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Makara Journal of Technology
Published by Universitas Indonesia
ISSN : 23552786     EISSN : 23564539     DOI : https://doi.org/10.7454/mjt
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering
MAKARA Journal of Technology is a peer-reviewed multidisciplinary journal committed to the advancement of scholarly knowledge and research findings of the several branches of Engineering and Technology. The Journal publishes new results, original articles, reviews, and research notes whose content and approach are of interest to a wide range of scholars. It also offers rapid dissemination. MAKARA Journal of Technology covers the recent research in several branches of engineering and technology include Electrical & Electronics Engineering, Computer Engineering, Mechanical Engineering, Chemical & Bioprocess Engineering, Material & Metallurgical Engineering, Industrial Engineering, Civil & Architecture Engineering, and Marine Engineering. Criteria used in determining acceptability of contributions include newsworthiness to a substantial part of the engineering & technology and the effect of rapid publication on the research of others. This journal, published three times each year, is where readers look for the advancement of discoveries in engineering and technology.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 500 Documents
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Multicriteria Analysis of Vehicle Exhausts Emission using Fuzzy Analytic Hierarchy Oke, Sunday Ayoola; Abdul, Ibraheem Adedotun; Badmus, Ismaila; Rajan, John; Jose, Swaminathan; Yekinni, Adekunle Adetayo; Olaiya, Kabiru Alani; Adeniran, Mofoluwaso Kehinde; Benrajesh, Pandiaraj
Makara Journal of Technology Vol. 30, No. 1
Publisher : UI Scholars Hub

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Abstract

This study employs the fuzzy analytic hierarchy process (FAHP) to identify the critical factors and their degree of relevance to the vehicle emission process. Its innovation lies in the potential to blend ambiguity and uncertainty with the established AHP. FAHP transforms information into a defuzzification state through signal-to-noise ratios, normalization, and pairwise comparison. The principal parameters considered are revenue, sold packing units, CAGR, packing materials, consumption, and CO2 emissions (A, B, C, D, E, and F, respectively). From the normalized defuzzified weight result, consumption (Parameter E) is the best (normalized weight, 0.8685917), while CO2 emissions (Parameter F) was the worst (normalized weight, 0.050454358). Considering the weights and ranks of the data, run order 4 ranked first with values of 0.2414, 0.0903, 0.1864, 0.1448, 0.2867, 0.0412, and 0.99078 for parameters A, B, C, D, E, and F, respectively. This work is useful for logistics managers who wish to control vehicle emissions. Manufacturers could reduce vehicle emissions by improving the combustion process designs through the data for the ranking of these prioritized parameters.
The PID-Based Three Quadcopter UAVs Formation Control under External Disturbance An, Vo Van; Mien, Trinh Luong
Makara Journal of Technology Vol. 30, No. 1
Publisher : UI Scholars Hub

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Abstract

This paper presents the design and evaluation of a formation control strategy for three quadcopter UAVs, based on a PID controller in a leader–follower structure, under the influence of external disturbances. Each UAV employs a six-degree of-freedom dynamic model and utilises a cascade PID control architecture, in which the inner control loop stabilises the attitude. In contrast, the outer control loop regulates position and maintains the formation. The PID parameters are tuned using the Ziegler–Nichols method to ensure simple implementation and low computational cost. The performance of the control system is evaluated through simulations in the MATLAB environment for two typical formation shapes (linear and triangular), both in noise-free conditions and under external disturbances. Simulation results show that in noise-free conditions, the system achieves a rise time of about 2–3 seconds, a settling time of 10–15 seconds for the linear formation, and 20–25 seconds for the triangular formation, with overshoot less than 15% on the position axes and a steady-state error of approximately 0.05 meters. When subjected to external disturbances, the UAVs still maintain the desired formation with a steady-state error of less than 0.1–0.5 meters, demonstrating an acceptable level of disturbance resistance. These results prove that, despite its simple structure, the proposed Leader–Follower PID control strategy can still ensure stable formation and effective trajectory tracking for small-scale UAV systems, while also being suitable for practical applications that require low cost and high deployability.
Global Research on Palm Oil Diseases and Infections: A Bibliometric Analysis Saputra, Fahmi Achmad; Mustika, Indria Puti; Safarida, Anna; Sriherwanto, Catur; Putra, Nicky Rahmana
Makara Journal of Technology Vol. 30, No. 1
Publisher : UI Scholars Hub

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Abstract

The sustainability of global palm oil production faces significant threats from a range of diseases and infections affecting palm oil crops. This study presents a comprehensive bibliometric analysis to map the scientific landscape and evolution of research related to palm oil diseases over the past decade. Using data from the Scopus database, 366 relevant documents were analyzed using R and VOSviewer to uncover trends in publication volume, citation impact, prolific authors, country contributions, and keyword co-occurrence networks. The findings reveal a strong research concentration on G. boninense, highlighting its critical role as a persistent pathogen in oil palm plantations. Emerging themes, such as bioactive compound research, comparative biology, and plant–microbiome interactions, reflect the growing interdisciplinary interest in sustainable disease management. This study identified key research gaps and proposed future directions, including molecular breeding, biocontrol strategies, and valorization of bioactive compounds from oil palm by-products. The results offer strategic insights to guide future scientific inquiry and inform sustainable solutions for global palm oil production.
A Deep Learning-Based Approach for Bot Detection in Trending Hashtags on X Hussain, Mehboob; Rana, Muhammad Rizwan Rashid; Imran, Muhammad; Shoaib, Muhammad; Mujtaba, Muhammad Hasaan
Makara Journal of Technology Vol. 30, No. 1
Publisher : UI Scholars Hub

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Abstract

The widespread presence of bots on social media platforms, such as X (formerly Twitter), poses a significant threat to the integrity of online information by facilitating the dissemination of misinformation and manipulating public discourse. This study proposes a robust deep learning-based framework, DeepBot, to detect bot participation in trending hashtags and discussions on X. The approach uses a dataset sourced from Kaggle, comprising user profile metadata, including follower count, tweet frequency, account verification status, and engagement metrics. The data were subjected to comprehensive preprocessing, including noise removal, part-of-speech (POS) tagging, and word embedding using the pre-trained GloVe model. RoBERTa is employed for feature extraction to capture deep contextual semantics, followed by classification through a deep neural network (DNN) to effectively distinguish between human users and bots. The proposed model is evaluated against established baselines using standard performance metrics. Experimental results demonstrate that DeepBot achieves superior performance with an accuracy of 92.82%, precision of 91.24%, and recall of 91.78%, confirming its effectiveness in enhancing the reliability of bot detection in social media trend analysis.
Implementation of Kansei Engineering Method in Designing Spreadable Cheese Packaging Sari, Novi Purnama; Analisa, Mega; Lestari, Suci Puji; Amalia, Khofifah Nur
Makara Journal of Technology Vol. 30, No. 1
Publisher : UI Scholars Hub

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Abstract

Some spreadable cheese packaging uses flexible plastic without additional features and easily damaged boxes. This packaging causes several problems, such as difficulty in re-storage, loss of product information due to damaged box packaging, wasteful use of packaging materials, and making the product less hygienic. The purpose of this study is to develop packaging that focuses on consumer emotions. The packaging was developed using the Kansei Engineering method, supported by Principal Component Analysis (PCA) as a determinant of the design concept, and Quantification Theory Type 1 (QTT1) to identify design elements. This study produced 46 packaging samples and 40 Kansei words. The resulting design concept has the highest principal component value, namely safety-practical, with an R-Square value of 0.7855. The design elements for the safe concept to being developed consist of a unique box shape (X1), PP plastic material (X2), medium size (X3), modern design style (X4), twist cap (X5), printing technique using stickers (X6), and upward lid orientation (X7). The results of this study successfully realized safe packaging through the selection of rigid plastic (PP) material with a twist cap. These results can also indicate consumer needs for functional packaging that solves product problems.
Investigation of Bridge Foundation Depth using Geoelectrical Dipole Configuration Latif, Devi Oktaviana; Hazhiyah, Amalia ula; Purnama, Adhitya yoga; Suyanto, Ika Rahmawati; Puspitasari, Surya Dewi; Utomo, Heru Budi
Makara Journal of Technology Vol. 30, No. 2
Publisher : UI Scholars Hub

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Abstract

Bridges are constructed to facilitate the crossing of pedestrians, vehicles, or trains and are essential components of land transportation infrastructure, which is crucial for ensuring the smooth flow of traffic. As part of the reconstruction project for the south side of the Muja Muju Bridge on Jalan Kusumanegara, the Yogyakarta City Government rebuilt this section of the bridge, which had become unfit for traffic use. The area features two bridges, serving as transportation routes: the Muja Muju Bridge on the north side and the newly reconstructed bridge on the south side. The demolition of the south side of the Muja Muju Bridge, as the initial stage of the project, will impact the construction of the bridge on the north side. Excavation for the foundation on the south side must consider various safety considerations, one of which is determining the depth of the foundation on the north side. In this study, the foundation depth is assessed through the geoelectrical measurement method, specifically the dipole–dipole technique. The interpretation of the results indicates that the foundation lies at a depth of 9 to 10 m from the soil surface.
Early Detection of Alzheimer’s Disease using Federated Learning Based on Personalized GANs: A Data-Driven Approach Fatima, Iffat; Iqbal, Uzair; Ishtiaq, Uzair; Ul Islam Khan, Muhammad Burhan
Makara Journal of Technology Vol. 30, No. 2
Publisher : UI Scholars Hub

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Abstract

Alzheimer’s disease (AD) is a complicated neurodegenerative disease marked by the slow worsening of memory and cognitive functions. Early detection is a key to effective disease management, but the creation of reliable machine learning models is not an easy task because medical data are limited, and there are strict privacy requirements, such as GDPR and HIPAA, that inhibit the creation of central databases. In this paper, a new privacy-saving model that combines personalized federated learning (PFL) with generative adversarial networks (GANs) is proposed to diagnose Alzheimer’s disease. Under the proposed method, the synthesis of data using GAN is conducted on the client to expand small datasets and counteract the performance loss due to non-identically distributed (non-IID) data across institutions. Massive testing was performed on the OASIS MRI dataset and compared with known federated learning baselines, such as FedAvg, FedSGD, and FedPer. The perFedGAN framework was proposed, and in IID settings, the proposed method achieved an accuracy of 96.41%. In non-IID difficult settings, the proposed method had an accuracy of 92.5%, which is a significant improvement over traditional federated averaging methodologies and also better robustness and personalization among dissimilar clients. This finding demonstrates the importance of using a combination of generative modeling and personalized federated learning to enable the precise, scalable, and privacy-preserving early detection of AD.
Design of a Pneumatic Actuator-Based Solar Tracker to Address the High Costs and Energy Shortages in a Center Pivot Irrigation System in the Karbala Desert A. Abdul-Hussein, Mustafa; A.-K. Mohammed, Jamal
Makara Journal of Technology Vol. 30, No. 2
Publisher : UI Scholars Hub

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Abstract

CPI systems used to spray crops in the Karbala Desert face two main problems: the cost of generating electricity with generators and the difficulty of delivering electricity from the grid to remote areas. Second, the fixed solar panels feeding some of these systems cannot produce enough electricity because they cannot track the sun’s rays. To solve these issues, appropriate actuators must be combined with these panels to form solar trackers. Due to their advantages over others, pneumatic actuators are proposed to enable the panels to constantly track the sun’s rays. This leads to the production of as much energy as possible during daylight hours. This study aims to design a single-axis pneumatic solar tracker using MATLAB/Simulink to produce sufficient electrical power to operate the entire irrigation system. An economic feasibility study was conduct for the developed system. Mathematical calculations were performed on the irrigation system to obtain the initial values necessary to complete the design procedure. The design results showed that using the pneumatic tracker enabled the PV panels to efficiently run the entire irrigation system during the day. Moving the panels on one axis increased the average panel’s efficiency by 48% compared to fixed panels. The feasibility study results indicate that building a CPI system using a solar tracker is expected to have 29.41% lower energy costs than a similar system with fixed panels.
Semantic-Guided Diffusion Autoencoder for Medical Image Synthesis and Representation Learning Yousefi, Neda; Koochari, Abbas; Zamanifar, Azadeh
Makara Journal of Technology Vol. 30, No. 2
Publisher : UI Scholars Hub

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Abstract

The development of robust machine learning models for medical diagnostics is constrained by the scarcity of large, annotated imaging datasets, a challenge rooted in high acquisition costs and stringent privacy regulations. To address this, generative approaches for data synthesis have emerged as an essential tool, and this study explores the use of diffusion models to generate medical images. This study introduces a novel Diffusion Autoencoder Model (DAM) integrated with a semantic encoder framework to generate interpretable and decodable image representations. Our DAM learns disentangled latent features via its encoder, which then conditions a diffusion probabilistic model acting as the decoder, enabling the synthesis of highly informative medical images. The model’s performance is assessed by using quantitative metrics demonstrating superior results compared to state-of-the-art generative models. In addition to its generative precision, the proposed model has significant potential for image classification. Notably, the semantic encoder learned by the model generates representations that lead to a significant increase in classification accuracy: for PneumoniaMNIST, the binary classification accuracy improved from 0.854 to 0.95; for RetinaMNIST, the multiclass accuracy increased from 0.528 to 0.94. These findings underscore the potential of DAMs to tackle data scarcity issues, enhance classification performance, and ultimately improve diagnostic methodologies.
Thermal Management Solutions for EV Batteries: Advanced Heat Transfer Mechanisms and Material Innovation Unegbu, Hyginus Chidiebere Onyekachi; Yawas, Danjuma Saleh
Makara Journal of Technology Vol. 30, No. 2
Publisher : UI Scholars Hub

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Abstract

Effective thermal management is critical for ensuring the safety, performance, and longevity of EV batteries. This study presents a comprehensive assessment of advanced BTMSs using CFD simulations, experimental validation, and statistical modeling. Three advanced heat transfer mechanisms microchannel cooling, heat pipes, and jet impingement cooling— were evaluated alongside novel materials, including graphene-enhanced TIMs and hybrid PCMs incorporating carbon nanotubes (CNTs). Jet impingement cooling provided the lowest peak temperature (33.8 °C) and the greatest heat dissipation efficiency (94.5%) among the tested systems. Microchannel cooling maintained 34.5 °C with superior temperature uniformity while graphene CNT hybrid PCMs reached thermal conductivities of up to 15.8 W/m·K, reducing the maximum temperature to 32.4 °C under high-power loads. Regression analysis confirmed that thermal conductivity and flow rate were key predictors of cooling performance (R²  >  0.85). Although these technologies are promising, integration remains constrained by high material costs and long-term reliability concerns, such as mechanical degradation and fatigue. This work offers a novel contribution by comparatively analyzing heat transfer mechanisms and material innovations across multiple validation platforms. This study provides data-driven insights to guide cost-effective and scalable thermal solutions for next-generation EV platforms.

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