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Journal of Novel Engineering Science and Technology
Published by Indonesian Institute of Science and Technology Research
ISSN : 29618916     EISSN : 29618738     DOI : https://doi.org/10.56741/jnest.v1i02
Core Subject : Science, Social, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Environmental Science Mechanical Engineering
Journal of Novel Engineering Science and Technology is a multi-disciplinary international open-access journal dedicated to natural science, technology, and engineering, as well as its derived applications in various fields. JNEST publishes high-quality original research articles and reviews in all of the disciplines mentioned above. All papers submitted will go through a rapid peer-review process to ensure their quality. Submissions must contain original research and contributions to their field. The manuscript must adhere to the author’s guidelines and have never been published before. All accepted manuscripts will be indexed in DOAJ, EBSCO, and Google Scholar. The indexation in SINTA, Scopus, and WoS will be provided in the future to provide maximum exposure to the articles.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 73 Documents
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Multimodal Gait Analysis Using IMU and EMG Sensors with HMM Classification to Differentiate Obese and Normal Body Types Setiyadi, Suto; Muhammad Ridho Rosa; Nigel Bryan Tang; Muhammad Sabiq Al Muttaqin; Muhammad Rafi Haykal Gumelar
Journal of Novel Engineering Science and Technology Vol. 4 No. 03 (2025): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v4i03.1272

Abstract

Gait analysis is essential for diagnosing movement disorders and monitoring rehabilitation progress; conventional methods are often costly and complex. This study aims to differentiate gait characteristics between individuals with obesity and those with normal body composition using a multimodal approach that integrates Inertial Measurement Unit (IMU) and electromyography (EMG) sensors. Data were collected from ten male participants (five classified as obese and five with normal body composition). IMU sensors were used to measure acceleration, angular velocity, and step count, while EMG sensors recorded muscle activity from the tibialis anterior and gastrocnemius muscles. We developed a real-time acquisition using ESP32 microcontrollers and Bluetooth Low Energy (BLE), and gait phase classification was performed using the Hidden Markov Model (HMM). Using heel-mounted sensors, the average step detection error ranged from 2.5% to 3.6%. IMU signals from obese participants indicated a shift in dominant gait phase from Initial Contact during slow walking to Loading Response during fast walking, with relative errors up to 27%. In contrast, participants with normal body composition exhibited more diverse and accurate phase distributions. EMG-based analysis provided more precise segmentation (with error rates as low as 0.47%). It revealed distinct muscle activation patterns: gastrocnemius activity was dominant during the Midswing or Midstance phases, while tibialis anterior activity peaked during Initial Contact, Initial Swing, or Loading Response. These findings suggest body composition significantly affects gait stability, phase transitions, and muscle activation patterns. Future work should explore advanced machine learning algorithms such as Long Short-Term Memory (LSTM) or Convolutional Neural Networks (CNN), integrate pressure sensors, and validate the system in real-world environments to enhance accuracy and reliability.
Integration of ECDHE Curve25519, RSASSA-PSS, and AES-256 for Enhanced PrivateDH Key Exchange Protocol in End-to-End Communication Ardi Saputra; Ronsen Purba
Journal of Novel Engineering Science and Technology Vol. 4 No. 03 (2025): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v4i03.1275

Abstract

The growing demand for secure digital communication calls for cryptographic protocols that are not only efficient but also capable of ensuring message confidentiality, integrity, and authenticity. PrivateDH is one such protocol that combines Diffie-Hellman, RSA, and AES; however, it still exhibits key weaknesses, including the absence of user authentication and reliance on classical Diffie-Hellman algorithms, which are computationally intensive and do not support forward secrecy. This study proposes an enhanced version of the PrivateDH protocol by integrating ECDHE Curve25519 as a replacement for classic DH, and RSASSA-PSS as a robust digital signature mechanism for user authentication. The methodology involves implementing and testing the proposed protocol within a peer-to-peer communication scenario, with performance evaluations based on handshake duration, CPU and memory usage, as well as security assessments including digital signature validation and forward secrecy. The results demonstrate that the enhanced protocol effectively accelerates key exchange, maintains resource efficiency, and provides reliable user authentication. In conclusion, this protocol contributes meaningfully to the advancement of more secure and efficient end-to-end communication systems, aligning with the demands of modern digital environments.
A Multimodal Graph-Based Recommendation Architecture for Vendor Discovery in Context-Aware Event Planning Nuryani Mawar Putri; Irwansyah Saputra
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1050

Abstract

Context-aware vendor recommendation remains a critical challenge in dynamic event planning systems, particularly in multilingual and temporally sensitive markets such as Indonesia. This paper presents MAMRS (Multi-Agent Multimodal Recommendation System), a novel graph-based architecture that integrates semantic similarity, temporal availability, and user interaction history within a heterogeneous knowledge graph processed using Graph Attention Networks (GAT). The system uniquely combines CSR-aware attention mechanisms with a local LLM (Mistral-7B) to deliver explainable, sustainability-focused recommendations while preserving data privacy. MAMRS introduces three key innovations: (1) A GAT-based reasoning layer that dynamically weights vendor relevance using both structural relationships and corporate social responsibility (CSR) scores. (2) A fusion scoring engine that optimizes for semantic, temporal, behavioral, and sustainability constraints (achieving 93.4% CSR compliance in output, validated through grid search and rule-based scoring), (3) A locally deployed LLM that generates natural-language justifications with 18.7% higher BLEU scores, complemented by ROUGE-L (0.44) and METEOR (0.39), and validated through a small-scale Likert-scale user study (n=5). Evaluation on 250+ real user queries and 1,200+ vendor profiles from BuatEvent.id demonstrates that MAMRS achieves: (1) 21.3% improvement in top-3 accuracy over dense retrieval baselines (with p < 0.05, two-tailed t-test). (2) 1.6s average latency (1.9s p95) on local infrastructure. (3) 88.7% recall@5 in cold-start vendor scenarios, including new vendors with sparse metadata. This paragraph of the first footnote will contain the date on which you submitted your paper for review, which is populated by IEEE. It is IEEE style to display support information, including sponsor and financial support acknowledgments, here rather than in an acknowledgment section at the end of the article. The current implementation supports Indonesian and English queries, with future work planned for additional ASEAN languages and domains such as education and healthcare. These results position MAMRS as an effective solution for regulation-sensitive, multilingual event-planning platforms that require auditable, trustworthy, and scalable recommendation rationales.
Federated Quantum Machine Learning for Secure Multi-Party Genomic Data Analysis: A Hybrid Architecture with Differential Privacy David Jumpa Malem Sembiring; Sinek Mehuli Br Perangin Angin
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1087

Abstract

The exponential growth of genomic data has created opportunities for collaborative biomedical research while posing significant privacy and security challenges. Traditional centralized machine learning approaches risk data breaches and regulatory non-compliance. This study proposes a novel hybrid architecture combining Federated Quantum Machine Learning (FQML) with Differential Privacy (DP) for secure multi-party genomic data analysis. Our simulated framework enables institutions to collaboratively train quantum machine learning models on distributed genomic datasets without exposing sensitive data. Using variational quantum circuits within a federated learning framework, we implement privacy-preserving models across three simulated genomic data silos. Gaussian noise injection via DP mechanisms during gradient updates ensures formal privacy guarantees (ε = 0.85). Experimental results on synthetic datasets demonstrate that FQML achieves 93.4% predictive accuracy in genome-wide association studies simulation, outperforming classical federated models in convergence speed by 27% and reducing communication overhead by 19%. These findings suggest FQML with DP provides a promising foundation for future secure genomic collaboration, though real-world validation remains necessary.
IoT-Based Real-Time River Monitoring and Early Flood Warning Using ESP32 and HC-SR04 Asri Mulyani; Dede Kurniadi; Rizki Esa Saputra
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1252

Abstract

Floods remain one of the most frequent and destructive natural disasters in Indonesia, primarily caused by rising river water levels. This study presents a prototype of an IoT-based river water level monitoring system using an HC-SR04 ultrasonic sensor, an ESP32 microcontroller, and the Blynk platform for real-time monitoring and alerting. The system classifies water levels into three categories: safe, alert, and danger, triggering local alarms (buzzers) and smartphone notifications accordingly. Evaluation results show a high sensor accuracy of up to 99.7%, with a notification delay averaging 1.2 seconds and a buzzer response time of less than 0.5 seconds. The system has been tested through black box testing and field simulations and has shown reliable performance for early flood warnings. These findings demonstrate the system’s potential in enhancing flood preparedness, particularly in vulnerable communities. Future enhancements include deploying waterproof sensors and integrating additional alert platforms such as SMS or sirens.
Flood Inundation Mapping Based on Land Use and Land Cover Change in Jatiroto Watershed, Lumajang, Indonesia Nabilla Syafa; Agus Suharyanto; Indradi Wijatmiko
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1279

Abstract

Floods are one of the most frequent natural disasters and cause considerable economic, infrastructure, and social losses, especially in areas experiencing land use/land cover change (LULC). In this study, we use GeoHECRAS and GIS to model the flood risk due to historic land use changes in the Jatiroto watershed (DAS Jatiroto) in Lumajang (East Java, Indonesia). The study evaluates the consequences of land-cover conversion between 1995 and 2023 on flood processes, including the variations of flood extent, depth, and duration. Rainfall, land use, soil types, and topography data were used to construct the flood prediction model, and the model was validated with historical flooding events. The findings indicate that the flood area is found to have increased substantially from 12.22% in 1995 to 15.66% in 2023. Moreover, flood depth was also raised, particularly in areas with urban and agricultural expansion.  The research emphasizes that land-use changes play a significant role in raising the flood risk and that GeoHECRAS can simulate the flood incidences. These results can be useful for the optimization of flood disaster management and prevention in the rapidly urbanizing areas. More robust flood risk management strategies could be developed if the effects of climate change were taken into account in future studies on flood risk modeling simulations.
Effect of Land Use and Land Cover Change on Inundation and Flood Loss Value in Lumajang District Regina Salsabila Athallah; Agus Suharyanto; Indradi Wijatmiko
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1281

Abstract

This study examines the relationship between land cover change and the extent of inundation and the value of economic losses due to flooding in Lumajang district in 1995, 2014, and 2023. Using a spatial approach based on satellite imagery and hydraulic simulation, as well as the ECLAC method in calculating losses, this study shows that the loss of natural land cover, such as forests and wetlands, as well as the expansion of residential areas, contributed significantly to the increase in inundation intensity and economic losses. These findings are important for spatial planning that is adaptive to hydrometeorological disaster risks. In addition, this research also supports the implementation of mitigation policies based on spatial and hydrological data that are appropriate to the conditions of the Jatiroto watershed.
Electric Motor Control: Innovations in Brushless DC Motors for Electric Vehicles Isaac John Ibanga; Philip Sunday; Asuquo Mfonobong Rivenus
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1324

Abstract

This study, titled Electric Motor Control: Innovations in Brushless DC Motors for Electric Vehicles, investigates advanced control technologies that enhance the performance, energy efficiency, and reliability of Brushless DC (BLDC) motors in electric vehicle (EV) applications. Employing a mixed-methods approach, the research integrates theoretical analysis, computational modeling, experimental validation, and real-world case studies. A comprehensive literature review from IEEE Xplore, ScienceDirect, SAE International, and industry reports forms the foundation, focusing on BLDC motor topologies, control strategies such as Field-Oriented Control (FOC) and Direct Torque Control (DTC), sensorless techniques, and emerging power electronics technologies including silicon carbide (SiC) and gallium nitride (GaN) devices. MATLAB/Simulink simulations are used to develop and test control algorithms, while electromagnetic and thermal analyses are conducted using ANSYS Maxwell and FLUX. Prototype validation involves hardware testing of a 1–10 kW BLDC motor system equipped with a DSP/FPGA controller and a SiC-based inverter under variable loads applied by a dynamometer. Key performance parameters, including torque ripple, energy efficiency, and thermal dissipation, are experimentally measured and statistically analyzed. The study also evaluates commercial EV systems, such as Tesla’s permanent magnet synchronous motors and Nissan Leaf’s BLDC implementations, to draw practical insights. Findings highlight that integrating advanced control methods, sensorless strategies, and high-performance power electronics significantly improves EV driving range, smoothness, and system durability. The results provide actionable insights for optimizing BLDC motor designs, addressing critical challenges in EV development, and supporting future innovations in sustainable transportation.
Optimizing Rice Planting Schedules Based on Rainfall Prediction Using a BiLSTM Network Fandi Presly Simamora; Khairul Hawani Rambe; Sophya Hadini Marpaung
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1340

Abstract

This study addresses the critical challenge of optimizing rice planting schedules in Indonesia, where unpredictable rainfall threatens national and regional food security. To tackle this issue, a Bidirectional Long Short-Term Memory (BiLSTM) network is proposed to accurately predict rainfall patterns, with a specific focus on Deli Serdang Regency in North Sumatra. Utilizing a comprehensive weather dataset from 2013 to 2022 sourced from BMKG, a feature selection process was conducted to identify the 10 most influential features for rainfall. The BiLSTM model was then developed through several experimental scenarios, varying the data duration and architectural complexity. The best-performing model, achieved in a scenario using a double BiLSTM architecture and 10 years of data, yielded a Mean Absolute Error (MAE) of 11.2382 mm and a Root Mean Squared Error (RMSE) of 19.5650 mm. The resulting predictive capability provides a data-driven framework for optimizing planting schedules. Crucially, the study also reveals the limitations of current planting criteria, which can be misleading in regions prone to intense, short-duration rainfall, highlighting the need for more adaptive, region-specific guidelines. This work contributes to mitigating crop failure risks, enhancing crop resilience, and ensuring long-term regional food security.
Multi-Response Optimization of 1 Kg Mass Artifact and Gravimetry as A Control Process in Legal Metrology Mass Laboratories Using the Taguchi Method and Response Surface Methodology Saufik Luthfianto; Sandi Satria; Irfan Santosa
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1376

Abstract

Artifacts are instruments used as reference standards or objects agreed upon at the national level. The reference standard must have a degree of accuracy based on measurements conducted by competent personnel. Another standard involves laboratory support facilities, including proper temperature, humidity, and lighting conditions. The purpose of applying standards is to minimize measurement errors and uncertainties. Therefore, standards in Legal Metrology ensure that all measuring instruments are traceable in terms of accuracy, both nationally and internationally. However, if the intercomparison results of a region fall outside the upper and lower limits in terms of artifact output, it is considered a failure, requiring room temperature standardization based on the optimal value of artifact actualization. The research method used is the Taguchi method combined with response surface methodology. This method is one of the mathematical approaches to quality control and is applied from the earliest stage, namely the design phase. The orthogonal array used is L8(24). The response measured is the actualization of a 1-kilogram special accuracy class weight artifact and gravimetry. The controlled and varied independent variables include AC temperature, actualization duration, lighting intensity, and the number of operators in the laboratory. Currently, the optimization process of laboratory conditions is still performed manually by laboratory operators, making it difficult to control the independent variables. Therefore, this study will implement the Internet of Things (IoT) to assist operators in setting independent variables for laboratory conditions. The research results show that the optimal conditions include an AC temperature of 16°C, an actualization duration of 15 minutes, 50% lighting intensity, and 1 operator in the laboratory.

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