms:ART

The Effect of Natural Stone Grain Size on Indoor Temperature Reduction Through the Coating Process on Galvanised Roofs Bintarto, Redi Hamidi, Nurkholis Sugiarto Widodo, Teguh Dwi Raharjo, Rudianto Gatnar, Kamil The capacity of a roof to absorb heat played a vital role in maintaining indoor temperature stability. Employing composite coatings made from natural materials presented a promising solution for contemporary roofing systems. This study explored the impact of incorporating natural stone powder combined with epoxy as a coating on galvalume roofing, focusing on its effects on thermal conductivity and indoor temperature reduction based on powder sizes. Temperature data were gathered from a small structure featuring a roof treated with the composite coating, which included andesite natural stones. Thermocouples were placed 20 cm above the roof, on the coated surface, beneath the galvalume layer, and inside the room to monitor heat transfer. The findings revealed that adding natural stone powder to roofing materials effectively lowered thermal conductivity and indoor temperature. The degree of temperature reduction varied depending on the size of the stone powder. Ultimately, the study confirmed that the inherent characteristics of natural stone powder size contributed significantly to enhancing a roof's insulation properties and reducing heat buildup indoors. Tecno Scientifica Publishing 2025-06-12 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://tecnoscientifica.com/journal/amms/article/view/667 10.53623/amms.v1i1.667 Advanced Mechanical and Mechatronic Systems ; Volume 1 - Issue 1 - 2025; 1-10 3122-6752 10.53623/amms.v1i1 eng https://tecnoscientifica.com/journal/amms/article/view/667/324 Copyright (c) 2025 Redi Bintarto, Nurkholis Hamidi, Sugiarto, Teguh Dwi Widodo, Rudianto Raharjo, Kamil Gatnar https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/675 2025-12-09T10:39:40Z amms:ART

Finite Element Analysis and Vibration Signal Processing Techniques To Determine the Frequency Response in Bridge Health Monitoring Study Reza Ramadhan, Yogi Nazri, Muhammad Adi Putra, Seno Rompas, Marina Riviani Adi, Januar Panca Guterres, Natalino Fonseca D. S. Piedade, Salustiano dos Reis Bridge Health Monitoring; Vibration Signal Analysis; Empirical Mode Decomposition (EMD); Wavelet Packets Decomposition (WPD); Finite Element Analysis (FEA); Structural Integrity Ensuring the structural integrity of large-scale bridges is critical worldwide, particularly in Indonesia. The integration of modern digital technologies significantly enhances this effort. A bridge health monitoring system is a vital tool for collecting data, allowing authorities to assess bridge conditions and refine inspection methods. Vibration responses measured using accelerometers, offer valuable insights into a bridge’s structural health. However, the complexity of vibration signals requires advanced signal processing techniques to extract meaningful information. Empirical Mode Decomposition (EMD) and Wavelet Packet Decomposition (WPD) are two promising methods for analyzing such complex signals. Given the large scale of bridge structures and the limited number of sensors typically available, researchers often use Finite Element Analysis (FEA) to simulate and predict vibration responses. For example, a study on the Cisomang Bridge in Bandung, Indonesia, employed FEA to model the bridge’s vibration characteristics. The first natural frequency identified was approximately 4.732 Hz, which served as a reference for further analysis. By integrating FEA models with advanced signal processing methods, the system aims to deliver reliable tools for monitoring and maintaining bridge health, thereby improving infrastructure safety and longevity. Tecno Scientifica Publishing 2025-07-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://tecnoscientifica.com/journal/amms/article/view/675 10.53623/amms.v1i1.675 Advanced Mechanical and Mechatronic Systems ; Volume 1 - Issue 1 - 2025; 29-51 3122-6752 10.53623/amms.v1i1 eng https://tecnoscientifica.com/journal/amms/article/view/675/335 Copyright (c) 2025 Yogi Reza Ramadhan, Muhammad Nazri, Seno Adi Putra, Marina Riviani Rompas, Januar Panca Adi, Natalino Fonseca D. S. Guterres, Salustiano dos Reis Piedade https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/676 2025-12-09T10:39:40Z amms:ART

A Classification and Prediction Method of Electric Battery Condition during Discharging Process Utilizing Adaptive Neuro-Fuzzy Inference System and Support Vector Machine Mukhidin Ramadhan, Yogi Reza Adi, Januar Panca Belo, Joao Bosco Akbar, Nur Arifin This paper presents a data-driven prediction method for electric battery condition monitoring with different loads. The prediction is subject to the usage time of the electric battery during the discharge condition. Two variables are selected as the prediction input i.e. load (Watt) and discharge voltage (Volt) to predict how much time (hour) has left during the discharge period. Adaptive Neuro-Fuzzy Inference System (ANFIS) serves dual purposes of classification and prediction of battery discharge conditions, while Support Vector Machine (SVM) is implemented for classification comparison. While SVM demonstrates superior classification performance with 95% accuracy compared to ANFIS's 88%, ANFIS provides the added value of precise time prediction. The time-series data was collected from the discharge battery experiment for a few hours that uses the electric rechargeable battery from a fully charged capacity to an empty capacity. The experiments were conducted on four different load conditions i.e. 130, 180, 200, and 220 Watts. The prediction result of ANFIS was compared with the result of the Support Vector Machine (SVM). The ANFIS was used to predict how many hours the battery has been used based on two inputs i.e. load (Watt), and discharge volt (Volt). Five different prediction targets i.e. 1, 2, 3, 4, and 5 hours are selected for the ANFIS prediction. This prediction target is according to the deterioration of the discharge voltage during the measurement. The rate of voltage drop varies under different load conditions, with specific discharge profiles observed for each tested load. The result shows that ANFIS can predict the target hour based on the present load and voltage data input during the discharge operation. From the hour prediction, it can estimate the remaining useful life of the battery because the total duration of the battery is known initially. SVM is used as a comparison classifier to the ANFIS. Although SVM demonstrates superior classification accuracy (95% versus ANFIS's 88%), ANFIS's ability to predict time with two-digit precision enables more accurate remaining useful life estimation in EV applications, where even minutes of battery life can be critical for route planning and operational decisions. Tecno Scientifica Publishing 2025-06-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf application/pdf https://tecnoscientifica.com/journal/amms/article/view/676 10.53623/amms.v1i1.676 Advanced Mechanical and Mechatronic Systems ; Volume 1 - Issue 1 - 2025; 11-28 3122-6752 10.53623/amms.v1i1 eng https://tecnoscientifica.com/journal/amms/article/view/676/328 https://tecnoscientifica.com/journal/amms/article/view/676/326 Copyright (c) 2025 Mukhidin, Yogi Reza Ramadhan, Januar Panca Adi, Joao Bosco Belo, Nur Arifin Akbar https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/692 2025-12-09T10:39:40Z amms:REV

Explainable Artificial Intelligence (XAI) in Medical Imaging: Techniques, Applications, Challenges, and Future Directions Purwono, Purwono Wulandari, Annastasya Nabila Elsa Nisa, Khoirun Explainable Artificial Intelligence medical imaging interpretability clinical trust deep learning The integration of Explainable Artificial Intelligence (XAI) into medical imaging is pivotal in addressing the “black-box” limitations of deep learning models, which often hinder clinical trust and regulatory approval. This review provides a comprehensive examination of XAI techniques that enhance interpretability and transparency in diagnostic imaging applications. Key approaches such as feature visualization (Grad-CAM, Integrated Gradients), attention mechanisms, symbolic reasoning, and example-based methods—are explored alongside their practical implementations. Specific cases in cardiac imaging, cancer diagnostics, and brain lesion segmentation illustrate the value of XAI in improving clinical decision-making and patient care. Moreover, the review highlights major challenges, including the trade-off between accuracy and interpretability, ethical and legal constraints, integration barriers within clinical workflows, and the complexity of medical data. To address these issues, future research directions are proposed, including the development of more robust example-based models, ethical frameworks, generalizable architectures, advanced visualization techniques, and interdisciplinary collaboration. With continued refinement and responsible deployment, XAI systems can enable AI models to become not only accurate but also interpretable and clinically relevant. This paper underscores the transformative potential of XAI in building trustworthy, transparent, and effective AI-driven diagnostic tools aligned with the practical demands of modern healthcare systems. Tecno Scientifica Publishing 2025-07-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/amms/article/view/692 10.53623/amms.v1i1.692 Advanced Mechanical and Mechatronic Systems ; Volume 1 - Issue 1 - 2025; 52-66 3122-6752 10.53623/amms.v1i1 eng https://tecnoscientifica.com/journal/amms/article/view/692/336 Copyright (c) 2025 Purwono Purwono, Annastasya Nabila Elsa Wulandari, Khoirun Nisa https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/701 2025-12-09T10:39:40Z amms:ART

Stilleto Type High Heel Shoe Design and Presesure Analysis with Adjustable Height Wibowo, Dwi Basuki Adham Adhwa Adibawa Harahap, Rudiansyah Ariadi, Yudhi Adjustable, distribution, high heels, pressure, shoe, stilleto Wearing high heels has been a consistent component in fashion trends for women in a variety of endeavors ranging from business to social settings. Research into the design of height-adjustable stilleto-type high heels is a response to shifting demands in the fashion industry. Consumer demand was not only focused on aesthetic appeal but also on comfort. Conventional high heels, especially the stiletto type, often had limitations in terms of long-term comfort due to their fixed height. This led to the need for innovative designs that allowed users to customize heel height according to their preference and comfort. This study presented the steps taken to develop adjustable high heels and analyzed how pressure was distributed on the sole of the foot. The pressure distribution on the soles of the feet while wearing adjustable high heels was measured using the FSR 400 device available at a shoe orthotics facility. The study aimed to develop an adjustable high-heel design that enhanced both fashion and user comfort through integrated design and pressure analysis. The manufacture of an adjustable high heel shoe model in this study was successfully completed by implementing an unloading system, where the heel featured two height options, 3 cm and 5 cm, and a screw-based locking mechanism. The subject of this research was was a 20-year-old female Mechanical Engineering student at Diponegoro University, with a shoe size of 39, a height of 159 cm, and a body weight of 45 kg. Test results revealed that heel pressure decreased as the heel height increased. Tecno Scientifica Publishing 2025-07-09 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://tecnoscientifica.com/journal/amms/article/view/701 10.53623/amms.v1i1.701 Advanced Mechanical and Mechatronic Systems ; Volume 1 - Issue 1 - 2025; 67-75 3122-6752 10.53623/amms.v1i1 eng https://tecnoscientifica.com/journal/amms/article/view/701/339 Copyright (c) 2025 Dwi Basuki Wibowo, Adham Adhwa Adibawa, Rudiansyah Harahap, Yudhi Ariadi https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/758 2025-12-09T10:39:57Z amms:ART

A Robot for Collecting Objects Based on the Convolutional Neural Network Method and Inertial Measurement Unit Sensor Syafiqah, Iffah Pamungkas, Daniel S. Mohd Ramli, Nurul Amira Convolutional Neural Network (CNN) Inertial Measurement Unit (IMU) sensor fusion autonomous robotics This study presents the development of an autonomous mobile robot for real-time object detection and collection by integrating a Convolutional Neural Network (CNN) with an Inertial Measurement Unit (IMU). The primary objective is to design, implement, and evaluate a sensor-fusion-based robotic system capable of detecting objects through image recognition, estimating orientation and motion via inertial sensing, and performing automated retrieval tasks in structured and semi-structured environments. The CNN is trained to recognize and localize objects using real-time video input, while the IMU provides data on the robot’s pose and dynamics. Through sensor fusion algorithms, the system achieves improved situational awareness, stability, and navigation accuracy. A closed-loop control framework translates sensory data into motion commands for the robot’s differential drive and gripper, enabling reliable object approach, grasping, and transport. Experimental results demonstrate high classification accuracy and a grasping success rate exceeding 85% in indoor tests. The proposed approach shows strong potential for applications in logistics, smart manufacturing, and service robotics, where repetitive object-handling tasks can be automated with reliability. Tecno Scientifica Publishing 2025-12-09 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://tecnoscientifica.com/journal/amms/article/view/758 10.53623/amms.v2i1.758 Advanced Mechanical and Mechatronic Systems ; Volume 2 - Issue 1 - 2026; 1-15 3122-6752 10.53623/amms.v2i1 eng https://tecnoscientifica.com/journal/amms/article/view/758/370 Copyright (c) 2025 Iffah Syafiqah, Daniel S. Pamungkas, Nurul Amira Mohd Ramli https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/764 2025-12-09T10:39:57Z amms:ART

Numerical Study: Crashworthiness of Hydrogen Powered Vehicle in a Collision Jacobs, Chipego Myo Aung, Kyaw Debnath , Sujan This numerical research focuses on the crashworthiness of a hydrogen powered vehicle in a collision including the safety of the hydrogen storage system. The model of the vehicle and the hydrogen storage system were developed in Ansys Space Claim. In another Ansys tool, Mechanical, the simulations for three crash scenarios were conducted. The simulations involved the modelled vehicle with the hydrogen system impacting a rigid wall in frontal, rear and side scenarios to assess the amount of deformation, stress distribution and the internal/total energy absorbed by the tanks. The results from the simulations showed that there was significant deformation and stress experienced by the hydrogen storage system. maximum stress values from the frontal impact were 4630.2 MPa which is way over values of typical failure points of Type IV tanks. From the side impact, it was noted too that the tanks had higher internal energy absorbed when compared to the other 2 scenarios. The recorded value of this amount of energy was 255.32 J and show there is a high risk of the tank rupturing or leaking. The data was analysed with other literature values confirming the found data from the simulations conducted. These findings demonstrate that even though the current configuration of the hydrogen system has less risk of failure from minor impacts, they are still in a state of vulnerability under severe crashes. Furthermore, the findings highlight the continued need of research on improving the configuration of storage systems, better protection systems and inclusion of many more parameters.    Tecno Scientifica Publishing 2025-12-09 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://tecnoscientifica.com/journal/amms/article/view/764 10.53623/amms.v2i1.764 Advanced Mechanical and Mechatronic Systems ; Volume 2 - Issue 1 - 2026; 16-32 3122-6752 10.53623/amms.v2i1 eng https://tecnoscientifica.com/journal/amms/article/view/764/403 Copyright (c) 2025 Chipego Jacobs, Kyaw Myo Aung, Sujan Debnath https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/776 2025-12-09T10:39:57Z amms:REV

Customized Prosthetic Feet via Topology Optimization and 3D Printing: A Critical Review Lestari, Wahyu Dwi Lestari Ariadi, Yudhi Putra, Azma topology optimization additive manufacturing prosthetic foot customization clinical validation This critical review examines the transformative impact of integrating topology optimization and additive manufacturing (AM) on the design and production of transtibial prosthetic feet. By systematically surveying peer-reviewed studies published between 2010 and 2024, this work highlights how computational algorithms—such as SIMP, level-set, and evolutionary methods—can achieve mass reductions of 50–70% while maintaining safety factors above 1.5. Concurrently, AM technologies including FDM, SLS, and SLA faithfully reproduce complex, patient-specific geometries with deviations under 5% from finite element analysis (FEA) predictions. Material innovations span thermoplastics (PLA, nylon 66), advanced composites (CFRP, titanium lattices), and emerging smart materials (shape-memory polymers, piezoelectric composites), collectively enhancing energy return by up to 30% and fatigue life by more than 10⁵ cycles. Comprehensive validation—encompassing ISO 10328 static testing, dynamic fatigue trials, gait simulations, and wearer trials—confirms both mechanical integrity and user comfort, aided by integrated sensor systems for real-time performance monitoring. Regulatory and clinical pathways, including ISO 13485, FDA 510(k), MDR, and ISO 14155 guidelines, are discussed to facilitate translation into practice. Future research should focus on multicenter clinical trials, open-access design repositories, adaptive materials, and machine learning–driven predictive maintenance to propel patient-centered innovation in prosthetic care. Tecno Scientifica Publishing 2025-12-09 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://tecnoscientifica.com/journal/amms/article/view/776 10.53623/amms.v2i1.776 Advanced Mechanical and Mechatronic Systems ; Volume 2 - Issue 1 - 2026; 33-49 3122-6752 10.53623/amms.v2i1 eng https://tecnoscientifica.com/journal/amms/article/view/776/402 Copyright (c) 2025 Wahyu Dwi Lestari Lestari, Yudhi Ariadi, Azma Putra https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/792 2025-12-09T10:39:57Z amms:ART

Integration of Vehicle Tracking, Control and Maintenance Zahri, Budiman Putri, Tiara Syah Pamungkas, Daniel Sutopo Vehicle tracking Vehicle management system GPS technology This research developed a vehicle tracking and control system aimed at improving fleet management efficiency in Batam. The problem addressed was the lack of an integrated solution that combined location monitoring, remote control, and service management for vehicles. The system integrated the ESP8266 microcontroller as the control unit, a Ublox Neo 6M GPS module for location tracking, a relay for engine control, and a SIM/GSM module for communication via HTTP. Data were stored in a PostgreSQL server and visualized using a web application developed with Node.js, Next.js, and Leaflet.js. The research objectives were to design, implement, and test a system capable of real-time vehicle monitoring, speed detection, and remote engine shutdown, accessible through a web browser on both computers and mobile devices. Testing was carried out using a motorcycle in the Batam Center area, showing that GPS readings, relay control, and data transmission were successfully executed. The highest longitude error observed was 0.000022, which remained within acceptable tolerance. The findings demonstrated that the system provided accurate and reliable vehicle tracking while offering practical solutions for fleet control. In conclusion, the developed system supported efficient vehicle management and could be further enhanced for broader fleet applications in urban areas. Tecno Scientifica Publishing 2025-12-09 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://tecnoscientifica.com/journal/amms/article/view/792 10.53623/amms.v2i1.792 Advanced Mechanical and Mechatronic Systems ; Volume 2 - Issue 1 - 2026; 50‒62 3122-6752 10.53623/amms.v2i1 eng https://tecnoscientifica.com/journal/amms/article/view/792/400 Copyright (c) 2025 Budiman Zahri, Tiara Syah Putri, Daniel Sutopo Pamungkas https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/797 2025-12-09T10:39:57Z amms:ART

Gait Generation Using a Fourier Series for a Quadruped Robot with 2-DoF Legs Munadi, M. Ariyanto, Mochammad This study proposed the development of a quadruped cat robot designed to achieve straight-line walking motion inspired by a cat’s gait. The objective was to enhance cat-like robotic mobility by addressing challenges in gait planning and joint coordination. The robot was constructed with 2 degrees of freedom (DoF) for each leg, using lightweight materials such as acrylic, plywood, and aluminum to balance strength and maneuverability. Geometric kinematic equations were applied to model the end-effector positions, and a Fourier series was employed to generate a smooth, periodic trajectory for the foot’s end-effector, minimizing jerky movements. The Fourier series fitting achieved high accuracy (R² ≈ 0.99) for the joint angles. The resulting prototype, controlled by an Arduino microcontroller, successfully demonstrated a stable and periodic gait cycle generated through the Fourier series approach, confirming the viability of this kinematic method for straight-line motion. Tecno Scientifica Publishing 2025-12-09 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://tecnoscientifica.com/journal/amms/article/view/797 10.53623/amms.v2i1.797 Advanced Mechanical and Mechatronic Systems ; Volume 2 - Issue 1 - 2026; 63-75 3122-6752 10.53623/amms.v2i1 eng https://tecnoscientifica.com/journal/amms/article/view/797/401 Copyright (c) 2025 M. Munadi, Mochammad Ariyanto https://creativecommons.org/licenses/by/4.0

oai:oai.tecnoscientifica.com:article/999 2026-04-20T01:36:56Z amms:ART

A Hybrid Machine Learning Framework for Multi-Limb Human Activity Recognition Using Synchronized Smartphone IMU Sensors: Dataset and Benchmarking Kurniawan, Ade Hidayat, Dadan Ramdan Saputra, Zain Iqbal Ayubi, Whirdyana Shalfa Rustin, Syifa Nurulfajri Mulya, Muhammad Ragil Rizky Mandolang, Chello Fhrino Mike Human Activity Recognition (HAR) using smartphone inertial measurement unit (IMU) sensors has emerged as a transformative technology for health monitoring, fitness tracking, and context-aware computing. However, existing HAR research is constrained by limited data availability, short recording durations, and single-limb sensing configurations. This study addresses these limitations through three principal contributions: (1) introduction of a novel open-access multi-limb HAR dataset featuring synchronized 60-second recordings from hand and ankle positions using tri-axial accelerometer, gyroscope, and magnetometer sensors, publicly available via Mendeley Data repository; (2) systematic benchmarking of classical machine learning classifiers including Random Forest, XGBoost, and Linear Support Vector Classifier under realistic multi-sensor fusion conditions; and (3) comprehensive analysis of model robustness across varying windowing configurations. The dataset comprises recordings from six participants performing six daily activities (walking, stair ascent, stair descent, standing, sitting, lying), totaling over 72 minutes of synchronized multi-sensor data. Experimental evaluation demonstrates that Random Forest achieves superior classification accuracy of 96.13%, significantly outperforming XGBoost (85.22%) and LinearSVC (58.54%). The publicly released dataset and benchmarking results provide a valuable resource for the HAR research community, enabling reproducible experimentation and facilitating advancement in multi-limb activity recognition systems. Tecno Scientifica Publishing 2026-04-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://tecnoscientifica.com/journal/amms/article/view/999 10.53623/amms.v2i2.999 Advanced Mechanical and Mechatronic Systems ; Volume 2 - Issue 2 - 2026; 76−87 3122-6752 10.53623/amms.v2i2 eng https://tecnoscientifica.com/journal/amms/article/view/999/512 Copyright (c) 2026 Ade Kurniawan, Dadan Ramdan Hidayat, Zain Iqbal Saputra, Whirdyana Shalfa Ayubi, Syifa Nurulfajri Rustin, Muhammad Ragil Rizky Mulya, Chello Fhrino Mike Mandolang https://creativecommons.org/licenses/by/4.0