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Vita Lystianingrum
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JAREE (Journal on Advanced Research in Electrical Engineering)
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : -     EISSN : 25796216     DOI : https://doi.org/10.12962/j25796216.v4.i2.116
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
JAREE is an Open Access Journal published by the Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya – Indonesia. Published twice a year every April and October, JAREE welcomes research papers with topics including power and energy systems, telecommunications and signal processing, electronics, biomedical engineering, control systems engineering, as well as computing and information technology.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 187 Documents
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The Selection of Energy Storage in the Southern Sulawesi Electricity System with AHP-TOPSIS Method Peri Indriyanto; Vita Lystianingrum
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 9 No. 2 (2025): July
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v9i2.452

Abstract

In the fourth quarter of 2023, a power deficit of up to 600 MW was observed in the Southern Sulawesi (Sulbagsel) electricity system due to the prolonged El Nino that affected hydropower plants. Based on the 2021-2030 RUPTL, the Sulbagsel system does not allow the addition of renewable energy (RES) plants, except with a battery firming scheme while the construction of fossil-based power plants is also limited by the government. One potential mitigation strategy for PLN (Perusahaan Listrik Negara) is to evaluate the potential application of Energy Storage Systems (ESS) in the Sulbagsel system. The purpose of this research is to evaluate the selection of ESS in the South Sulawesi electricity system through a combination of AHP (Analytical Hierarchy Process) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). The AHP-TOPSIS method is an optimal choice for this evaluation due to its capacity to address complex evaluation scenarios by considering a multitude of comprehensive criteria and conducting weighting analyses to ascertain accuracy and identify optimal solutions. The evaluation criteria encompass technical, economic, environmental, and social considerations, with a total of 15 subcriteria. The alternatives to be evaluated are PHES (Pumped Hydro Energy Storage), BESS (Battery Energy Storage System), and HESS (Hydrogen Energy Storage System). The results of the evaluation demonstrate that PHES is the optimal alternative, with a preference value of 0.68. BESS is ranked second with a preference value of 0.33, while HESS is ranked third with a preference value of 0.20.
Optimal Variable Speed Control of BLDC Diesel Generator to Enhance Fuel Efficiency Bakhtiar Sudibyo; Heri Suryoatmojo; Daniar Fahmi
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 9 No. 2 (2025): July
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v9i2.460

Abstract

The growing adoption of renewable energy technologies still faces challenges such as instability, intermittent, and limited energy storage capacity. Diesel engine generators, known for their stability and reliability, remain essential as primary or backup power sources, especially in remote areas. However, conventional diesel generators operating at constant speed are inefficient in fuel consumption and produce high emissions. This study investigates the implementation of a variable-speed diesel generator system using a BLDC (Brushless Direct Current) generator controlled by a fuzzy logic-based controller (FLC). The proposed system adjusts engine speed and the duty cycle of the converter to optimize fuel efficiency while maintaining voltage and frequency stability. Simulation results demonstrate that the system reduces fuel consumption by up to 7.6% (0.86 liters/hour) for a 100 kW generator. Additionally, the FLC effectively stabilizes voltage and frequency during load changes and finally enhancing overall system performance.
A Novel 350 MHz Capacitive Soil Moisture Sensor for Precision Agriculture Pablo Petrashin; Walter Lancioni; Juan Castagnola
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 9 No. 2 (2025): July
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v9i2.455

Abstract

This paper presents a novel soil moisture sensor system based on a Colpitts oscillator operating at 350 MHz. The sensor utilizes the variation in capacitance of a sensing capacitor formed by two electrodes inserted into the soil. As soil moisture changes, the dielectric constant of the soil-water mixture also changes, directly affecting the capacitance and thus the oscillation frequency of the Colpitts circuit. This frequency range (150-500 MHz) was specifically chosen to minimize the influence of soil salinity on measurements, as supported by previous research.The sensor design is simple, consisting of readily available and low-cost components such as capacitors, inductors, and only one RF transistor. This simplicity makes the sensor suitable for mass production using standard PCB fabrication techniques. Laboratory tests were conducted using a GW INSTEK GSP-827 spectrum analyzer and a Digital Electronics L/C Meter IIB to calibrate the sensor and validate its performance. The tests demonstrated a strong correlation between oscillation frequency, capacitance, and soil moisture, as evidenced by the data presented.Key advantages of the system include its simplicity, low cost, low energy consumption, and robustness against soil salinity, surpassing the performance of traditional resistive sensors in conductive soils. The sensor offers potential applications in automated irrigation systems and precision agriculture, enabling optimized water usage and improved crop management. Future research directions include linearizing the sensor's response to enhance measurement accuracy, particularly in soils with high conductivity, and developing biodegradable electrodes using materials like beeswax and soy mixtures, balsa wood, or polylactic acid (PLA) to enhance the sensor's sustainability and minimize its environmental impact
Integration of Liquid Organic Fertilizer Fermentor with Automated Hydroponic Fertilization Based on IoT Muhammad Fakhrudin Zukhri; Puti Yeni Aisyah; Yudi Wirawan; Tia Yohana Nainggolan; Firda Anandhita; Muhammad Ivan Hermawan
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 9 No. 2 (2025): July
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v9i2.493

Abstract

As urbanization continues to accelerate, particularly in cities like Surabaya, the availability of agricultural land has been steadily decreasing, making food security a growing concern. In response to these challenges, urban farming, particularly through hydroponic systems, has emerged as a promising solution to ensure sustainable food production in limited spaces. However, issues such as the high cost and limited availability of high-quality fertilizers, as well as the difficulty in maintaining a consistent farming schedule, have posed significant barriers. This study aims to address these challenges by integrating IoT-based systems for temperature and pH monitoring, aiming to enhance farming efficiency. The validation results for both the DS18B20 Temperature Sensor and the pH Sensor 4502-C demonstrate their high accuracy and reliability for environmental monitoring. The DS18B20 sensor showed minimal error, with 0.89% for increasing temperatures and 1.34% for decreasing temperatures, achieving 99.11% and 98.66% accuracy, respectively. These results confirm the sensor’s effectiveness in real-time temperature control applications, such as those used in hydroponics and fermentation systems. Similarly, the pH Sensor 4502-C exhibited remarkable performance, with 99% accuracy in the acidic buffer, 98.99% in the neutral buffer, and 99% in the basic buffer. The error rates were extremely low, at 0.002% for acidic and basic buffers, and 0.01% for the neutral buffer, reinforcing the sensor’s reliability for pH monitoring in controlled environments.
Markerless Facial Reconstruction Motion Capture Using Triangulation Method Muhammad Alwali; Sevito Fernanda Pambudi; Laras Suciningtyas; Eko Mulyanto Yuniarno
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 9 No. 2 (2025): July
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v9i2.456

Abstract

Motion capture is a popular research topic, with one of its main applications being human face reconstruction. The demand for converting 2D images into 3D reconstructions continues to increase, especially in facial reconstruction, where progress is made in improving the accuracy of facial position prediction. However, there is still a significant gap in developing facial reconstruction technologies that can consistently convert 2D to 3D data with high accuracy, especially in scenarios involving dynamic facial expressions, diverse facial angles, and complex environmental conditions. Therefore, an approach using the triangulation method for 3D face reconstruction in the real world was developed. In the experiments, two cameras were used to obtain two face landmark coordinates so that the triangulation method can be implemented for 3D face reconstruction. This research aims to develop a motion capture approach that is able to accurately and efficiently transform 2D data into 3D face models without the need for complex hardware. The main contribution of this research is the development of a machine learning-based markerless motion capture technique designed to improve the accuracy of face position prediction in 3D face reconstruction from 2D data in realistic environments. This method seeks to bridge the current technology gap by providing a more flexible and reliable solution, expanding the potential applications of motion capture in various fields without dependence on specialized hardware. The results of face reconstruction research using markerless motion capture and triangulation method show RMSE values of 3.560839 for eyes, 1.644749 for nose, and 4.054638 for lips.
Techno-economic Analysis of Large-scale Integration of Variable Renewable Energy Sources into Myanmar's National Grid with Interconnection and Energy Storage System Min Lwin Thein; Hla Aye Thar; Kyaw Thu Oo
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 10 No. 1 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.471

Abstract

Myanmar’s abundant hydropower, solar, and wind resources present significant opportunities for a sustainable energy transition. Hydropower, with a potential capacity of 19,567 MW, currently forms the backbone of the energy strategy, though its seasonal variability necessitates improved transmission networks and planning. Solar energy, with an annual potential of 51,973.8 TWh, is increasingly utilized for both utility-scale and off-grid applications, while wind energy, with a theoretical potential of 80 TWh annually, requires further exploration. This study employs the EnergyPLAN modeling tool to analyze the techno-economic feasibility of integrating variable renewable energy sources (VRES) into Myanmar’s grid by 2030, targeting 11% of electricity generation from solar and wind. Five scenarios are examined, highlighting the role of grid flexibility solutions such as cross-border interconnections and energy storage system (ESS). Without flexibility measures, Critical Excess Electricity Production (CEEP) could reach 7.69 TWh annually. Introducing interconnections reduces CEEP to 0.57 TWh, while the combined deployment of interconnections and ESS further minimizes CEEP to 0.41 TWh and increases Exportable Excess Electricity Production (EEEP) to 7.29 TWh. These results underscore the importance of strategic investments in ESS and regional interconnections to address VRES variability, enhance grid stability, and reduce curtailment. Such measures are crucial for optimizing Myanmar’s renewable energy potential, advancing its energy transition, and reinforcing its position in the ASEAN regional energy market.
IMPACT OF DYNAMIC PRICING WITH SMART METERS ON ENERGY CONSUMPTION AND GRID LOAD STABILITY IN ELECTRIC VEHICLE USAGE Fendy Herlambang; Rony Seto Wibowo; Adi Soeprijanto
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 10 No. 1 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.529

Abstract

The rapid adoption of electric vehicles (EVs) in Indonesia presents both opportunities and challenges for the national electricity grid. This study analyzes the impact of dynamic electricity pricing, enabled by smart meter technology, on energy consumption patterns and grid load stability among EV users. Using a quantitative descriptive approach, data were collected from 100 EV users charging their vehicles at smart meter-equipped stations. The study examined variations in electricity consumption and costs across peak, off-peak, and super off-peak periods and analyzed changes in grid load before and after dynamic pricing implementation. Results show that while electricity consumption volumes remain statistically similar across different charging times, the total electricity cost varies significantly due to dynamic tariff differences. EV users who shifted charging to lower-tariff off-peak hours benefited from reduced costs, demonstrating economic incentives to adapt consumption behavior. Furthermore, dynamic pricing contributed to a 10% reduction in peak electricity load and a more even distribution of grid demand during nighttime hours, enhancing grid stability and operational efficiency. In conclusion, dynamic pricing combined with smart metering is an effective approach to managing the growing electricity demands from EV adoption, improving cost efficiency for consumers and promoting a more resilient and sustainable.
Improved Surge Speed Adaptive LOS Guidance Law in Current Disturbance Environment for USV Muhammad Alifuddin Akbar; Mochammad Sahal; Abdullah Alkaff
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 10 No. 1 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.530

Abstract

This work proposes a method for calculate the desired surge velocity integrated with an Adaptive Line of Sight, called Improved Surge Speed Adaptive LOS (ISS-ALOS), to enhance the path-following performance of USVs. This method utilizes ALOS guiding law to enable the USV to maintain the desired path regardless of disturbances. The proposed ISS-ALOS uses the path's curvature, cross-track error, and cross-track error rate to calculate the desired surge velocity. The method is applied to USV following a curved path with several bends and a rectangle path with sharp turns, to demonstrate with high reliability that the proposed method can direct the USV along the specified path with minimum cross-track error. Comparison with the latest guidance law available in publication is also presented to show the superiority of the proposed method.
An An Active Balancing for Liquid Metal Battery Using Bidirectional Flyback Converter with Fuzzy Logic Control M Zainul Muttaqi Muttaqi; Feby Agung Pamuji
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 10 No. 1 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.531

Abstract

The use of renewable energy to replace fossil fuels in electricity generation systems has been increasing every year. Unlike fossil fuels, which can generate energy according to load demand, renewable energy sources produce power unpredictably. Therefore, batteries are needed for power shifting. Li-ion is the most commonly used battery, although it only has a life cycle of about 1500 - 4500 cycles. We need another type of battery as an alternative because if we only rely on Li-ion, it will cause battery waste, which is a problem for the environment. Liquid Metal Battery (LMB), according to the latest research, have a life cycle of up to 10,000 times, so they could eventually become a promising alternative. LMB is composed of modules with a combined configuration of parallel and series circuits and requires a management system, especially for the balancing process. Due to the large capacity of this battery type, bidirectional active balancing using a bidirectional flyback converter is an ideal alternative, as the process is fast and highly efficient. Fuzzy logic control can be used in this system because it does not require mathematical equations and is suitable for systems with multiple inputs and outputs.
An Implementation of the Convolutional Neural Network Algorithm for Detecting Crack in 150 kV Transmission Line Insulator Andhika Rizki Priambodo; Ronny Mardiyanto; Heri Suryoatmojo
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 10 No. 1 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.537

Abstract

High Voltage Overhead Transmission Lines require routine inspections to detect potential fault and ensure reliable operation. These inspections are conducted through manual tower climbing, known as Climb Up Inspection (CUI). With the advancement of technology, drones—Unmanned Aerial Vehicles (UAVs)—have increasingly been adopted as a more efficient alternative for conducting such inspections. While drone-based inspections significantly improve the speed of data acquisition, they often create a bottleneck at the stage of data analysis and image processing due to limited human resources and concurrent operational tasks. To address this issue, this study presents the development of an Artificial Intelligence (AI) based software application utilizing Deep Learning, specifically the Convolutional Neural Network (CNN) algorithm, to automate the classification of insulator conditions captured by drone imagery. The proposed application is designed to categorize insulators into two conditions: broken and normal. By automating the analysis process, this system is expected to enhance inspection efficiency, reduce maintenance response time, and support the creation of a more reliable and resilient power transmission network.

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