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Yuliah Qotimah
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ITB Journal, Gedung CRCS ITB Lantai 6 Jalan Ganesa No. 10 40132 Bandung - Indonesia
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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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The Effect of Illumination, Electrode Distance, and Illumination Periods on the Performance of Phototrophic Sediment Microbial Fuel Cells (PSMFCs) Harimawan, Ardiyan; Devianto, Hary; Khodiyat, Nicholas; Gatalie, Kreszen Livianus; Aslan, Christian
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
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.2024.56.1.1

Abstract

Microbial fuel cells (MFCs) can potentially be used to overcome issues with battery powered light buoys and their frequent maintenance. In this study, a phototrophic sediment microbial fuel cell (PSMFC) was chosen, as the microalgae provide oxygen to be reduced on the cathode and to release the necessary nutrients for the bacteria on the anode. To achieve this, we studied the effect of illumination, the period of the illumination, and the distance between 9-cm2 stainless steel mesh electrodes on the performance of the MFC. The illuminated cells were able to produce higher OCP (max. 205.2 mV) and higher power density (max. 0.68 mW/m2). However, the highest current was achieved during the unilluminated variation (max. 5.3 μA unilluminated and 3.3 μA illuminated). Prolonged illumination produced a higher OCP, current, and power density. A longer electrode distance produced a higher OCP, power density, and current. SEM analysis showed that biofilm formation tended to be scattered at lower electrode distance and more clumped (filling the anode area) at higher electrode distance. Through FTIR analysis, it was found that all MFC variations had the same organic matter, but a more concentrated organic content was found in the MFC at longer electrode distances.
Bridge Capacity Assessment through LRFR Method and Bridge Seismic Performance Evaluation Using the PBSD Concept: Case Study Sabara, Akhmad Ilham Ramadhan; Imran, Iswandi
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
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.2024.56.1.2

Abstract

In this study, a comprehensive evaluation was conducted of the condition and performance of a concrete arch-type bridge located in close proximity to a fault. Utilizing the LRFR capacity assessment method and seismic performance analysis through the NLTHA process based on the PBSD concept, finite element modeling (FEM) was employed with a focus on construction stage analysis and model updating for calibration to site conditions. The assessment encompassed the determination of the rating factor for structural elements under service and ultimate limit state loading. Performance analysis under seismic loads includes an examination of engineering demand parameters such as concrete and reinforcement strains in columns, subjected to varying seismic hazard levels. Additional scrutiny involves assessing bearing displacement and overturning potential to identify potential damage before column failure. The primary objective of this research was to investigate pertinent and efficient techniques for evaluating bridge capacity and seismic performance. A thorough understanding of these methods is expected to facilitate the identification of suitable solutions to enhance the safety and reliability of bridges in Indonesia.
Circular Economy Approaches in the Palm Oil Industry: Enhancing Profitability through Waste Reduction and Product Diversification Siagian, Utjok Welo Risma; Wenten, I Gede; Khoiruddin, Khoiruddin
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
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.2024.56.1.3

Abstract

Today, facing difficult environmental and sustainability questions, the palm oil industry is an important force in global trade and development. As a transformative solution to these problems, this review assesses the implementation of circular economy (CE) strategies. CE principles promote the transformation of waste into value through recycling, upcycling and other low-carbon innovation applications. This review estimates the capability of palm-based biomass, including palm oil mill effluent (POME) and refinery wastes. It evaluates how different technologies such as gasification are used to change these fuel sources into energy fuels and value-added products for industry. It also involves incorporating Industry 4.0 to boost efficiency and waste value creation into the operation. Although the potential of CE in creating an eco-friendly, profitable palm oil industry is apparent, nevertheless it must overcome all kinds and levels of barriers – from economic to technological to social. This review points out for collaborative efforts, technological advancement, and supportive policies to navigate these challenges, advocating for a unified shift towards sustainability and efficiency in the palm oil sector.
Hematite-Gamma Alumina-based Solid Catalyst Development for Biodiesel Production from Palm Oil Rizkiana, Jenny; Bryan, Bryan; Gozali, Edbert; Bustomi, Agus Tendi Ahmad; Prakoso, Tirto
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
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.2024.56.1.4

Abstract

This research investigated the performance of hematite-gamma alumina (Fe2O3/γ-Al2O3) catalyst in biodiesel production from palm oil. A full factorial experimental design was utilized to analyze the effect of hematite content, catalyst loading, and methanol-to-oil ratio on catalyst performance. From the experiment, biodiesel in the range of 73.6 to 87.6% FAME content was obtained. It was concluded that the catalyst composition, the methanol-to-oil ratio, and the catalyst loading have a significant effect on the FAME content of the biodiesel. Hematite has strong affinity for fatty acids, so a larger hematite surface area will result in a higher fatty acid absorption capacity. The addition of excess methanol can reduce the contact inhibition between the reactants and the active site of the catalyst, thereby increasing the conversion rate of the reaction. Moreover, a higher amount of catalyst loading can result in an increase in the FAME content when accompanied by an increase in the hematite content of the catalyst.
Comparison Study of Corn Leaf Disease Detection based on Deep Learning YOLO-v5 and YOLO-v8 Chitraningrum, Nidya; Banowati, Lies; Herdiana, Dina; Mulyati, Budi; Sakti, Indra; Fudholi, Ahmad; Saputra, Huzair; Farishi, Salman; Muchtar, Kahlil; Andria, Agus
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
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.2024.56.1.5

Abstract

Corn is one of the primary carbohydrate-rich food commodities in Southeast Asian countries, among which Indonesia. Corn production is highly dependent on the health of the corn plant. Infected plants will decrease corn plant productivity. Usually, corn farmers use conventional methods to control diseases in corn plants. Still, these methods are not effective and efficient because they require a long time and a lot of human labor. Deep learning-based plant disease detection has recently been used for early disease detection in agriculture. In this work, we used convolutional neural network algorithms, namely YOLO-v5 and YOLO-v8, to detect infected corn leaves in the public data set called ‘Corn Leaf Infection Data set’ from the Kaggle repository. We compared the mean average precision (mAP) of mAP 50 and mAP 50-95 between YOLO-v5 and YOLO-v8. YOLO-v8 showed better accuracy at an mAP 50 of 0.965 and an mAP 50-95 of 0.727. YOLO-v8 also showed a higher detection number of 12 detections than YOLO-v5 at 11 detections. Both YOLO algorithms required about 2.49 to 3.75 hours to detect the infected corn leaves. This all-trained model could be an effective solution for early disease detection in future corn plantations.
Asphalt Concrete Production Technology Using Oil Sludge from Zhaik Munay LLP Satayeva, Sapura Sanievna; Burakhta, Vera Alekseevna; Urazova, Aliya Frunzeevna; Nazarova, Dauriya Sagindykovna; Khamzina, Bayan Elemesovna; Begaliyeva, Raikhan Sabitovna; Shinguzhieva, Altynay Bakytzhanovna; Satybayeva, Nurgul Artigalievna; Yerzhanova, Zhadyra Toigalievna; Murzagaliyeva, Alma Askarovna
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
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.2024.56.1.6

Abstract

Oil sludge exhibits a compositional similarity to bitumen, a pivotal constituent in asphalt concrete mixtures. This similarity underscores the potential applicability of oil waste in the production of asphalt concrete, serving not only as an organic binder to fortify indigenous soils but also as a binding agent for the fabrication of organomineral mixtures. The incorporation of oil sludge in road construction endeavors holds promise for the conservation of natural resources, the amelioration of the environmental landscape, and a concurrent reduction in the cost of construction materials. The focus of this study encompasses a comprehensive examination of the physical and mechanical properties pertaining to asphalt concrete of Grade I, Type B. To enhance the performance attributes of asphalt concrete, an additive in the form of oil sludge sourced from ZhaikMunay LLP (Uralsk) was introduced. Various proportions of oil sludge, namely 5%, 10%, and 15%, were incorporated into the asphalt concrete mixture. The utilization of 5% oil sludge elicited negligible alterations in the properties of the asphalt concrete. However, with a 15% addition of oil sludge, discernible reductions were observed in maximum compressive efficiency (0.03% by volume) and shear resistance, indicated by the internal friction coefficient efficiency (0.01% by volume).
Minimize Total Cost and Maximize Total Profit for Power Systems with Pumped Storage Hydro and Renewable Power Plants Using Improved Self-Organizing Migration Algorithm Tran, Dao Trong; Phan, Tan Minh
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
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.2024.56.1.7

Abstract

This study presents the application of an improved self-organizing migration algorithm (ISOMA) for minimizing the total electricity production expenditure (TEPE) and maximizing the total electricity sale profit (TPRF) for hydrothermal power systems (HTPS) without and with renewable energies. Two power system configurations were employed to test the real efficiency of ISOMA while dealing with two objective functions. In the first configuration, there was one thermal power plant and one hydropower plant, while in the second configuration, wind and solar energy were both connected to the first system. The results achieved in the first configuration with the first objective function indicated that ISOMA not only outperformed SOMA according to all comparison criteria but was also superior to other methods such as evolutionary programming (EP), acceleration factor-based particle swarm optimization (AFPSO), and accelerated particle swarm optimization (APSO). The evaluation of the results achieved by ISOMA in the second configuration with the objective function of maximizing the TPRF revealed that ISOMA could reach better profits than SOMA in terms of maximum, mean and minimum TPRF values over fifty trial runs. As a result, it was concluded that pumped storage hydropower plants are very useful in integrating with renewable power plants to cut total cost for thermal power plants and in reaching the highest profit for the whole system. Also, ISOMA is a suitable algorithm for the considered problem.
Classifying Coal Mine Pillar Stability Areas with Multiclass SVM on Ensemble Learning Models Hertono, Gatot Fatwanto; Wattimena, Ridho Kresna; Mendrofa, Gabriella Aileen; Handari, Bevina Desjwiandra
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
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.2024.56.1.8

Abstract

Pillars are key structural components in coal mining. The safety requirements of underground coal mines are non-negotiable. Accurately classifying the areas of pillar stability helps ensure safety in coal mines. This study aimed to classify new pillar stability categories and their stability areas. The multiclass support vector machine (SVM) method was implemented with two types of kernel functions (polynomial and radial basis function (RBF) kernels) on pillar stability data with four new categories: failed or intact, either with or without an appropriate safety factor. This classification uses three basic ensemble learning models: Artificial Neural Network-Backpropagation Rectified Linear Unit, Artificial Neural Network-Backpropagation Exponential Linear Unit, and Artificial Neural Network-Backpropagation Gaussian Error Linear Unit. The results with four data proportions and ten experiments had an average accuracy and standard deviation of 92.98% and 0.56%-1.64% respectively. The accuracies of the multiclass SVM method using the polynomial kernel and the RBF kernel with Bayesian parameter optimization to classify the areas of pillar stability were 91% and 92%, respectively. The multiclass SVM method with the RBF kernel captured 96.6% of potentially dangerous pillars. The visualization of classification areas showed that areas with intact pillars may also have failed pillars.
Comparison of the Mechanical Properties and Approach to Numerical Modeling of Fiber-reinforced Composite, High-Strength Steel and Aluminum Abdulqadir, Samer Fakhri; Alaseel, Bassam Hamid; Sameer, Jamal Oudah
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
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.2023.56.1.9

Abstract

The performance of carbon fiber reinforced polymer (CFRP) composite materials under quasi-static and high strain rate loading can be predicted with a high level of accuracy using the non-linear finite element analysis (FEA) method. Experimental validation tests under uniaxial tensile loading have shown a good correlation with FEA predictions for thermoset polymer composites, using commercially available epoxy resin MTM710 with carbon fiber reinforcement and for comparative tests on DP600 steel and aluminum alloys (AC170 and 5754 series). The physical and numerical results comparison of composite, aluminum, and high-strength steel indicates that the composite may be used as an alternative to aluminum and high-strength steel since the composite was shown to have almost the same strength as steel and higher strength than aluminum with the advantage of being lightweight and possessing similar mechanical behavior under quasi-static conditions. The results demonstrated that the strain rate range used did not significantly affect the strength of the composite materials. The selection of materials can be optimized reliably by FEA based on mechanical properties, cost, and weight. This will significantly reduce the new product introduction timescale, which is essential for the wider use of polymer composites for structural applications, especially in the automotive industry.
Fault Surface Rupture Modeling Using Particle Image Velocimetry Analysis of Analog Sandbox Model Furqan, Terry Alfa; Sapiie, Benyamin; Natawidjaja, Danny Hilman; Widodo, Lilik Eko; Rudyawan, Alfend; Hadiana, Meli
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
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.2024.56.1.10

Abstract

This study investigated the correlation between fault kinematics, surficial displacement, and surface rupture geometry patterns between earthquake cycles using particle image velocimetry (PIV) analysis of an analogue sandbox modeling that mimics InSAR observations. The research explored various fault systems, including reverse, normal, and strike-slip faults, through controlled sandbox experiments. The fault surface rupture zone manifests itself due to strain accumulation between two mobile blocks. The displacement magnitude is most pronounced on the surface and is absorbed by the section above the hanging wall or moving block. During fault surface rupture formation, the leading edge of the surface movement consistently extends beyond the anticipated fault surface rupture zone and retreats upon full fault surface rupture development. Subsequently, the distribution of the surface movement is sharply confined by the established fault surface rupture. The key findings of this study underscore the potential of PIV of sandbox modeling for studying fault surface rupture geometry and its development, providing insight into seismic processes. Overall, this work contributes to advancing our knowledge of seismic phenomena and improving strategies for earthquake prediction and mitigation.

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