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Syafii
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Andalasian International Journal of Applied Science, Engineering, and Technology
Published by Universitas Andalas
ISSN : -     EISSN : 27970442     DOI : https://doi.org/10.25077/aijaset
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Mechanical Engineering
The Andalasian International Journal of Applied Science, Engineering, and Technology (AIJASET) is an international journal dedicated to the improvement and dissemination of knowledge on applied science, engineering and technologies including energy, environment, industrial, agriculture, civil, transportation, Information and communication and other related sciences and engineering. AIJASET follows new trends and advanced technology for practical applications of appropriate technology.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 221 Documents
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Analysis of Spun Pile Bearing Capacity Based on Spt Data Using Analytical and Software Modeling Approaches Dicky Hamdani Tampubolon; Tika Ermita Wulandari
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i2.299

Abstract

Foundations are crucial structural elements that support the entire building load. At the study site, soil conditions are variable and complex, consisting of clay and sand layers with non-uniform bearing capacity, making accurate foundation capacity assessment essential. This study compares the bearing capacity of spun piles using analytical calculation with the Luciano Decourt method, numerical modeling with GEO5 software, and field verification through PDA Tests. SPT values were used as input for both calculation methods. Results indicate that the analytical method predicts a bearing capacity of 202.41 tons, lower than the PDA Test result of 296.7 tons, while GEO5 modeling produces 361.48 tons, closer to field conditions. The difference arises because the analytical method relies on a simplified SPT-based calculation, whereas GEO5 processes soil data into more detailed parameters and computes soil response at each layer incrementally. These findings confirm that numerical modeling provides a more representative estimate of bearing capacity, while differences with analytical calculations remain acceptable in geotechnical practice. This study offers a reliable reference for designing spun pile foundations on complex soils, enhancing design accuracy and structural safety.
Engineering Design and Five Whys–Based Analysis for Jig Redesign in a New Auto Press Pin Machine Armijal Armijal; Muhammad Hamdan Fadhlani; Alfadhlani Alfadhlani; Prima Fithri; Reinny Patrisina
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i2.322

Abstract

Abstract— PT ABC is a major automotive manufacturer in Indonesia producing NR-series and hybrid engines. To address limitations of the manual press pin machine, an auto press pin machine was developed. However, operational issues emerged during the load–unload process due to instability of the jig path, leading to workpiece misalignment and reduced process reliability. This study aims to identify the root causes of the jig path instability and propose an engineering-based improvement. Root cause analysis was conducted using the five whys method based on field observations and discussions with operators and technicians. The analysis indicated that geometric incompatibility between the jig track and the cylinder block was the primary cause of instability. Based on these findings, the jig path was redesigned using computer-aided design (CAD) with SolidWorks, followed by motion study simulations to evaluate stability and potential interference. The redesigned jig path demonstrated improved positional stability and smoother workpiece movement during simulation. Although quantitative performance indicators were not evaluated, the proposed design provides a technical foundation for future performance-based assessments
Leveraging Virtual Desktop Infrastructure to Improve Workforce Productivity and Flexibility Ahmad Syafruddin Indrapriyatna; Muhammad Andrian Fadillah; Rahmat Budiarto; Ikhwan Arief
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i2.324

Abstract

Despite the widespread adoption of Virtual Desktop Infrastructure (VDI), empirical evidence on its organizational impact—particularly in developing-country contexts—remains limited, with prior studies largely focusing on technical performance rather than workforce outcomes. This study addresses this gap by evaluating the impact of VDI implementation on work efficiency and employee mobility in a real organizational setting. We provide empirical evidence from a real-world deployment that demonstrates how VDI contributes to measurable improvements in workforce performance in a resource-constrained environment. A mixed-method approach was employed, combining quantitative survey data and qualitative insights. User perceptions were measured before and after VDI implementation using a five-point Likert scale across key indicators, including system performance, accessibility, user satisfaction, flexibility, and workflow continuity. The results reveal a significant increase in the mean score from 14.75 to 21.67, indicating substantial improvements in system usability and access flexibility. The findings demonstrate that centralized desktop management reduces technical variability, enhances system consistency, and enables seamless remote access, thereby improving workflow continuity and supporting distributed work practices. This study provides one of the few empirical evaluations of VDI implementation in a real-world organizational setting within a developing-country context.
Integrating Traffic Density Analysis with Automated Violation Detection Through IoT Sensor Fussion Chukwu Augustine Chisom; Ezema Longinus Sunday; Mfonobong Eleazar Benson; Hilary Ugo Ezea
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i2.328

Abstract

Urban traffic congestion imposes heavy economic and safety burdens on cities, especially in developing countries where fixed-time signal systems are used. Conventional methods are static and exacerbating delays and infractions. This study designs and implements a low-cost intelligent traffic control system that uses density-based signal processing and dual infrared sensors to adapt green-phase durations dynamically. It integrates real-time IoT-enabled offender detection and capture to link control and enforcement. The system uses a PIC16F877A microcontroller with optical dual-sensor arrays to measure traffic density and a Wi-Fi camera to capture images of violators. The Dynamic Time Allocation Technology (DTAT) system uses rules to figure out how much "green time," or 4 to 90 seconds, should be assigned to four different density states. MPLAB IDE is used for firmware development in C++, along with Proteus for simulation and real-world testing of prototypes. The prototype correctly identifies traffic state using a spatial sensor separation of 6 car lengths. It also adjusts the timing proportionally, reducing the number of wasted phases when conditions are uneven. Interrupt-driven enforcement reliably captures and transmits red-phase violations without disrupting control, demonstrating integrated feasibility on resource-limited hardware. This is a flexible, cost-effective approach to managing traffic in resource-poor areas.
Machine Learning Application for Predicting Stroke Patient Length of Stay: A Gradient Boosting Approach Asmuliardi Muluk; Hilal Hamdi; Ibrahim Kucukkoc
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i2.336

Abstract

Hospital length of stay (LoS) prediction is critical for operational planning and resource allocation in stroke care. This study developed and validated machine learning models for predicting binary LoS (short <=7 days vs prolonged >7 days) at a Type A teaching hospital in Indonesia. Random Forest and Gradient Boosting algorithms were trained on 250 stroke patients admitted in 2023 and validated on an independent test set of 62 patients, using 42 clinical features. Gradient Boosting achieved 77.4% accuracy, 78.6% sensitivity, 77.8% specificity, and AUC-ROC of 0.849, outperforming Random Forest by 4.8 percentage points (p=0.008). Hyperparameter optimization via grid search improved baseline accuracy from 74.2% to 77.4%. Feature importance analysis identified primary stroke diagnosis (18.7%), serum AST (12.4%), consciousness level (9.8%), patient age (8.6%), and ICU admission (7.3%) as dominant predictors. Cross-validation showed minimal overfitting, confirming strong generalizability. The optimized model demonstrated high computational efficiency (8.6-second training, 1.9-millisecond prediction per patient), suitable for real-time clinical deployment. This study contributes a validated, locally-optimized tool for Indonesian healthcare, supporting operational decisions on bed allocation, rehabilitation planning, and staff scheduling, with broader applicability to similar lower-middle-income healthcare systems.
Proposed Integrated Assessment of RULA, REBA, and OWAS for Ergonomic Risk Evaluation and Workstation Improvement Purwanto Purwanto; Dzakiyah Widya Ningrum; Muhammad Naufal Hakim; Muhamad Tatmainul Qulub
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i2.337

Abstract

Manual work with non-ergonomic postures significantly contributes to musculoskeletal disorders (MSDs), affecting worker health and productivity. This study analyzed ergonomic risks at a production workstation using the Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), and Ovako Working Posture Analysis System (OWAS) methods. A descriptive quantitative approach was applied to seven operators. The results showed that 28.6% of operators were categorized as high risk, 28.6% as moderate risk, and 42.8% as low risk. High-risk postures were associated with upper arm flexion above 60°, trunk bending, non-neutral wrist positions, and manual material handling activities. OWAS analysis identified two operators in action class 3, indicating that corrective actions are needed soon. Ergonomic improvement simulations, including worktable height adjustment, material repositioning, and assistive device use, reduced RULA scores by 40–50%, REBA scores by 35–45%, and OWAS action class from 3 to 2. The study concludes that integrating RULA, REBA, and OWAS provides a comprehensive ergonomic risk assessment and supports effective interventions to reduce MSD risks, improve worker comfort, and enhance productivity.
A Comparative Study of Deep Learning Models for Image Classification: Simple MLP, Deep MLP, Basic CNN, LeNet CNN Baffoe Nicholas; Jephthah Kwame Lanor
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i2.343

Abstract

This study presents a comparative analysis of four deep learning architectures: Simple Multi-Layer Perceptron (MLP), Deep MLP, Basic Convolutional Neural Network (CNN), and LeNet Convolutional Neural Network (CNN). The models are evaluated on the MNIST handwritten digit dataset under identical experimental conditions using CPU-only hardware. Each model is assessed based on classification accuracy, training time, and number of trainable parameters. Experimental results demonstrate that convolutional architectures consistently outperform fully connected architectures in classification accuracy, with the Basic CNN achieving the highest validation accuracy of 99.15% and the LeNet CNN offering the best balance between performance and efficiency with only 61,706 trainable parameters. The findings confirm that architectural design has a greater influence on model performance than the number of layers or parameters alone. This study provides a comprehensive review of the relevant literature, detailed architectural descriptions, experimental methodology, and a thorough analysis of results to support the comparative conclusions.
Design and Performance Analysis of Dual-Axis Solar Tracking to Enhance PV Efficiency Using Mamdani Fuzzy Logic Method Seflahir Dinata; Ahmad Faisal Mohamad Ayob; Fariq Alamsyahid Al Ghiffari; Didik Krisyanto; Heranudin Heranudin; Sunardi Sunardi
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i2.344

Abstract

The efficiency of a solar panel (PV) depends on solar radiation. In general, the solar panels will produce maximum power with radiation of 1000 W/m2 at a temperature of 25 oC. The sun's relative position will affect the strength of solar radiation received by the PV. This work aims to implement Mamdani fuzzy logic to optimize sunlight detection by dual-axis solar tracking (DAST) movement. The designed solar tracker consists of a light sensor (light-dependent resistor/LDR) as the input, an Arduino Mega as the controller, and a linear actuator to drive the solar panels. The solar panels used are 100 Wp monocrystalline. Data collection was carried out for 10 hours with time intervals every 10 minutes, starting from 7.00 am to 17.00 pm. Efficiency optimization is reviewed by comparing the output voltage of solar panels using and without sunlight tracking. The test results show that the solar tracker shows significant performance in maximizing the capture of sunlight so that the PV gets optimum radiation. The energy generated by fixed and solar trackers is 0.63 kWh and 0.92 kWh, respectively. The solar tracker also result higher efficiency 4.17% more, it shows that the use of the proposed method can increase the PV efficiency.
Degradation of Thermally Aged Insulation Paper in Mixed Polyol Ester and Used Mineral Oil Abdul Rajab; Emilliano Emilliano; Darmawan Hidayat; Mohammad Taufik; Mohamad Kamarol Mohd Jamil; Hidayatul Fadli
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i2.350

Abstract

This study investigates the mechanical, chemical, and microstructural changes of thermally aged transformer insulation paper in polyol ester and its mixtures with residual used mineral oil. The mixture was intended to simulate retrofilling conditions where complete removal of aged mineral oil from transformers cannot be ideally achieved. The proportions of used mineral oil in the mixtures are 0, 2.5, 5.0, and 7.5% by volume. Kraft paper, cut into approximately 45 cm × 20 cm, was wrapped around 9 g of copper conductor, immersed in about 1000 ml of oil mixture to imitate real transformer conditions. Samples were aged in an oven at 140 °C for 14 and 28 days. The results demonstrate that the chemical and physical integrity of the insulation paper, with tensile strength, FTIR, and SEM analyses consistently indicating increasing cellulose degradation, oxidation, and fiber damage at higher used mineral oil concentrations and longer aging durations. In addition to assessing degradation, the study highlights that hydrolysis of polyol ester generates long-chain fatty acids capable of esterifying cellulose. This reaction contributes to improved paper insulation’s thermal stability by slowing depolymerization and limiting further fiber damage. Conversely, the presence of used mineral oil accelerates cellulose deterioration.
EEG-Based Cybersickness Classification Using Hjorth Parameters and Random Forest During 3D Gaming Exposure Alfi Zuhriya Khoirunnisaa; Misbah Misbah
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i2.351

Abstract

This study presents an efficient EEG-based framework for cybersickness classification utilizing Hjorth parameter features under a 3D immersive video game stimulus (Mirror Edge). Multimodal data acquisition was performed using a 14-channel Emotiv EEG system for objective measurement and the Simulator Sickness Questionnaire (SSQ) for subjective validation. The EEG signals were subjected to comprehensive preprocessing procedures, including band pass filtering and Independent Component Analysis (ICA) to eliminate artifacts. Then, using Discrete Wavelet Transform (DWT) to isolate theta,alpha, and beta bands. Hjorth parameters: activity, mobility, and complexity were subsequently extracted to capture the temporal dynamics of neural activity with low computational overhead. To mitigate feature redundancy and dimensionality, Correlation Feature Selection reduced the feature space from 126 to 9 salient features. Classification performance was evaluated using Random Forest, Support Vector Machine, and K-Nearest Neighbor. Experimental results indicate a consistent increase in SSQ scores across participants, with disorientation emerging as the predominant symptom. Random Forest achieved superior performance with an accuracy of 82%, outperforming K-NN (72.72%) and SVM (59.09%). Notably, feature reduction preserved Random Forest performance while enhancing alternative classifiers. These findings highlight the robustness and computational efficiency of the proposed approach, demonstrating its potential for real-time EEG-based cybersickness detection.

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