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Journal of Applied Engineering and Technological Science (JAETS)
Published by Yayasan Pendidikan Riset dan Pengembangan Intelektual
ISSN : 27156087     EISSN : 27156079     DOI : -
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Journal of Applied Engineering and Technological Science (JAETS) is published by Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI), Pekanbaru, Indonesia. It is academic, online, open access, peer reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. Journal of Applied Engineering and Technological Science (JAETS) is published annually 2 times every June and Desember.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 405 Documents
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Lean-Driven Sustainable Engineering Enhanced by TRIZ: A Conceptual Approach to Waste Elimination in Manufacturing System Laboratory Uly Amrina; Hendi Herlambang; Galang Persada Nurani Hakim; Irkham Syifaul Qulub; Lutfi Saputro
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/z8fge021

Abstract

Manufacturing system laboratories are essential in engineering education, however, existing laboratory-scale equipment often exhibits long cycle times, high energy consumption, poor ergonomics, and limited integration of sustainability principles. Prior studies generally address lean manufacturing, TRIZ-based innovation, or sustainable engineering separately, leaving a gap in a unified framework tailored for laboratory environments. This study aims to develop an integrated lean-driven sustainable engineering framework enhanced by TRIZ to systematically eliminate waste in a manufacturing system laboratory. A conceptual–experimental approach was adopted by combining lean waste analysis, TRIZ-based technical contradiction resolution, and sustainable engineering principles to redesign a modular Heating–Vacuum–Trimming (HVT) system. The proposed system was evaluated through prototyping and laboratory testing. The results demonstrate a reduction in process cycle time of up to 33% and a decrease in electrical energy consumption of approximately 16% compared to conventional equipment. From a practical perspective, the framework enables the development of modular, ergonomic, and energy-efficient laboratory tools that improve operational efficiency. From a theoretical perspective, this study extends the integrated application of lean manufacturing, TRIZ, and sustainable engineering into a cohesive framework suitable for laboratory-scale manufacturing systems. The proposed approach offers transferability.
Spatio-Temporal Graph Neural Network Based on Nonlinear Time–Frequency Features for Mu-ERD Classification in Multi-Session EEG Motor Imagery Firman Aziz; Jeffry Jeffry; Syahrul Usman; Rahmat Fuadi Syam; Muhammad Nur Arafah; Nurul Fathanah Mustamin
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.8679

Abstract

Mu rhythm event-related desynchronization (ERD) is a key indicator of motor imagery activity based on EEG signals. However, accurate classification of ERD remains challenging due to the nonlinear nature of EEG signals and inter-session variability. This study proposes a motor imagery classification approach using a Spatio-Temporal Graph Neural Network (ST-GNN) model that leverages nonlinear time-frequency features extracted via Variational Mode Decomposition (VMD) and Synchrosqueezing Transform (SST). The dataset was collected from a single healthy subject across five separate sessions, each consisting of two conditions: relaxation and motor imagery. After preprocessing and segmentation, features were extracted and represented as spatio-temporal graphs to be processed by the ST-GNN. The model was evaluated using metrics such as accuracy, F1-score, AUC-ROC, and the Session Stability Index (SSI). The results show that the ST-GNN achieved an accuracy of 94.2%, F1-score of 94.1%, and AUC-ROC of 96.1%, along with high prediction stability across sessions. This performance outperformed baseline models including CNN, CSP+SVM, and STFT+MLP.These findings support the hypothesis that ERD is a distributed brain network phenomenon and demonstrate that the ST-GNN approach with VMD/SST-derived features is a promising strategy for developing adaptive and accurate BCI systems.
Enhancing Student Motivation in Programming Education Through N-EGM-Based Gamification: A Mobile Application Approach Ranty Deviana Siahaan; Boy Martahan Sitorus; Emely Angelica Lestari; Enrico Hezkiel Sirait
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.8721

Abstract

Programming education continues to face persistent challenges, including high cognitive load, abstract syntax complexity, and declining intrinsic motivation among students. Although gamification has been widely adopted to address these issues, existing frameworks such as MDA and Octalysis lack structured personalization and socialization mechanisms tailored specifically for programming learning contexts. This study proposes a mobile-based programming learning application designed using the Newton Enhanced Gamification Model (N-EGM) and empirically evaluates its effectiveness through the Hedonic Motivation System Adoption Model (HMSAM). The study involved 116 undergraduate Informatics students selected using purposive sampling. Data were collected using validated questionnaire instruments and analyzed through descriptive statistics, reliability testing, multiple regression analysis, multicollinearity diagnostics, and common method bias detection using SPSS. The findings indicate that gamification elements mapped through the N-EGM framework explain 99.1% of the variance in student motivation and 98.9% of the variance in engagement (p < 0.001). Leaderboard and Objective elements were the strongest predictors of motivation, while Economy and Quest significantly influenced immersion. Multicollinearity diagnostics confirmed acceptable VIF values (< 5), and Harman’s single-factor test indicated no critical common method bias. Theoretically, this study contributes by integrating a structured multi-layer gamification framework with a hedonic adoption model in a programming education context. Practically, it provides a systematic design blueprint for implementing adaptive and socially integrated gamification strategies in mobile STEM learning environments.
Design and Potential of a Hybrid Biogas Reactor with Solar Panels for Energy Conversion in Tropical Areas Nukhe Andri Silviana; Parluhutan Panjaitan; Ninny A Siregar; Nuril Mahda; Rana Fathinah Ananda; Yuana Delvika; Yudi Daeng Polewangi; Habib Satria; Indri Dayana
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.8977

Abstract

Improperly managed cow dung can pollute the environment, causing unpleasant odors and contaminating groundwater. Therefore, a portable biogas reactor with a floating digester model was designed to process the waste into biogas energy and organic fertilizer while reducing the impact of pollution. The design method used the Pahl and Beitz approach, starting from task clarification to design development, focusing on portability, efficiency, and ease of operation. The reactor was designed to be able to process up to 300 kg of waste per month and was equipped with solar panels as an alternative energy source to operate the heating agitator and sensors, making it suitable for use in areas with minimal electricity access. The system was tested based on the daily performance of photovoltaic PV from 7:00 am to 6:00 pm. Based on the results of observations, a typical pattern of the PV system with the highest performance occurred when the light intensity was maximum at 12:00 pm. This hybrid system and reactor has the potential to be a sustainable solution in livestock waste management and support environmentally friendly agricultural practices.
Deep Embedded Clustering for Indonesian Protein, Fat, and Energy Availability Data Zakha Maisat Eka Darmawan; Oktavia Citra Resmi Rachmawati; Ashafidz Fauzan Dianta; Kholid Fathoni; Rizky Yuniar Hakkun; Tri Budi Santoso; Kevin Ilham Apriandy
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.8996

Abstract

Understanding disparities in regional food availability is crucial for food security policies. Most previous studies on Indonesian food availability use conventional clustering methods. These methods operate directly on the feature space and may miss complex, non-linear relationships in nutritional data. This limitation highlights the need for advanced analytical approaches to uncover deeper patterns. This study analyzes patterns of provincial food availability in Indonesia using Deep Embedded Clustering (DEC). It uses per capita indicators of energy, fat, and protein from both plant and animal sources, as well as the 2023 Food Consumption Pattern (FCP) score. DEC integrates representation learning with clustering. This allows the model to capture latent structures and nonlinear relationships that traditional clustering cannot identify. The analysis began by comparing K-Means and Hierarchical Clustering using the silhouette score to generate pseudo-labels for the DEC model. Hierarchical Clustering with Ward linkage and Euclidean distance achieved the highest silhouette score (0.3958) and was used for pseudo-label generation. Two DEC configurations were implemented, showing improved clustering performance. These achieved silhouette scores of 0.7829 (DEC-1) and 0.6385 (DEC-2). The results reveal four distinct clusters of Indonesian provinces, each with different food availability characteristics. These range from balanced, nutrient-rich regions to provinces with more limited or specific nutritional patterns. The findings show that DEC can capture complex structures in nutritional data. It produces more meaningful clusters than conventional approaches. In practice, the identified clusters provide policymakers, nutrition experts, and the food industry with useful insights for region-specific strategies. These strategies can improve food security and nutritional balance. Theoretically, this study contributes to the use of deep learning-based clustering in food availability analysis. It is especially relevant in national food security research. Future research may extend this approach by integrating time-series data and spatial analysis. This will help understand the temporal and regional dynamics of food availability in Indonesia.
Effect of Polymer Modified Asphalt With Crumb Rubber on The AC-WC Wear Layer Against Rutting Uses Wheel Tracking Machine (WTM) Kinanti Wijaya; Sitorus Jeremia; Batubara Hamidun; Sebayang Nono
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9001

Abstract

Premature rutting remains one of the most critical failure mechanisms in flexible pavements, particularly in tropical regions where high temperatures and increasing axle loads accelerates permanent deformation. Although polymer modified asphalt has been widely investigated to mitigate integration between laboratory rutting performance and statistical prediction models. This study aims to evaluate the effect of crumb rubber (CR) modification on the rutting resistance of Asphalt concrete wearing course (AC-WC) mixtures and to quantify its influence using regression analysis. AC-WC mixtures were prepared with crumb rubber contents of 0%, 5%, 10%, 15% and 20% by weight of asphalt binder, following Indonesian Bina Marga specifications. Rutting performance was assessed using a Wheel Tracking Machine (WTM), while Marshall properties were used to determine optimum asphalt and CR contents. The results indicate that CR contents of 5% and 10% significantly enhance dynamic stability, with the 10% CR mixture exhibiting the highest rutting resistance (1162.7 passes/mm). regression analysis confirms a very strong relationship between CR content and dynamic stability (R2 = 0.979), indicating the dominant role of polymer modification in controlling permanent deformation. These findings demonstrate that crumb rubber improves asphalt elasticity and load distribution under repeated wheel loading the study provides practical implications for sustainable pavement design by promoting waste tire utilization while improving rutting performance in AC-WC layers. The novelty of this research was integrating WTM based rutting evaluation with statistical regression modeling to identify the optimal crumb rubber content for tropical pavement applications.
Efficient Road Surface Classification on Low-Cost Devices Using Vehicle Vibration Data Cong Ngo Van; Duc-Nghia Tran; Thu Bui Thi; Vu Duong Tung; Pham Quang Huy; Manh Tuyen Vi; Duc-Tan Tran
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/2afgj009

Abstract

During road traffic operations, pavement quality directly affects safety, vehicle operating costs, and pavement maintenance activities. Traditional inspection methods are often costly and time-consuming, and they cannot provide continuous data on pavement conditions. This study aims to develop an efficient road-surface classification system capable of real-time operation on low-cost hardware devices. The system uses vibration data collected from vehicles in motion to identify and classify road types with high accuracy and optimized performance. The proposed system employs inertial sensors mounted on vehicles to acquire accelerometer and gyroscope signals and then extracts time-domain statistical features from these signals. To address the main challenge of deploying an effective recognition model in a resource-constrained computing environment, the paper proposes a hybrid feature selection algorithm that combines filter and wrapper methods. This algorithm leverages the fast-processing speed of filter methods and the effective feature selection capability of wrapper methods. The selected feature set is then evaluated using three machine learning models: Random Forest (RF), Gradient Boosting (GBM), and XGBoost. The classification task focuses on three real-world pavement types: smooth asphalt (with less than 10 years of service), degraded asphalt (with more than 15 years of service), and cement concrete pavement. Experimental results show that the proposed feature selection algorithm and classification models achieve high classification performance and fast execution speed. The system attains accuracy higher than 0.95 while reducing computational cost. These findings confirm the feasibility of deploying road-surface classification systems on low-cost devices for real-time pavement monitoring and highlight the importance of appropriate feature selection in balancing system accuracy and performance.
Linking Mercury Contamination to Transport Dynamics in an Indonesian River: A Data-Driven Engineering Framework for ASGM-Impacted Watersheds Rindi Genesa Hatika; Indang Dewata; Ria Karno; Ika Daruwati; Fadhila Hidayah
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9121

Abstract

While Artisanal and Small-Scale Gold Mining (ASGM) severely contaminates watersheds with mercury (Hg), existing studies primarily diagnose pollution levels without identifying the underlying transport mechanisms or actionable engineering solutions. Addressing this gap, this study analyzes Hg concentrations, identifies physical transport vectors, and proposes a data-driven mitigation framework for the Kuantan River, Indonesia. A targeted spatial sampling (n=10) was conducted during the dry season (June 2025), with water samples analyzed using Cold Vapour Atomic Absorption Spectrometry (CVAAS). Results revealed gross contamination, with 100% of samples exceeding the World Health Organization (WHO) limit of 0.001 mg/L (ranging from 0.0027 to 0.0081 mg/L). The Heavy Metal Toxicity Load (HMTL) indicated critical toxicological risks (3.94–11.81). Crucially, Principal Component Analysis (PCA) identified Total Dissolved Solids (TDS) as the dominant spatial transport vector, demonstrating that Hg is predominantly particulate-bound rather than dissolved. To mitigate this, a hierarchical engineering framework is proposed, featuring source control (mercury-capturing retorts), pathway interruption (sedimentation basins to trap TDS), and receptor protection (point-of-use filtration). Although limited by a small sample size, this study extends foundational environmental engineering knowledge by linking statistical transport diagnostics to structural interventions, offering a replicable policy and watershed management blueprint for ASGM-impacted regions globally.
Utilization of Cocopeat, Empty Fruit Bunch, and Palm Kernel Shell as Renewable Energy Feedstock in Boiler Annisa Bhikuning; Supriyadi Supriyadi; Sandi Apriandi Setiawan; Yustika Agustin; Suhaila Binti Hussain
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/4tv82m49

Abstract

The increasing generation of biomass waste from coconut and palm oil industries presents both environmental challenges and opportunities for renewable energy utilization. This study evaluates the potential of cocopeat, empty fruit bunch (EFB), and palm kernel shell (PKS) as alternative fuels in boiler applications through fuel characterization, blending analysis, and thermochemical performance modeling. This research aims and objective to characterize the three wastes, determine the best composition mixture as boiler fuel, and estimate the potential exhaust gas emissions. This research uses laboratory testing methods, calculation of calorific value, and estimation of exhaust gas emissions (CO₂, SO₂, NO₂) using stoichiometric calculations and steam production modeling based on energy balance principles. The test was carried out by comparing cocopeat pellets (PECO); a mixture of 50% cocopeat and 50% EFB (BCO); PKS; and EFB. The results showed that 10% PECO, 30% EFB, 60% PKS has a high calorific value of 17.05 MJ/kg. Furthermore, NO2 emissions and steam production rate are decreased to 3.42% and 7.93% than 20% PECO, 40% EFB, 40% PKS. This is due to the high value of the coconut shell fraction (PKS), which produces a high calorific value. Furthermore, the cocopeat mixture, consisting of 10%BCO, 30%EFB, and 60%PKS, has low NO2 emissions and can produce high steam in boilers. This indicates that cocopeat can be used as a new fuel when mixed with EFB, thereby maximizing the utilization of coconut waste and reducing environmental impact.
Visual Detection of Oil Palm Maturity Leveraging Simple Evolving Connectionist System Al-Khowarizmi Al-Khowarizmi; Fatma Sari Hutagalung; Halim Maulana
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/kvqm6450

Abstract

Detecting the ripeness of oil palm fruit bunches is a crucial process in the palm oil industry to ensure the quality and quantity of oil extracted. Conventional methods still rely on subjective and inefficient manual observation. This study proposes a visual detection system using the Simple Evolving Connectionist System (SECoS) algorithm to identify the ripeness of oil palm bunches based on visual images. This model utilizes color, texture, and shape characteristics extracted from images and processed through an adaptive and evolving neural network structure. The results demonstrate that SECoS is capable of high detection accuracy and adapts to new data patterns. This system has the potential to be applied in precision agriculture practices. The model achieved an average accuracy of 91.3%, with the highest accuracy of 94% in the "Ripe" category in the final test based on 300 dataset. This demonstrates that parameter optimization is crucial in improving the model's ability to adapt to variations in oil palm bunch image data. Accuracy improvements were evident in both training and validation data. However, not all categories achieved optimal results, with accuracy for the "empty bunch" labels (89%) and "unripe" labels (88%) being relatively lower than for the other categories.

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