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Bulletin of Network Engineer and Informatics (BUFNETS)
Published by GWEX NET PUBLISHER
ISSN : 29874858     EISSN : 29868017     DOI : https://doi.org/10.59688/bufnets
Core Subject : Science,
Computer Science & IT
The Journal invites original articles and is not simultaneously submitted to another journal or conference. Scopes: Information Technology: Software Engineering, Knowledge and Data Mining, Multimedia Technologies, Mobile Computing, Parallel/Distributed Computing, Computer Graphics, Virtual Reality, Data and Cyber Security. Computer Network: Management and Protocol Network, Telecommunication Systems, Wireless Communications, Fuzzy Sensor and Network, Internet of Things, Data Communication and Networking.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 95 Documents
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Design and Evaluation of a 1.8 GHz MMIC RF Power Amplifier for LTE Band 3 R.A.Rahmadina Fitriah; Sopian Soim; Mohammad Fadhli
Bulletin of Network Engineer and Informatics Vol. 4 No. 2 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) October 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/sdy3j002

Abstract

Driven by the growing demand for high-performance wireless communications, the development of efficient radio frequency (RF) power amplifiers for LTE applications has become crucial. Operating at 1.8 GHz, LTE Band 3 is highly prevalent in modern cellular networks, necessitating RF front-end components with robust gain, efficiency, and linearity. This study presents the design and characterization of a 1.8 GHz Monolithic Microwave Integrated Circuit (MMIC) RF power amplifier tailored for LTE Band 3, implemented using the Keysight Advanced Design System (ADS). The design integrates a TQP7M9102 MMIC device with microstrip-based input and output matching networks implemented on an FR-4 substrate. Small-signal and large-signal behaviors were analyzed through S-parameter and Harmonic Balance simulations. The simulation results indicate that at 1.8 GHz, the amplifier achieves an input return loss (S11) of -14.2 dB, an output return loss (S22) of -12 dB, and a forward gain (S21) of 11.8 dB. Furthermore, it exhibits a peak Power Added Efficiency (PAE) of 52.3%, an output 1-dB compression point (OP1dB) of 19.2 dBm, and an output third-order intercept point (OIP3) of 20.1 dBm. These findings validate that the proposed MMIC RF power amplifier delivers sufficient gain, impedance matching, efficiency, and linearity for LTE Band 3 deployment, showcasing the utility of ADS-based simulations in modern RF design.
Implementation of IP Cameras and Computer Vision in a Smart Surveillance System for Smoker Identification in Higher Education Environments Nursiyanto Nursiyanto; Dona Yuliawati; Bayu Nugroho
Bulletin of Network Engineer and Informatics Vol. 4 No. 2 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) October 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/3n14t140

Abstract

Smoking is a serious health problem that increases the risk of chronic diseases and reduces the quality of life of the community. In the campus environment, smoke-free zone (SFZ) policies are often difficult to enforce due to limited supervision and low compliance among the academic community. To address this challenge, this study developed a smart surveillance system based on IP cameras and computer vision technology using the YOLO-pose algorithm. The research aim is to produce a system capable of automatically detecting smoking activity, thereby supporting the enforcement of SFZ policies in educational settings. The research methods include literature study, problem analysis, dataset collection through annotation using the Pose Annotator Tool, detection model training using YOLO11m-pose, and performance evaluation using standard metrics. The dataset was constructed by adding body keypoint annotations, including the characteristic hand-to-mouth gesture pattern associated with smoking activity. The model was trained for 300 epochs and tested using GPU and CPU hardware to measure inference speed. The results showed that the model achieved very high performance, with precision and recall values of 1.000 and mAP50 of 0.995. The system can operate in real-time at an average speed of 45–60 FPS on a mid-range GPU and continues to run on a CPU at 8–12 FPS. This study concludes that the integration of custom pose annotations with the YOLO-pose architecture can improve the effectiveness of smoking activity detection compared to static object-based approaches
Implementation of The Blake3 Algorithm for Detecting Changes and Ensuring The Integrity of Web-Based Digital Documents Putri Nabila; Aryanti Aryanti; Martinus Mujur Rose
Bulletin of Network Engineer and Informatics Vol. 4 No. 2 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) October 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/2hne3s53

Abstract

The increasing dependence on web-centric digital document management for archiving contracts, academic credentials, and administrative records has intensified anxieties regarding data manipulation and unauthorized alterations. Traditional cryptographic verification methods, including MD5 and SHA-1, are no longer viable due to demonstrated collision vulnerabilities. Meanwhile, more robust alternatives like SHA-256 and SHA-3 frequently encounter performance bottlenecks when processing large volumes of data in real-time web architectures. To address these limitations, this research integrates BLAKE3 a cutting-edge cryptographic hash function that pairs SHA-256-level security with superior throughput via a parallelized Merkle-tree design into a web-based platform dedicated to document integrity verification. Adopting a Research and Development (R&D) methodology framework alongside the Waterfall System Development Life Cycle (SDLC), the project progressed through requirements analysis, system architecture design, deployment, and evaluation phases. The prototype was built using a PHP/Node.js web stack backed by a relational database that logs the BLAKE3 hash of each uploaded asset, recalculating and cross-checking the signatures upon every retrieval query. System performance was assessed via black-box functionality assessments, avalanche effect trials, and hash execution time benchmarks contrasting BLAKE3 against MD5, SHA-256, and SHA-3. The empirical data demonstrates that the platform achieved a 100% success rate in identifying simulated file tampering. Furthermore, BLAKE3 outpaced SHA-256 by a factor of 3.7 to 8.1 depending on file dimensions, while yielding an optimal avalanche effect of 49.9%. These outcomes suggest that BLAKE3 serves as a highly scalable and dependable substitute for safeguarding digital assets within web information systems, particularly for platforms handling high-throughput document verification.
Design of an Edge-Intelligence Early-Warning System for Waste Odor Using Decision-Tree Classification-Rule Extraction on an ESP32 Microcontroller Agiska Ria Supriyatna; Imam Asrowardi; Eko Subyantoro; Dzakwan Akbar Perdana Wijaya; Naufaldi Favian Archi; Zahara Ummu Abdillah
Bulletin of Network Engineer and Informatics Vol. 4 No. 2 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) October 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/fd5dmk21

Abstract

Uncontrolled organic waste releases volatile gases such as ammonia, hydrogen sulfide, and assorted volatile organic compounds that signal active decomposition and create both nuisance and health risk. Conventional odor monitoring relies on human olfaction or on laboratory instruments that are subjective, intermittent, or too costly for continuous deployment. This study presents a low-cost early-warning system that performs odor classification entirely at the edge on an ESP32 microcontroller. A gas sensor array (electronic nose) comprising six metal-oxide MQ sensors (MQ2, MQ3, MQ4, MQ8, MQ9, and MQ135) was used to acquire 184 labelled samples evenly split between a normal class and an odor-warning class. A Decision Tree classifier was trained, and the resulting decision logic was distilled into a compact set of human-readable threshold rules suitable for execution on a constrained microcontroller. Principal component analysis confirmed that the two odor states form clearly separable clusters (the first two components explain 89.7% of the variance). The extracted rule set attained 100% accuracy, precision, recall, and F1-score on a held-out test set, matching heavier models (random forest, support-vector machine, k-nearest neighbours) while requiring only a few bytes of memory and a handful of comparison operations per inference. To probe real-world reliability, a Gaussian sensor-noise injection study and a single-sensor-failure study were conducted. A redundant three-sensor majority-vote rule preserved above 95% accuracy at a noise level of 50% of the feature standard deviation and remained near 100% even when any single sensor failed, whereas a single-sensor rule collapsed to 48.6% on failure of its governing sensor. The results demonstrate that edge intelligence based on extracted decision-tree rules offers an accurate, interpretable, fault-tolerant, and extremely lightweight solution for continuous on-site waste-odor early warning.
Improving the Performance of a Support Vector Machine by Using Features Extracted from MobileNetV2 for Identifying Coffee Plant Diseases in Pagar Alam Nurmaleni Nurmaleni; Febriansyah Febriansyah; Inka Rizky Padya; Desi Puspita
Bulletin of Network Engineer and Informatics Vol. 4 No. 2 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) October 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/d7tsrf02

Abstract

Delays and inaccuracies in the early detection of plant diseases contribute to the decline in coffee productivity in Pagar Alam City. This study aims to optimize the performance of the Support Vector Machine (SVM) by utilizing MobileNetV2 as a feature extractor to improve the accuracy of coffee leaf disease classification. The research method follows the CRISP-DM framework, which includes problem understanding, data preparation, image preprocessing, data augmentation, feature extraction using MobileNetV2, classification using SVM, and model evaluation. The dataset consists of three classes: healthy leaves, leaf spots, and leaf rust, with all images resized to 224×224 pixels before processing. Test results on 180 test data points showed that the SVM model approach using MobileNetV2 was able to achieve an accuracy of 95%, with a precision of 95%, a recall of 94%, and an F1-score of 94%—higher than that of conventional SVM, which achieved an accuracy of 84%. The research results indicate that feature extraction using MobileNetV2 enhances the SVM’s ability to distinguish the visual characteristics of coffee leaf diseases more accurately and efficiently. This approach has the potential to be implemented as a fast, lightweight, and real-time coffee plant disease detection system to support increased coffee productivity in Pagar Alam City.

Page 10 of 10 | Total Record : 95

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