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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 57 Documents
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MATLAB-Assisted Dynamic Analysis and Balancing Optimization in Railway Wheel Sets Kushawaha, Jeetender Singh
Journal of Novel Engineering Science and Technology Vol. 4 No. 01 (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.v4i01.851

Abstract

This study presents a MATLAB-assisted approach for dynamic analysis and optimization of balancing in railway wheel sets. By simulating multi-plane dynamic balancing equations, critical sources of imbalance were identified and mitigated. The methodology reduced counterbalancing weight consumption by 95.9%, achieving significant cost savings and operational efficiency in production trials. Factors such as wheel eccentricity and machine calibration were addressed comprehensively. This approach validates MATLAB as a cost-effective tool for industrial applications, with the provided code enabling adaptation in similar fields.
Physical Characteristics Analysis on Intelligent Reflecting Surface for High Speed Telecommunication Networks Su Win, Naw Aye Myat Su; Tun, Hla Myo; Win, Lei Lei Yin; Win, Thanda; Aye, Mya Mya; Win, Khin Kyu Kyu; Soe, Khaing Thandar; Pradhan, Devasis
Journal of Novel Engineering Science and Technology Vol. 4 No. 02 (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.v4i02.804

Abstract

The paper mainly focuses on the physical characteristics analysis of an intelligent reflecting surface for high-speed telecommunication networks. The research problem in this study are (i) To overcome the bottleneck, a novel transmission scheme, named hybrid reflection modulation (HRM) must be considered, exploiting both active and passive reflecting elements at the RIS and their combinations, which enables to convey information without using any radio frequency (RF) chains, (ii) In the HRM scheme, the active reflecting elements using additional power amplifiers can be able to amplify and reflect the incoming signal, while the remaining passive elements can reflect the signals with appropriate phase shifts, (iii) Based on this novel transmission model, we will observe an upper bound for the average bit error probability (ABEP), and derive achievable rate of the system using an information theoretic approach, and (iv) Moreover, comprehensive computer simulations could be performed to prove the superiority of the proposed HRM scheme over existing fully passive, fully active and reflection modulation (RM) systems. The research directions are as follows: (i) Implementing the Intelligent Reflecting Surfaces (IRS) and Hybrid Reflection Modulation Technologies for 6G Wireless Communication, (ii) Implementing the Intelligent Reflecting Surfaces (IRS) and Hybrid Reflection Modulation Technologies with physical layer security techniques, and (iii) Modelling the mathematical equation for optimization design of IRS system. There are two portions in this study. The first is designing the signal model in the IRS surface with specific physical parameters. The second one is an analysis of the capacity of point-to-point MIMO channels.  The analyses are conducted using by MATLAB language. The results confirm the performance specification of the IRS system for high-speed telecommunication applications.
LLMs Solution to Fake News, Disinformation, and Hoaxes: Llama 3 [70B]-based Hoax Detection and Counteraction System Jufriansah, Adi; Pramudya, Yudhiakto; Khusnani, Azmi; Malahina, Edwin Ariesto Umbu
Journal of Novel Engineering Science and Technology Vol. 4 No. 02 (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.v4i02.952

Abstract

In the digital age, hoaxes or false information are a significant challenge, as they can harm public comprehension, form inaccurate opinions, and endanger the health and safety of individuals. Artificial intelligence technology, particularly large language models (LLMs) like Llama 3, provides an innovative solution to these challenges. A sophisticated generative model with superior natural language processing capabilities, Llama 3 enables the effective detection and clarification of hoaxes. A dataset that is seven times larger than its antecedent, Llama 2, is utilized to train this model. The dataset has a token capacity of up to 128K and a context length of up to 8 K. By utilizing these capabilities, Llama 3 is capable of comprehending context, offering responses that are grounded in scientific data, and reducing response errors. Educational chatbots, interactive web platforms, and mobile applications that are based on Llama 3 can be implemented. This model effectively identifies and clarifies false information regarding cosmic rays that are purportedly hazardous through the presentation of pertinent scientific facts, as demonstrated by case studies. Llama 3's capabilities encompass its capacity to modify parameters to generate valid and pertinent responses. This renders it a critical instrument for bolstering community resilience to the dissemination of falsehoods, as well as digital literacy and awareness. Llama 3, which is open source, facilitates global collaboration in the development of a more secure and trustworthy information ecosystem.
Optimizing Brain Tumor Classification with Freeze-5 VGG16 and Dataset Fusion Vicky; Ronsen Purba
Journal of Novel Engineering Science and Technology Vol. 4 No. 02 (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.v4i02.999

Abstract

Magnetic resonance imaging (MRI)-based brain tumor classification is pivotal for early diagnosis and treatment planning. This study enhances the VGG16 pretrained model through freeze-5 fine-tuning (i.e., freezing the first five convolutional layers) and dataset fusion of two public repositories, yielding 5,023 training and 1,311 testing images. Preprocessing includes normalization and grayscale-to-RGB conversion, followed by moderate augmentation (rotation ≤ 15°, shift ≤ 0.1, zoom ≤ 0.1, brightness [0.9–1.1]). The base VGG16 (without top layers) is extended with GlobalAveragePooling2D, Dense (1024, ReLU), Dropout (0.5), and Dense (4, softmax) layers. The model is compiled with the Adam optimizer (lr=1e-4), EarlyStopping, and ReduceLROnPlateau callbacks. On the test set, the proposed configuration achieves peak accuracy of 99.16 % and macro-F1 of 0.99, outperforming prior hybrid approaches. An ablation study confirms that the freeze-5 strategy combined with data augmentation significantly boosts generalization without overfitting. These results underscore the critical role of optimal layer-freezing and dataset fusion in brain tumor classification. Future work will explore ensemble architecture and real-time clinical deployment.
Hybrid Ensemble Retrieval-Augmented Generation for Indonesian Legal Consultation with Keyword Boosting Suharyadi; Saputra, Irwansyah
Journal of Novel Engineering Science and Technology Vol. 4 No. 02 (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.v4i02.1042

Abstract

This study presents the design and evaluation of a fully local, hybrid ensemble Retrieval-Augmented Generation (RAG) system tailored for Indonesian legal consultation. By integrating sparse (BM25), dense (FAISS), and keyword-aware retrieval mechanisms, the system balances lexical, semantic, and domain-specific relevance to retrieve high-quality legal context. A curated dataset of 8,450 legal consultation articles was scraped from a trusted legal platform, cleaned through multi-stage pre-processing, and indexed for efficient retrieval. Retrieved documents are formatted into structured prompts and fed into locally hosted large language models (LLMs) using Ollama, allowing for complete offline operation. Experiments comparing five retrieval configurations TF-IDF, BM25, FAISS, ensemble BM25+FAISS, and ensemble with keyword boosting demonstrate that the hybrid ensemble with keyword boosting yields the most relevant and grounded answers. Both quantitative (retrieval score analysis) and qualitative (manual relevance rating) evaluations were performed, confirming the effectiveness of the ensemble strategy in improving answer quality. Additionally, the system achieves practical response times (12–20 seconds) on consumer-grade hardware without reliance on cloud services. This work makes a novel contribution by demonstrating that a hybrid ensemble retrieval framework, specifically tuned to the linguistic characteristics and retrieval challenges of Indonesian legal texts, can significantly enhance the performance of local RAG-based legal QA systems. Future directions include real-time indexing, fine-tuning of legal-domain LLMs, and extending the system to support other legal domains such as statutory law, regulations, and court rulings.
Channel Coding Analysis for High-Speed Telecommunication System Saw, Khin; Yin Win, Lei Lei; Myo Tun, Hla; Win, Thanda; Aye, Mya Mya; Kyu Kyu Win, Khin; Pradhan, Devasis
Journal of Novel Engineering Science and Technology Vol. 4 No. 03 (2025): Forthcoming Issue - 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.v4i03.820

Abstract

The paper mainly focuses on the channel coding design for high-speed telecommunication systems. The challenging issues in this study are based on (1) the growing demand for high data speed and an increase in subscribers, and (2) high-speed telecommunication networks allow users to avoid them due to better speed and more bandwidth. The objectives of this study are (1) to obtain a higher data rate, higher spectral efficiency, higher throughput, higher bandwidth, and higher energy efficiency at lower latency and (2) to detect/correct errors caused when information is transmitted through noisy channels. Therefore, high-speed telecommunication channel coding techniques will play a major role in achieving fast communication with minimum errors. The linear block and turbo codes are fundamental to analyzing the channel coding scheme for specific purposes. Theoretical concepts with numerical simulation are used to conduct the analyses. The simulation results on BER analyses confirm that the performance criteria could be met with real-world applications.
Implementation of Knowledge-Based Graph Neural Networks for Reasoning and Ranking Medical Entities from CORD-19 Texts Agus Rahmat Fadillah; Irwansyah Saputra
Journal of Novel Engineering Science and Technology Vol. 4 No. 03 (2025): Forthcoming Issue - 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.v4i02.1049

Abstract

The rapid growth of biomedical literature, espe- cially during the COVID-19 pandemic, has introduced new challenges in retrieving clinically relevant information using conventional search methods. This study proposes a novel, interpretable framework for biomedical information retrieval that integrates Named Entity Recognition (NER), knowledge graph construction, and Graph Neural Networks (GNNs) to support semantic reasoning and entity-level ranking. Unlike prior biomedical retrieval systems that operate at document level or perform link prediction over KGs, our framework introduces a novel task formulation contextual entity-level ranking powered by graph-based semantic reasoning. Leveraging the CORD-19 dataset, the system filters abstracts based on user queries, extracts domain-specific entities using SciSpacy, and constructs a semantic graph that captures co-occurrence relationships among medical concepts. A Graph Convolutional Network (GCN) is then employed to prop- agate relevance signals across the graph, enabling context- aware entity ranking. Experimental evaluations using queries such as ”pneumonia” and ”cough” demonstrate superior performance over traditional IR baselines like TF-IDF and BM25, achieving a Mean Average Precision (MAP) of 0.95 and Precision@3 of 1.00. The results confirm the system’s effectiveness in identifying semantically meaningful biomed- ical entities while offering enhanced transparency through graph-based visualizations. This work contributes a scalable and extensible approach to biomedical search and lays the foundation for intelligent literature exploration in medical research and clinical decision support.

Page 6 of 6 | Total Record : 57

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