Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Pendidikan Vokasional Teknik Elektronika (JVoTE)
Fernanda Sucitra Murti
Universitas Negeri Jakarta
Control & Systems Engineering Electrical & Electronics Engineering Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Two-Stage Adaptive Normalization Based on Exponential Moving Average for DC Offset and Variance Non-Stationarity Compensation in Real-Time Volcanic Seismic Signal Preconditioning Radimas Putra Muhammad Davi Labib; Ratri Andinisari; Mochammad Rifki Ulil Albaab; Ferty Lanisa Putri; Fernanda Sucitra Murti; Agam Nizar Dwi Nur Fahmi
JURNAL PENDIDIKAN VOKASIONAL TEKNIK ELEKTRONIKA (JVoTE) Vol. 8 No. 2 (2025): Vol 8 No 2 (2025): JURNAL PENDIDIKAN VOKASIONAL TEKNIK ELEKTRONIKA (JVoTE) Volu
Publisher : PENDIDIKAN VOKASIONAL TEKNIK ELEKTRONIKA FAKULTAS TEKNIK UNIVERSITAS NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/jvote.v8i2.71692

Abstract

Volcanic seismic signals output by digitizers as raw counts carry the accumulated imperfections of the entire acquisition chain—specifically, significant DC offset and large-scale variability—thereby violating the stationarity assumption underpinning downstream estimation blocks. This study proposes a two-stage adaptive normalization architecture based on the Exponential Moving Average (EMA) to serve as a statistical pre-conditioning block prior to the Kalman Filter. The first stage removes the DC offset using a recursive mean estimator—analytically equivalent to a first-order high-pass filter—while the second stage performs Z-Score standardization based on a running estimate of the standard deviation. Testing was conducted on a 386.48-second vertical-channel recording from the Leker (LKR) station at Mount Semeru (sampled at 100 Hz; 38,649 samples), characterized by a DC offset of −7,292.4 counts and a variance dynamic ratio of 10.42. With an adaptation constant of 0.005, the effective cutoff frequency of 0.0796 Hz lies approximately eighteen times below the 1.42 Hz fundamental frequency of the harmonic tremor, resulting in a passband attenuation of only −0.035 dB. Results demonstrate that the estimator locks onto the bias with a discrepancy of 7.72 counts (0.070% of the signal's standard deviation), reduces the dynamic ratio to 1.52, and yields an output where 99.989% of samples fall within the ±3 range. The Normalized Innovation Squared test confirms that the static covariance parameters of the Kalman Filter remain consistent with the normalized input, yielding a ratio of 1.17 compared to 17.10 for the raw input—an improvement factor of 14.6. The algorithm requires 8 bytes of state memory and 0.0104% processor utilization on an ARM Cortex-M4F at 48 MHz, whereas static normalization of the same recording requires 151.0 kB and cannot be implemented on that platform.
Machine Learning-Based State of Charge Estimation for the Energy Management System of a 3 GT Hybrid Battery-Solar Electric Boat Radimas Putra Muhammad Davi Labib; Fernanda Sucitra Murti; Ferty Lanisa Putri; Bagus Anggraini; Alfarid Hendro Yuwono; Parama Diptya Widayaka
JURNAL PENDIDIKAN VOKASIONAL TEKNIK ELEKTRONIKA (JVoTE) Vol. 8 No. 2 (2025): Vol 8 No 2 (2025): JURNAL PENDIDIKAN VOKASIONAL TEKNIK ELEKTRONIKA (JVoTE) Volu
Publisher : PENDIDIKAN VOKASIONAL TEKNIK ELEKTRONIKA FAKULTAS TEKNIK UNIVERSITAS NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/jvote.v8i2.72072

Abstract

Small-scale electric boats face highly restricted energy margins due to limited battery capacities, whereas a single fishing cycle typically lasts up to eight hours. In operational contexts, the state of charge (SoC) acts as the primary decision variable in the Energy Management System (EMS), making the overall system accuracy heavily dependent on the precision of SoC estimation. For LiFePO4 batteries, the highly flat open-circuit voltage (OCV) curve in the middle SoC range causes conventional estimation methods to lose their sensitivity. This study proposes an integrated simulation model for a 3 GT electric fishing boat, combining a 48 V 200 Ah LiFePO4 battery pack using an OCV-resistance-RC approach, an 860 Wp photovoltaic array, and a 10 kW Brushless DC (BLDC) motor. This modeling generates an eight-hour mission dataset at a one-minute resolution, comprising 60 voyage variations equivalent to 28,800 samples. Random Forest and Long Short-Term Memory (LSTM) algorithms are evaluated using data leakage-free feature engineering that relies solely on measured quantities from the battery management system, excluding cumulative current integration. Based on 5,052 test samples, the LSTM method achieves superior accuracy with an RMSE of 0.587 and an R2 of 0.9970, outperforming Random Forest, which yields an RMSE of 0.696 and an R2 of 0.9958. However, the LSTM computation demands approximately 33 times longer training time. Energy audit results demonstrate that the photovoltaic array supplies 29.4% of the total mission energy requirement, with 8 out of 60 voyages reaching the battery protection threshold. This confirms that accurate SoC estimation is an absolute operational requirement to ensure the electrical reliability of fishing boats.
Co-Authors Agam Nizar Dwi Nur Fahmi Alfarid Hendro Yuwono Bagus Anggraini Ferty Lanisa Putri Mochammad Rifki Ulil Albaab Parama Diptya Widayaka Radimas Putra Muhammad Davi Labib Ratri Andinisari