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Jurnal Pendidikan Vokasional Teknik Elektronika (JVoTE)
Published by Universitas Negeri Jakarta
ISSN : -     EISSN : 26227029     DOI : -
Core Subject : Education, Engineering,
Control & Systems Engineering Electrical & Electronics Engineering Other
The Jurnal Pendidikan Vokasional Teknik Elektronika (JVoTE) is dedicated to all electronics engineering practitioners. JVoTE covers topics related to: 1. Teaching and Learning in TVET 2. Evaluation, Assessment, and Certification in TVET 3. Human Resources Management in TVET 4. Vocational Resources in TVET 5. Contemporary Issues in TVET: Policy in TVET
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 84 Documents
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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.
Sentiment Analysis of JKN Mobile Application in Health Technology Using Naive Bayes, Gradient Boosting, and AdaBoost Deni Utama; Rauhil Fahmi; Muhammad Ridho Kurniawan Pratama
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.v8i2.72073

Abstract

The increasing adoption of mobile health services highlights the need to evaluate user experience through feedback. The JKN Mobile application, developed by BPJS Kesehatan (Indonesia’s Social Security Agency for Health), has accumulated thousands of user reviews on the Google Play Store. These reviews provide an opportunity to gain insights into public sentiment regarding the app. This study conducts sentiment analysis using three machine learning algorithms—Naive Bayes, Gradient Boosting, and AdaBoost—to classify user opinions as positive, negative, or neutral. The reviews were preprocessed, vectorized using TFIDF, and evaluated using standard classification metrics. The results indicate that Naive Bayes achieves the highest classification performance, followed by AdaBoost and Gradient Boosting. These findings offer valuable guidance for developers and policymakers in improving the app’s usability and functionality.
Decomposition of the 605 kW Base-to-Peak Load Gap in a Hospital Building: Evidence of Occupancy Schedule Dominance over Cooling Load in Demand-Side Management Strategies Radimas Putra Muhammad Davi Labib; Bagus Tri Kuncoro; Ade Ayu Rahmawati; Vina Oktaviani; Agam Nizar Dwi Nur Fahmi; Made Dika Nugraha
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.v8i2.72074

Abstract

The reliability of a hospital's power supply hinges on the accurate determination of backup source capacity; however, standard design practices typically rely on static installed power values ​​without accounting for dynamic load fluctuations. This study characterizes the electrical load profile of a healthcare facility using a reference load profile derived from building energy simulations, comprising 8,760 hourly observations over a full year. The analysis procedure includes timestamp validation, power balance verification, end-use decomposition, descriptive statistics for weekday and weekend profiles, percentile-based clustering to distinguish between base load (5th percentile) and peak load (95th percentile) regimes, and vulnerability zone mapping via load duration curves. The recorded average load was 1,156.3 kW, with an annual peak of 1,576.2 kW and a load factor of 0.734. The dynamic range between the base load (880.2 kW) and peak load (1,484.9 kW) was 604.7 kW (38.4% of the peak load), while the non-sheddable vital load measured 695.7 kW. Decomposition of this range revealed a decoupling between energy share and peak contribution: cooling systems accounted for 43.3% of annual energy consumption but contributed only 10.4% to the dynamic range, whereas lighting and interior equipment collectively contributed 59.7%. The weekend load deficit was only 14.7% (177.4 kW) and was concentrated during active hours; the difference was less than 1% during nighttime hours but reached 26.9% at 16:00. Vulnerability mapping indicates that a 17.5% reduction in generator capacity is achievable through a maximum load shed of 276.2 kW, all of which falls within the category of deferrable loads. These findings shift demand-side management priorities toward scheduled loads rather than air conditioning systems.
Optimization of Notch Band Frequency in Communication Technology Using Different SRR Types in Vivaldi Antenna Agam Nizar Dwi Nur Fahmi; Nurhayati Nurhayati; Miftahur Rohman
JURNAL PENDIDIKAN VOKASIONAL TEKNIK ELEKTRONIKA (JVoTE) Vol. 8 No. 1 (2025): Vol 8 No 1 (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.v8i1.72334

Abstract

This research was based on M-Vivaldi (Modification Vivaldi) antenna design, which had a bandwidth of 2.4-9.1 GHz with a total antenna size of 150 x 75 mm. In the M-Vivaldi design, Split Ring Resonator (SRR) was incorporated to introduce a band-notch effect. The SRR came in various types, including Complementary Square-Split Ring Resonator (CS-SRR), Complementary Circular-Split Ring Resonator (CC-SRR), Half Circular-Split Ring Resonator (HC-SRR), and Flower-Split Ring Resonator (F-SRR). These different SRR types created distinct notch band, resulting in a combination of all SRR types into All Complementary-Split Ring Resonator (AC-SRR). The research focused on five notch band, namely 1.5-2.3 GHz, 2.4-2.8 GHz, 3.2-3.7 GHz, 3.2-4.8 GHz, and 6.4-7 GHz. These notch band effectively mitigated interference in Ground Penetrating Radar (GPR), Wireless Local Area Network (WLAN), Worldwide Interoperability for Microwave Access (WiMax), and Band Satellite communication.

Page 9 of 9 | Total Record : 84

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