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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 4 Documents
 1 
Search results for , issue "in press papers" : 4 Documents clear
Measurement of Motor Vehicle Emissions Based on Low-cost Sensors Michelle Kurniawan; Sopaheluwakan Alesandro Ardiles; Andre Suwardana Adiwidya; Annisa Zahwatul Ummi; Maulana Fauzan Athalla Halinda; Indah Cikal Al Gyfary Okthaviany; Deni Ali Marwan Gajah; Putri Naila Alyana Hidayat; Irvin Judah Lalintia; Prichel Adisatya Kampong; Rahmat Awaludin Salam; Indra Chandra
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.v10i2.6002

Abstract

One of the problems that occur in Indonesia is air pollution caused by the high intensity of citizen activity, especially most of which still use fossil-fueled motor vehicles to carry out their activities. The fossil fuel that is not burned perfectly can cause gas and particle pollution to form which can cause its amount to increase in the air and affect the environment. Therefore, measurement of motor vehicle emission tests based on low-cost sensors is conducted with CO, CO2, NO2, PM2.5, temperature, and humidity as its parameters. The usage of flow rate is addressed to the flow of the emission gas, which will be measured from an exhaust of a motor vehicle into the testing chamber and forwarded into the air for disposal. The flow rate used in this test ranges from 12-15 lpm. For that reason, the author performed the test for 10 minutes and got the average results of the measured parameters. The results are 1200 ppm for CO, 140000 ppm for CO2, and 80 °C for temperature (the show results are the average of the test results). The results shown are due to the small range of the low-cost sensor resulting in a huge difference.
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.
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.

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