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Journal of Engineering and Technological Sciences
Published by Institut Teknologi Bandung
ISSN : 23385502     EISSN : 23375779     DOI : 10.5614/j.eng.technol.sci
Core Subject : Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering
ournal of Engineering and Technological Sciences welcomes full research articles in: General Engineering Earth-Surface Processes Materials Science Environmental Science Mechanical Engineering Chemical Engineering Civil and Structural Engineering Authors are invited to submit articles that have not been published previously and are not under consideration elsewhere.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 132 Documents
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Volcanoes Segmentation at the Western Sunda Arc based on Satellite-derived Geological Lineaments and Land Surface Temperatures Rahmanto, Ridwan; Saepuloh, Asep; Kriswati, Estu; Purnamasari, Heruningtyas Desi
Journal of Engineering and Technological Sciences Vol. 57 No. 3 (2025): Vol. 57 No. 3 (2025): June
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.3.4

Abstract

The Western Sunda Arc is an active tectonic zone formed by the subduction of the Indo-Australian Plate beneath the Eurasian Plate. The tectonic zone hosted for 83 active volcanoes, including Mts. Sinabung, Krakatau, Tangkuban Parahu, Merapi, and Semeru. The dense volcano concentration and high volcanic activity cause complexity in monitoring and observation processes. Segmenting volcanoes by location and tectonic setting is necessary to simplify the disaster monitoring and enhance mitigation efforts through focused observation areas. This study focuses on the segmentation of the volcanoes distributed at the Sunda Arc in Indonesia by analyzing the satellite-derived geological lineaments and land surface temperatures. The Sunda Arc is a complex volcanic chain that spans through Sumatra and Java Islands and lies in an active tectonic region. Remote sensing data and advanced geospatial techniques were used to examine geological lineament patterns and surface temperatures along the volcanic arc and the results were validated through fieldwork. Moreover, Shuttle Radar Topography Mission (SRTM) and Landsat 8 OLI/TIRS imagery were applied to achieve accurate lineament extraction and surface temperature anomaly detection. Lineament density was also computed through the modified Segment Tracing Algorithm (mSTA) to identify the fault zones and structural discontinuities in order to ensure better regional geological understanding. Subsequently, land surface temperature analysis was used to classify thermal anomalies and this led to the differentiation of natural volcanic sources from ground surfaces. These parameters were integrated to segment the volcanoes of the Sunda Arc into nine zones. Each zone was presented by average lineament density from 207.83 km/km2 to 166.06 km/km2, land surface temperature from 23.36 °C to 28.65 °C, angle of subduction slab from 22.871° to 38.007°, and lineament strikes from N 330° E to N 260° E. The zones were later discussed relative to the gradient of the Sunda Arc subduction slab as a form of contribution to the existing knowledge on geothermal dynamics, tectonic processes, and volcanic hazards beyond the region.
Development of Active-Smart Packaging: Effect of Chitosan Nanofiber, Zinc Oxide Nanoparticles, and Anthocyanin on Gelatine-Based Halochromic Film for Meat Preservation Kusumawati, Nita; Bahar, Asrul; Basukiwardojo, Maria Monica Sianita; Samik, Samik; Rahayu, Nunik Tri; Estiningtyas, Indri Wasa; Kurniawan, Muhammad Ridho Hafid
Journal of Engineering and Technological Sciences Vol. 57 No. 3 (2025): Vol. 57 No. 3 (2025): June
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.3.7

Abstract

Gelatine-based smart active packaging has the potential to improve the quality of packaged meat and monitor its freshness without having to open it. This research aims to develop halochromic films by combining gelatine films with chitosan nanofibers (CHNF) and zinc oxide nanoparticles (ZnONPs). The addition of nanofillers such as CHNF and ZnONPs has been proven to improve mechanical properties (The humidity decreased by approximately 15.6%, while Young’s modulus increased tenfold) and provide active packaging properties, such as antioxidants (IC50 test decreased 13% from 33,12191 to 28,82021) and antimicrobials against S. aureus (increased from 9,40 to 19.73 for inhibition zone), E. coli (increased from 6.61 to 19.91 of inhibition zone), and P. aeruginosa (increased from 8.63 to 18.65 of inhibition zone). Meanwhile, the smart packaging properties are provided by anthocyanin from telang flowers, which can change color as the freshness of the meat decreases or the acidity of the meat changes. The quality of smart active packaging is reflected in the pH sensitivity, ammonia release, and anthocyanin release. The film's mechanical properties also showed improvement in humidity, Young's modulus, water vapor permeability (WVP), and water solubility. Fourier Transform Infra-Red (FTIR) characterization analysis showed good compatibility between the gelatine, anthocyanins, CHNF, and ZnONPs matrix. This research result demonstrates that gelatine-based films with a combination of CHNF and ZnONPs can be used to create eco-friendly and multifunctional packaging films for meat preservation.
Design and Test of a Secondary Shearing Device for the Lentinus Edodes Stem Wang , Zhen; Xiao , Weizhong; Liu , Yuandong; Zhang , Yuting; Liu , Qin; Cui, Xiao; Kong, Weili
Journal of Engineering and Technological Sciences Vol. 57 No. 3 (2025): Vol. 57 No. 3 (2025): June
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.3.8

Abstract

To address the high labor intensity and low efficiency in the manual stem cutting of Lentinus edodes (shiitake mushrooms), A secondary shearing device was designed. This device automated the shearing and separation of mushroom roots and stems, significantly improving the efficiency of the stem-cutting process. Evaluated through indicators such as productivity, pass rate, and damage rate, a four-factor and three-level response surface test was conducted. The test factors included tool velocity, stem length, number of blades, and conveying velocity. Using Design-Expert 12.0 software to analyze the test data, the optimal parameter combination was determined: a tool velocity of 5800 r/min, stem length of 28 mm, four blades, and a conveying velocity of 52.78 m/min. Under these conditions, the device achieved a productivity of 0.49 Kg/h, a pass rate of 91.18%, and a damage rate of 5.73%. Verification tests confirmed the reliability of these parameters, showing slight deviations from the experimental predictions but still meeting the requirements for Lentinus edodes stem cutting. This study demonstrated that the secondary shearing device could significantly reduce labor intensity and improve efficiency in mushroom cultivation practices. Future research should focus on integrating robotic systems for automation and exploring additional parameters to further enhance the device's performance.
An Adaptive Multi-region Fusion Network for Dense Face Detection Yang, Luxia; Zhang, Chuanghui; Zhang, Hongrui; Hou, Yilin
Journal of Engineering and Technological Sciences Vol. 57 No. 3 (2025): Vol. 57 No. 3 (2025): June
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.3.9

Abstract

In recent years, face detection has been widely applied in various intelligent monitoring systems. However, missed detections and low detection accuracy present challenges, such as small, blurred, and occluded faces in multi-face detection scenarios. To address these challenges, an adaptive multi-region fusion network is designed for dense face detection. First, in the shallow layers of the network, a multi-scale cross-stage fusion (MC4f) module is designed to replace the C3 module, which solves the issue of gradient explosion or disappearance in deep networks and promotes the effective convergence of the network on small datasets. An adaptive fusion explicit spatial vision centre (AESVC) is then designed between the backbone and neck networks to adaptively fuse local and global features to refine face information and enhance feature representation capabilities in complex tasks. Subsequently, a multi-scale parallel attention mechanism (MSPAM) is proposed to enhance the cross-scale fusion of facial features and reduce the loss of shallow features. Finally, to achieve accurate facial key point localisation and alignment, wing loss and A-loss functions are integrated, which balances the relationship between easy and difficult samples. Compared with the original model, the proposed model increases the mean average precision (mAP) by 1.75, 2.01, and 3.06% for easy, medium, and hard samples, respectively. The experimental results prove the effectiveness of the adaptive multi-region fusion network for dense face detection.
Improving Myoelectric Hand Gesture Recognition using Multiple High-density Maps Jaber, Hanadi A.; Hakim, Heba; Alhakeem, Zaineb Mohammed; G. Abood, Aum_Al Huda
Journal of Engineering and Technological Sciences Vol. 57 No. 3 (2025): Vol. 57 No. 3 (2025): June
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.3.10

Abstract

The identification of human motion intention through electromyography (EMG) signals is an important area of development in human–robot interaction. This technology aids amputees in controlling their prosthetic limbs in a more intuitive manner, facilitating the execution of daily activities. However, hand amputees face challenges in using dexterous prostheses due to control difficulties and low robustness in real-life situations. This study aims to enhance the accuracy of EMG gesture recognition by extracting spatial characteristics via multiple high density (HD) maps. A total of five HD-maps are generated utilizing the root mean square value (RMS), mean absolute value (MAV), zero crossings (ZC), sign slope changes (SSC), and waveform length (WL) features. The influence of each distinct HD-map, along with the synergistic effect of numerous HD-maps in the extraction of intensity features, is assessed with regard to its impact on classification accuracy. Three machine learning classifiers are employed to categorize nine hand movements of the Ninapro (DB5) dataset. The results show that features extracted from the combination of multiple HD-maps (CMHD) achieved a high accuracy in comparison to those of individual HD-maps. Moreover, the proposed features are superior to those of conventional TD features. The error rate is reduced by approximately 7.76% relative to time domain (TD) features. The results obtained confirm the significance of spatial features extracted from multiple HD-maps that ensure consistent information in different EMG channels
Fully Automated Ground Motion Selection Platform Considering Multiple Seismic Source Contributions for Seismic Evaluation of Existing Buildings in Indonesia Alexander, Nick; Irsyam, Masyhur; Asrurifak, Muhammad; Hendriyawan, Hendriyawan; Faizal, Lutfi
Journal of Engineering and Technological Sciences Vol. 57 No. 3 (2025): Vol. 57 No. 3 (2025): June
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.3.5

Abstract

Buildings throughout Indonesia are at risk of experiencing damaging earthquake events from multiple sources within shallow crustal and subduction zones. Seismic assessments per SNI 9273: 2024 require earthquake acceleration time series, but a public web-based platform that can facilitate this need was not yet available. Thereby, it became urgent to investigate the possibility of developing an automated engineering tool that can determine hazard-consistent ground motion set with good target-match accuracy for regions where multiple seismic source contribution is prevalent. The investigation was conducted through the development of a fully-automated ground motion selection and scaling platform specifically customized to facilitate the seismic evaluation and retrofit of existing buildings throughout Indonesia, limited to upper bound period range of 3 s. This platform has been officially launched for public access by the Indonesian Ministry of Public Works to enhance the efficiency and accuracy of seismic assessments with the broader goal of improving seismic safety. Completion and test case validation of the platform confirmed that such useful tool can be developed provided that the following key features are incorporated: (1) specifically-defined scope of application; (2) comprehensive data integration to consider multiple seismic source contribution; and (3) modified greedy selection algorithm to enhance target-match accuracy
Electroencephalogram-Based Multi-Class Driver Fatigue Detection using Power Spectral Density and Lightweight Convolutional Neural Networks Suprihatiningsih, Wiwit; Romahadi, Dedik; Feleke, Aberham Genetu
Journal of Engineering and Technological Sciences Vol. 57 No. 4 (2025): Vol. 57 No. 4 (2025): August
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.4.2

Abstract

Driver fatigue is the primary factor contributing to traffic accidents globally. To address this challenge, the electroencephalogram (EEG) has been proven reliable for assessing sleepiness, fatigue, and performance levels. Although alertness monitoring through EEG analysis has shown progress, its use is affected by complicated methods of collecting data and labelling more than two classes. Based on previous research, the original form of EEG signals or power spectral density (PSD) has been extensively applied to detect driver fatigue. This method needs a large, deep neural network to produce valuable features, requiring significant computational training resources. More observations regarding feature extraction and classification models are needed to reduce computational cost and optimize accuracy values. Therefore, this research aimed to propose a PSD-based feature optimization on a lightweight convolutional neural network (CNN) model. Five types of statistical functions and four types of signal power ratios were applied, and the best features were selected based on ranking algorithms. The results showed that feature optimization using the Relief Feature (ReliefF) algorithm had the highest accuracy. The proposed lightweight CNN model obtained an average intra-subject accuracy of 71.01%, while the cross-subject accuracy was 69.07%.
Deeper Insight into the Rational Design and Synthesis of Zeolites Revealed by Machine Learning: A Mini Review Mardiana, St; S. F. Nanda, Arxhel; Arcana, I Made; Ismunandar, Ismunandar; Fajar, Adroit T. N.; Kadja, Grandprix T. M.
Journal of Engineering and Technological Sciences Vol. 57 No. 4 (2025): Vol. 57 No. 4 (2025): August
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.4.4

Abstract

Zeolites are widely applied in various fields owing to their outstanding properties. However, our understanding on the nature of zeolite synthesis is not completed yet due to its high dimensional parameters. Machine learning has the ability to unravel fundamental relationships between complex parameters and predict the possible outcomes; thus, it can potentially reveal the nature of zeolite synthesis. This mini review highlights the current use of machine learning to comprehend the black box issue in zeolite synthesis. Conventional syntheses of zeolite were also elaborated to showcase the gap between traditional methods and machine learning approaches in zeolite synthesis. The future prospects of machine learning applications in zeolite synthesis are also discussed. This mini-review may bring crucial insights on the zeolite synthesis process.
Synthesis of Geopolymer from Ferronickel Aluminosilicate Waste Samadhi, Tjokorde Walmiki; Wulandari, Winny; Dwinidasari, Aya Anisa; Rahmasari, Arum
Journal of Engineering and Technological Sciences Vol. 57 No. 4 (2025): Vol. 57 No. 4 (2025): August
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.4.1

Abstract

The nickel industry in Indonesia generates massive volumes of ferronickel slag that may harm the environment. This research evaluates the feasibility of utilizing coal fly ash and slag from a ferronickel smelter in Obi Island in Indonesia to synthesize geopolymer, an environmentally friendly cementitious material. Compressive strength of geopolymer mortars was measured as a function of slag particle size (coarse and fine), fly ash mass fraction in the dry aluminosilicate binder precursor blends (0.4 and 0.8), and thermal curing period (24 and 48 hours). Mortar specimens were produced by mixing ash and slag with activator solution and sand. The activator solution contained Na2SiO3 and NaOH at a mass ratio of 2:1. Solid reactants to activator solution mass ratio was 3.33. After heat curing, specimens were held in ambient conditions to an age of 7 days. The compressive strength of the mortars was in the 2.1-24.8 MPa range. Geopolymer mortars were able to comply to Indonesian SNI 15-2049-2004 or US ASTM C1329-05 standards for Portland cement. FTIR and XRD characterizations confirmed the conversion of fly ash and slag into amorphous geopolymers at near ambient temperature. Finer slag particle size increased reactivity, ultimately producing higher compressive strength.
Design and Application of a Kirigami-Based Soft Robotic Gripper using Finite Element Analysis Gomes, Efrem Olivio; Chang, Shyang-Jye; Saputra, Ilham
Journal of Engineering and Technological Sciences Vol. 57 No. 4 (2025): Vol. 57 No. 4 (2025): August
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.4.3

Abstract

The demand for adaptable and efficient soft robotic grippers has grown due to their potential applications in industries such as food handling, manufacturing, and logistics. This study explores a Kirigami-based soft robotic gripper, designed to handle a wide range of objects with minimal risk of damage. The gripper utilizes a Kirigami-inspired structure combined with Liquid Silicone Rubber (LSR CN-251), chosen for its flexibility, durability, and food-safe properties. Finite element analysis was conducted to analyze the gripper’s mechanical performance under tensile forces ranging from 0.1 N to 4.3 N, focusing on stress distribution and deformation. Experimental validation was performed to verify the simulated results and assess the gripper’s performance in real-world scenarios. The simulations revealed predictable stress distribution and controlled deformation, with experimental tests demonstrating the gripper’s successful handling of delicate items, irregular objects, heavier item, and others. The Kirigami structure’s passive force distribution enabled a secure yet gentle grip, minimizing the risk of damage. The gripper’s adaptability, flexibility, and lightweight construction were confirmed in these tests. Manufactured from food-safe LSR, the gripper presents a cost-effective and efficient alternative to traditional pneumatic or jamming-based grippers. Limitations in the experimental setup, such as the restricted range of the uArm Swift Pro, were noted, and future research should explore dynamic performance under real-world conditions, enhance the range of motion, and integrate sensory feedback for improved precision.

Page 10 of 14 | Total Record : 132

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