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JMECS (Journal of Measurements, Electronics, Communications, and Systems)
Published by Universitas Telkom
ISSN : 24777994     EISSN : 24777986     DOI : https://doi.org/10.25124/jmecs.v6i1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology
Journal of Measurements, Electronics, Communications, and Systems (JMECS) is a scientific open access journal featuring original works on communication, electronics, instrumentation, measurement, robotics, and security networking. The journal is managed by the School of Electrical Engineering and published by Telkom University. The target audience of JMECS are scientists and engineers engaged in research and development in the above-mentioned fields. JMECS publishes full papers and letters bi-annually in June and December with a high standard double blind review process. Review cycles are typically finished within twelve weeks by application of modern electronic communication facilities. All published articles are checked using ithenticate plagiarism checker software. The scopes include: ELECTRONICS (ELEC) Theory and Design of Circuits Biomedics COMMUNICATION SYSTEMS (COMS) Information Theory Source Coding Channel Coding Optical Communications Wireless Communications SIGNAL PROCESSING (SIGN) Signal and System Image Processing AUTOMATION AND ROBOTICS (AUTO) Industrial Automation Control Theory Control Systems INSTRUMENT AND MEASUREMENT (INST) Power systems Renewable energy Smart Building Sensors Acoustics MATERIAL AND DEVICES (MATE) Material for Electronics Nanomaterials Photonics NETWORKING AND SECURITY (NETW) Network Theory Communication Protocols Switching Internet of Things, ANTENNA AND MICROWAVE (ANTE) Antennas Propagations Nanosatellite Radar Remote Sensing Navigation ARTIFICIAL INTELLIGENCES (ARTI) Machine Learning Intelligent Transportation Systems
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 85 Documents
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Simulation of Updraft and Downdraft Gasification Using Computational Fluid Dynamics (CFD) for Production of Syngas from Chicken Manure Waste Amaliyah Rohsari Indah Utami; Anindya Nabila Salma; Daffa Rayhan Betha Muchtar; Neni Sintawardani; Suwandi
JMECS (Journal of Measurements, Electronics, Communications, and Systems) Vol. 12 No. 2 (2025): JMECS
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v12i2.8099

Abstract

The rapid industrialization of the poultry sector has led to significant environmental challenges, including nutrient pollution, odor generation, and greenhouse gas emissions from improper manure management. This study examines the potential of chicken manure waste gasification as a sustainable approach to renewable energy production, while simultaneously addressing waste disposal concerns. Computational Fluid Dynamics (CFD) simulations were conducted in ANSYS Fluent software version 2019 R2 under a student academic license provided by Telkom University, to investigate updraft and downdraft gasification processes under varying operational conditions, including airflow velocity and temperature. The simulation model demonstrated high accuracy in predicting syngas composition, with average errors of 0.1657% at 680°C and 0.0969% at 800°C, validating its reliability. The optimal gasifier dimensions are  30 cm diameter and 40 cm height) 16.5 cm diameter and 60 cm height for updraft and downdraft, respectively. These dimension are consistent with industry standards. The results indicates that airflow velocity significantly influenced syngas composition; moderate increases enhanced CO production in updraft configurations, while excessive airflow in downdraft setups reduced CO concentration due to overoxidation. Temperature optimization further improved syngas quality, with higher temperatures (800°C) increasing the concentrations of CO and H₂. The H₂/CO ratio remained stable under updraft conditions but exhibited more significant variability in downdraft setups due to differences in reaction kinetics and flow dynamics. These findings highlight the importance of precise control over operational parameters to optimize syngas yield and composition for energy applications. Future work should focus on refining simulation models, exploring diverse feedstocks, and enhancing process efficiency to advance sustainable waste-to-energy technologies.
Adaptive Control Optimization for Solar Energy Storage Systems Using Fuzzy Logic, Genetic Algorithms, and State of Charge Estimation Andicho Haryus Wirasapta; Tiara Deta Pamungkas
JMECS (Journal of Measurements, Electronics, Communications, and Systems) In Press Papers
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v13i1.10090

Abstract

The intermittent nature of solar energy results in a generation–load mismatch, posing a significant challenge to reliable power utilization. Battery Energy Storage Systems (BESS) play a crucial role in mitigating this issue. However, effective operation requires advanced control strategies. Conventional techniques, such as classical Maximum Power Point Tracking based on Constant Current/Constant Voltage, often struggle to cope with the nonlinear dynamics of PV–BESS systems, leading to reduced efficiency and accelerated battery degradation. This paper proposes a hybrid adaptive control strategy integrating fuzzy logic decision-making, Genetic Algorithm (GA) optimization, and Extended Kalman Filter (EKF)-based State of Charge (SoC) estimation. A comprehensive PV–BESS model is developed in the MATLAB/Simulink environment using real solar irradiance and realistic load profiles. Simulation results demonstrate an absolute improvement in energy efficiency of approximately 14.3%, a SoC estimation accuracy within ±5%, and an extension of battery lifetime by 18–25% compared to conventional control methods. The proposed approach offers a robust and computationally efficient solution for PV–BESS operation, making it suitable for future microgrid and renewable energy storage applications.
Analysis of Transformer Oil Degradation Using Dissolved Gas Analysis and Fuzzy-Based Diagnostic Assessment Taufik Husnaedi; Sinka Wilyanti; Arisa Putri
JMECS (Journal of Measurements, Electronics, Communications, and Systems) In Press Papers
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v13i1.10256

Abstract

This study aims to evaluate transformer insulating oil degradation and post-purification recovery by integrating conventional Dissolved Gas Analysis (DGA) interpretation methods with a fuzzy-logic-based diagnostic framework. The research was conducted as an in-depth case study on Transformer 1 at the Cipinang Gas Insulated Substation (GIS), where repeated DGA measurements indicated progressive thermal stress prior to maintenance intervention. Oil samples were collected at multiple observation points before and after purification and analyzed using established DGA techniques, including Total Dissolved Combustible Gas (TDCG), Roger’s Ratio, Doernenburg Ratio, and the Key Gas Method. These outputs were then incorporated into a fuzzy inference system (FIS) developed in MATLAB to generate a unified oil-condition index. The results show that before purification, elevated concentrations of C₂H₄, C₂H₆, CO, and increasing TDCG values consistently indicated incipient thermal faults. After purification, combustible gas levels and TDCG values declined significantly, shifting the transformer condition to a normal operating state. While conventional ratio-based methods occasionally produced borderline or ambiguous classifications, the fuzzy-logic framework successfully consolidated multiple diagnostic outputs into a single, consistent condition assessment. The study demonstrates that integrating DGA with fuzzy inference enhances diagnostic clarity, improves post-purification evaluation, and supports more reliable transformer maintenance decision-making.
The Performance Analysis of Hate Comments in Cyberbullying Cases Based on IndoBERT and Cendol Nancy Olivia Syahanifa; Kartika Dwi Maharani; Anggara Budiyanto; Miftah Huljannah; Suryo Adhi Wibowo; Koredianto Usman
JMECS (Journal of Measurements, Electronics, Communications, and Systems) Vol. 12 No. 2 (2025): JMECS
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v12i2.9116

Abstract

The rise of hate comments on social media, especially during politically sensitive periods such as Indonesia’s 2024 election has increased the urgency of automated cyberbullying detection. This study aims to evaluate and compare the performance of two Indonesian-language NLP models IndoBERT and Cendol in classifying hate speech on platform X (formerly Twitter). A total of 8,375 comments were collected and labeled into neutral, negative, and positive categories, with preprocessing steps including normalization, tokenization, and oversampling to balance class distribution. IndoBERT, trained on formal Indonesian corpora, achieved 90.7% accuracy and performed better on structured and formal texts. In contrast, Cendol, developed with informal social media data, scored 90.6% accuracy and showed superiority in identifying slang, sarcasm, and modified spellings. The findings highlight the complementary nature of both models. IndoBERT excels in recognizing policy-related or legal content, while Cendol is more effective in detecting casual hate speech. The study recommends ensemble learning strategies that integrate both models to improve content moderation systems in Indonesian digital platforms. These insights contribute to the development of more context-sensitive AI tools for hate speech detection in local languages. Keywords: hate speech; NLP; IndoBERT; Cendol
Streaming Telemetry-Based Network Microburst Detection with Queue Metric Analytics for Packet Loss Mitigation Muchamad Rusdan; Ade Rahmat Iskandar
JMECS (Journal of Measurements, Electronics, Communications, and Systems) In Press Papers
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v13i1.10203

Abstract

Network microbursts, defined as high-intensity traffic surges lasting for an extremely short duration 100–800µѕ, have become a major cause of hidden packet loss that degrades the performance of critical applications in modern data center infrastructure. Conventional monitoring methods based on SNMP (Simple Network Management Protocol) fail to detect this transient phenomenon due to inadequate polling intervals, creating a blind spot in network visibility. This research designs, implements, and evaluates a microburst detection framework that leverages streaming telemetry and multivariate analysis of queue metrics to overcome the limitations of existing systems. The study adopts the Design Science Research (DSR) approach in a Mininet emulation environment with Open vSwitch, utilizing an integrated pipeline of gNMI/gRPC, Prometheus, and a Python-based detection algorithm that combines dynamic thresholding and queue metric correlation analysis. Evaluation against 1.6 million microburst events revealed that the proposed framework achieved a detection accuracy of 96.8% with an equivalent F1-Score at a 10 ms sampling interval, dramatically outperforming SNMP, which failed to detect any events. Correlation analysis showed a strong relationship between queue depth and packet drop rate, confirming the effectiveness of queue metrics as predictive indicators. The multivariate algorithm successfully reduced the false positive rate by 63% (from 5.7% to 2.1%) compared to a static threshold approach, despite increasing CPU overhead by 8–19%. The results of the study demonstrate the effectiveness of streaming telemetry with queue metric analysis for real-time microburst detection, while also providing practical implementation guidelines in the form of optimal configurations at 10–30ms intervals for various deployment scenarios.

Page 9 of 9 | Total Record : 85

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