JOURNAL ZETROEM
type: channel
description: ================================================================================================== Journal Zetroem p-ISSN: 2656-081X | e-ISSN: 2656-081X Journal Zetroem is an international peer-reviewed open-access journal published every four months (three issues per year) by the Electrical Engineering Study Program, Universitas PGRI Banyuwangi. The journal provides a scientific platform for researchers, academics, scientists, and engineers worldwide to exchange ideas and disseminate theoretical and practical advancements in the field of electrical engineering and related technologies. Journal Zetroem welcomes high-quality original research articles in, but not limited to, the following areas: Electrical Power Systems Electronics and Embedded Systems Control Systems and Automation Internet of Things (IoT) Artificial Intelligence and Intelligent Systems Telecommunications Renewable Energy Systems Instrumentation and Sensors Robotics and Mechatronics Computer Engineering and Applications Informatics The journal aims to support the development of innovative technologies and interdisciplinary research contributing to industrial and societal advancement. Journal title Journal Zetroem Abbreviation Zetroem Frequency 3 issues per year (March, June, November) Type of Review Double Blind Review Print ISSN 2656-081X Online ISSN 2656-081X Editor in Chief Charis Fathul Hadi Publisher Electrical Engineering Study Program, Universitas PGRI Banyuwangi Citation Analysis Google Scholar |Dimensions | Garuda | Sinta Phone 085649231296 edmontonrvs.com METRO4D METRO4D SLOT DEWAN4D mega888 sbctoto https://srinivaspublication.com/published-books/ https://srinivaspublication.com/books/ scatter hitam aku4d hay4d asian4d asian4d hoki99 hoki 99 asian4d asian4d idrwin88 idrwin88 slot idrwin88 idrwin88 idrwin88 idrwin88 idrwin88 MYTOGEL MYTOGEL DAFTAR MYTOGEL SLOT BBTN4D Login BBTN4D kenangan4d mole4d mole4d login layarkaca21 sbctoto sbctoto sbctoto daftar yok4d yok4d daftar sbctoto sbctoto log…
publisher: Prodi Teknik Elektro Universitas PGRI Banyuwangi
language: en-US
publication name: JOURNAL ZETROEM
issn: 2656-081X
items: Real-Time Vehicle Detection and Counting Using YOLOv8 and ByteTrack Multi-Object Tracking on Surveillance Cameras
items: Virtual Private Network for Data and Information Communication Security:Testing on Zyxel USG 2200 Router
items: Prototyping IoT-Based Safety Telemetry System for Forest Firefighters
items: IoT-Enabled Smart Farming System Based on Sugeno Fuzzy Logic for Land Monitoring, Automated Irrigation, and Fertilization Recommendation
items: IoT and Machine Learning-Based Smart Watering Model for Water Optimization in Vegetable Gardens
items: Z-Score-Free Stunting Prediction from Basic Anthropometric Measurements Using Machine Learning
items: Design, Construction, and Performance Analysis of a Portable Solar Panel for Efficient Smartphone Charging
items: Chemical Composition Analysis and Heat Absorption Potential of Andesite Stone as Absorber Material in Thermoelectric Generators
items: Image-Based Spatial Shading Analysis for Power Performance Degradation in Photovoltaic Systems
items: Study of Smart Microgrid Technology on a Laboratory Scale Grid System
items: Analysis of the Achievement of Indonesia's Nationally Determined Contribution Target in 2030
items: Analysis of Overloading Impact on Harmonic Content and Loss of Life in Distribution Transformers
items: Transforming Investment Structures in Capital-Intensive Electricity Utilities: A Managed Service Approach for CapEx-to-OpEx Transformation
items: Comparing Machine Learning and Neuro-Fuzzy Models for Hydropower Prediction Under Seasonal Operating Conditions

Real-Time Vehicle Detection and Counting Using YOLOv8 and ByteTrack Multi-Object Tracking on Surveillance Cameras
type: item
description: Vehicle counting in tourist area parking facilities is typically performed manually, leading to counting errors and the inability to provide real-time capacity updates. This study proposes an automated real-time vehicle detection and counting system integrating YOLOv8s as the object detector and ByteTrack as the Multi-Object Tracking algorithm on surveillance camera footage to support parking capacity management. The dataset consists of 1,377 images across two vehicle classes, cars and motorcycles, prepared through a data-leakage-free pipeline with a 70/20/10 training-validation-test split followed by horizontal flip augmentation applied exclusively to the training subset. The model was trained for 50 epochs on an NVIDIA GeForce RTX 4050 GPU. A virtual counting line positioned at 70% of the frame height, combined with ByteTrack's persistent unique ID mechanism, enables precise vehicle entry and exit counting while preventing double counting. Evaluation results show that the YOLOv8s model achieved precision of 0.904, recall of 0.961, F1-score of 0.930, and mAP@0.5 of 0.969. Vehicle counting evaluation over a 30-minute test video yielded a counting accuracy of 96.875%, MAE of 2.25, and MAPE of 3.125%. The system operated at an average processing speed of 37.82 FPS, exceeding the real-time threshold of 25–30 FPS. These results indicate that the proposed system has the potential to serve as an alternative solution for automated parking capacity management at tourist area facilities.
creator: Filantropi Yusuf Aji Cahyono
creator: Raden Arief Setiawan
creator: Angger Abdul Razak
rights: Copyright (c) 2026 Filantropi Yusuf Aji Cahyono, Raden Arief Setiawan, Angger Abdul Razak https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 8
number: 2
starting page: 101
ending page: 108
doi: 10.36526/ztr.v8i2.8313

Virtual Private Network for Data and Information Communication Security:Testing on Zyxel USG 2200 Router
type: item
description: This study evaluates the efficacy and security robustness of a Remote Terminal Unit (RTU) data connection protected by a Virtual Private Network (VPN) deployed on the Zyxel USG 2200 router. The aim is to evaluate the effects of IPsec and SSL VPN implementations on network performance, particularly in terms of latency and packet loss, and to assess system resilience against cyberattacks through penetration testing. An experimental methodology was employed, involving the configuration of IPsec/SSL on the Zyxel router and VA modem, conducting packet transmission tests, and performing a security evaluation using Kali Linux tools (Hydra, NMAP, and Metasploit). The results indicate superior network performance, with latency values of 1.8 ms and 5.1 ms, and packet loss rates of 0.14% and 0.81%, signifying that the communication channel is exceptionally reliable and appropriate for real-time RTU applications. Port scanning revealed five open ports (21, 22, 53, 80, 443), but all penetration attempts were futile. Hydra faced failure due to a key-exchange mismatch; NMAP was unable to identify appropriate authentication keys despite numerous attempts, and Metasploit experienced connection issues during exploitation efforts. These findings validate that the established encryption and authentication systems offer sufficient defense against brute-force and fundamental exploitation threats. The research reveals that deploying IPsec and SSL VPN on the Zyxel USG 2200 enhances communication security while maintaining network performance. Future studies should incorporate long-term load testing and more sophisticated, multi-layered attack scenarios to achieve comprehensive security certification.
creator: Nurwahidah Jamal
creator: Ihsan Ihsan
creator: Kety Lulu Agustin
creator: Andi Yasir Amsal
creator: Farida Farida
creator: Mariatul Kiptiah
rights: Copyright (c) 2026 Nurwahidah Jamal, Ihsan Ihsan, Kety Lulu Agustin, Andi Yasir Amsal, Farida Farida, Mariatul Kiptiah https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 8
number: 2
starting page: 109
ending page: 116
doi: 10.36526/ztr.v8i2.7830

Prototyping IoT-Based Safety Telemetry System for Forest Firefighters
type: item
description: Forest fires significantly impact the environment, carbon emissions, and public health. Ensuring the safety of forest firefighters remains a critical challenge, as they operate in high-risk environments with limited communication infrastructure. This study develops an Internet of Things (IoT)-based safety telemetry system utilizing LoRa communication to monitor firefighters’ physiological and environmental conditions in real time. The system is built on an ESP32 microcontroller integrated with sensors for body temperature, heart rate, ambient temperature and humidity, CO and CO₂ concentrations, as well as a GPS module for location tracking. Sensor data are transmitted via a LoRa SX1278 module to a Raspberry Pi–based gateway and subsequently forwarded to a cloud server for visualization and monitoring. System performance was evaluated in forested and residential environments over distances of up to 1 km. The results demonstrated an average data transmission success rate of 97% with a latency of 1.47 seconds, indicating reliable LoRa communication in areas with limited cellular network coverage. Furthermore, the sensors achieved measurement errors below 3%, while the system operated continuously for 12.5 hours with a power consumption of only 0.68 W, demonstrating high accuracy, reliability, and energy efficiency. The system also provides an early warning mechanism for hazardous conditions encountered by firefighters. User evaluations revealed an 89% satisfaction rate, particularly regarding the effectiveness of the early warning feature and overall system usability. These findings demonstrate that the proposed LoRa-based IoT telemetry system can effectively enhance firefighter safety through accurate, reliable, and energy-efficient real-time monitoring.
creator: Eka Wahyudi
creator: Novi Indah Pradasari
creator: Ar-Razy Muhammad
creator: Darmanto
creator: Dedy Hidayat Kusuma
creator: Indra Pratiwi
creator: Herman
rights: Copyright (c) 2026 Eka Wahyudi; Novi Indah Pradasari; Ar-Razy Muhammad; Darmanto; Dedy Hidayat Kusuma (Author); Indra Pratiwi, Herman https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 8
number: 2
starting page: 117
ending page: 127
doi: 10.36526/ztr.v8i2.7533

IoT-Enabled Smart Farming System Based on Sugeno Fuzzy Logic for Land Monitoring, Automated Irrigation, and Fertilization Recommendation
type: item
description: Conventional farming systems are widely practiced without considering field conditions, including temperature, soil moisture, and soil pH, leading to inaccurate irrigation, fertilization, and crop selection. This study designed and implemented an Artificial Intelligence and Internet of Things-based smart farming system using the Sugeno Fuzzy Logic method to support real-time land monitoring, fertilizer recommendations, and automatic watering control. An experimental method was applied through literature review, hardware and software design, multisensor integration, implementation, testing, and evaluation. The system integrates an ESP32 controller, a DHT11 sensor, a soil moisture sensor, a soil pH sensor, an ultrasonic water-level sensor, an ACS758 current sensor, an Android-based Blynk application, and solar panels as the energy source. The results show that the system displayed temperature, humidity, soil pH, reservoir water status, fertilization status, and watering status in real time via the Android app. Sensor testing showed reliable performance, with the DHT11 producing average differences of 0.675°C for temperature and 1.75% for humidity compared with BMKG reference data. The soil moisture sensor identified dry, normal, and wet soil conditions with average values of 23.73%, 47.50%, and 80.23%, respectively. The pH sensor recorded average values between 6.48 and 6.72, while the ultrasonic sensor achieved an average reservoir-level error of 0.62%. Solar panel testing produced average voltages of 13.89 V on the multimeter and 13.61 V on the solar charge controller.
creator: Nur Yanti
creator: Syafruddin Syarif
creator: Ihsan
creator: Dwi Lesmideryati
creator: Totok Sulistyo
creator: Fathur Zaini Rachman
rights: Copyright (c) 2026 Nur Yanti, Syafruddin Syarif; Ihsan; Dwi Lesmideryati; Totok Sulistyo; Fathur Zaini Rachman (Author) https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 8
number: 2
starting page: 128
ending page: 139
doi: 10.36526/ztr.v8i2.7567

IoT and Machine Learning-Based Smart Watering Model for Water Optimization in Vegetable Gardens
type: item
description: Efficient water management has become increasingly important in modern agriculture due to growing increasing demand for finite water supplies and the necessity of promoting sustainable farming practices. Traditional time-based irrigation approaches often result in inefficient water use and limited adaptability to dynamic environmental conditions. This study presents the design and preliminary validation of an Internet of Things (IoT)- and Machine Learning (ML)-based smart irrigation framework at Technology Readiness Level (TRL) 3. The proposed framework integrates real-time sensor measurements, external weather information, and a Random Forest–based forecasting algorithm to determine crop water demand support adaptive irrigation scheduling. Experimental and simulation-based evaluations demonstrated that the Random Forest model achieved satisfactory predictive performance, with an RMSE of 0.19 L/m² and an MAE of 0.16 L/m². Furthermore, the proposed framework showed the potential to reduce irrigation water consumption by approximately 30% compared with conventional fixed-schedule irrigation while maintaining adequate water availability for crop growth. The integration of multi-source environmental data and predictive analytics enabled more accurate irrigation decisions, contributing to improved water-use efficiency and reduced irrigation-related operational costs. These findings highlight integrating connected sensing systems with machine learning techniques can facilitate evidence-based irrigation management while promoting long-term agricultural sustainability.
creator: Novi Indah Pradasari
creator: Eka Wahyudi
creator: Darmanto
creator: Ar-Razy Muhammad
creator: Rizqia Lestika Atimi
creator: Indra Pratiwi
rights: Copyright (c) 2026 Novi Indah Pradasari; Eka Wahyudi; Darmanto; Ar-Razy Muhammad; Rizqia Lestika Atimi (Author); Indra Pratiwi https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 8
number: 2
starting page: 140
ending page: 147
doi: 10.36526/ztr.v8i2.7532

Z-Score-Free Stunting Prediction from Basic Anthropometric Measurements Using Machine Learning
type: item
description: Early detection of stunting commonly relies on the WHO height-for-age Z-Score, which requires reference standards, calculation tools, and correct interpretation. These requirements can reduce screening efficiency in resource-limited community health services. This study proposes and validates a Z-Score-free machine learning framework for toddler stunting screening using only basic anthropometric measurements: age, sex, weight, and height. The scientific novelty lies in the deliberate exclusion of Z-Score-derived variables, weight-for-age status, weight-for-height status, and other nutritional-status indicators from the predictor set to prevent data leakage. Four yearly datasets from Jeneponto Regency, Indonesia, were combined, producing 40,071 labeled toddler records after removing one blank row. SMOTE was applied only to training data, and missing numeric values were handled through median imputation inside the training pipeline. Four algorithms were evaluated: XGBoost, Random Forest, LightGBM, and Logistic Regression. Under the valid Z-Score-free pipeline, Random Forest achieved the highest hold-out accuracy and F1-score (95.81% accuracy, 94.86% F1-score), while LightGBM achieved the highest ROC-AUC (99.28%) and recall (95.83%). XGBoost remained highly competitive (95.00% accuracy, 93.90% F1-score, and 99.12% ROC-AUC), but it did not outperform all competing classifiers on this dataset. Stratified 5-fold cross-validation confirmed the same overall pattern, with Random Forest producing the highest mean F1-score. SHAP analysis showed that height and age were the dominant contributors to the XGBoost decision process, aligning with the biological basis of stunting as impaired linear growth relative to age.
creator: Muhammad Resha
creator: Apriana Toding
rights: Copyright (c) 2026 Muhammad Resha, Apriana Toding https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 8
number: 2
starting page: 148
ending page: 154
doi: 10.36526/ztr.v8i2.8736

Design, Construction, and Performance Analysis of a Portable Solar Panel for Efficient Smartphone Charging
type: item
description: This study aims to design, construct, and analyze the performance and efficiency of a portable solar panel system specifically developed for smartphone battery charging as an environmentally friendly alternative energy solution. With the increasing reliance on portable electronic devices and the urgent need to reduce fossil fuel dependency, a highly mobile and efficient charging system is required for outdoor or off-grid situations. The research utilizes a quantitative experimental method, employing a 20 Watt-Peak monocrystalline solar panel, a 12V 7Ah storage battery, a Solar Charge Controller (SCC) to regulate energy flow safely, and a buck converter to adjust the output voltage for smartphones. The portable system was integrated into a compact suitcase design for high mobility. Performance testing was conducted over seven consecutive days from 08:00 to 16:00, measuring voltage, current, power output, charging time, and overall system efficiency. The results indicate that the solar panel's output power is highly dependent on sunlight intensity, reaching its maximum peak of up to 15.40 W at midday while maintaining a relatively stable voltage range. The system demonstrated the capability to gradually and safely charge a smartphone battery up to 80% capacity. The highest overall system charging efficiency recorded during the testing period was 55.5%. In conclusion, the designed 20 WP portable solar panel system exhibits stable performance and adequate efficiency, making it a highly practical, reliable, and sustainable off-grid power source for charging portable electronic devices.
creator: Tri Joko
creator: Erlina
creator: Doni Pratama
rights: Copyright (c) 2026 Tri Joko, Erlina, Doni Pratama https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-07-03
publication date: 2026-07-03
volume: 8
number: 2
starting page: 155
ending page: 169
doi: 10.36526/ztr.v8i2.8137

Chemical Composition Analysis and Heat Absorption Potential of Andesite Stone as Absorber Material in Thermoelectric Generators
type: item
description: The performance of thermoelectric generators is strongly influenced by heat absorption and thermal management on the hot side because these factors determine the temperature gradient and electrical energy output produced. Conventional ceramic materials commonly used as thermal media generally have high production costs and limited heat absorption capacity creating the need for alternative materials that are natural economical and sustainable. This study investigates andesite stone as a potential natural heat-absorbing material for thermoelectric generators through chemical composition analysis using the X-ray fluorescence method. The results show that the andesite sample is dominated by silica and alumina forming a stable aluminosilicate structure. Other identified compounds include iron oxide calcium oxide and magnesium oxide. Iron oxide contributes to increased radiation heat absorption through higher emissivity while calcium oxide and magnesium oxide improve crystal lattice stability and resistance to thermal shock. The very low loss on ignition value of 0.02% indicates minimal volatile compounds suggesting excellent thermal and chemical stability at high temperatures. Based on the relationship between composition and material properties the studied andesite stone is estimated to have moderate thermal conductivity and effective thermal buffering capability which are beneficial for maintaining a stable temperature gradient in thermoelectric generator systems. Overall these findings indicate that andesite stone has strong potential as a natural heat-absorbing or coating material in thermoelectric generator applications and can become a more economical and environmentally friendly alternative to conventional synthetic ceramic materials for future sustainable energy systems and broader industrial thermal applications worldwide.
creator: Septiannissa Azzahra
creator: Samsurizal
creator: Aryana Rachmad Sulistya
creator: Azli bin Yahya
rights: Copyright (c) 2026 Septiannissa Azzahra, Samsurizal, Aryana Rachmad Sulistya , Azli bin Yahya https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 8
number: 2
starting page: 170
ending page: 178
doi: 10.36526/ztr.v8i2.7998

Image-Based Spatial Shading Analysis for Power Performance Degradation in Photovoltaic Systems
type: item
description: Partial shading is one of the dominant causes of energy performance degradation in static photovoltaic (PV) systems, especially in tropical environments where dust accumulation and vegetation growth frequently occur. Conventional shading analysis in photovoltaic energy studies is commonly conducted using geometric simulation or irradiance‑based modelling without explicitly identifying physical shading objects on the PV surface. This paper proposes a computer vision‑based approach to quantitatively detect shading objects and statistically evaluate their impact on PV energy performance. A Python–OpenCV framework was developed to calculate pixel‑based shading area on a 10 Wp static PV module. Electrical parameters including voltage, current, and output power were experimentally measured under shading levels ranging from 10% to 100%. Statistical analyses consisting of Pearson correlation, linear regression, significance testing, and one‑way Analysis of Variance (ANOVA) were applied. The results show a very strong negative correlation between shading area and both current and power output (r = −0.98, p < 0.001). Linear regression indicates that shading area explains 96.1% of current variation (R² = 0.961), while ANOVA confirms statistically significant differences in energy output across shading levels (p < 0.01). These findings demonstrate that computer vision‑based shading quantification provides a statistically robust and energy‑oriented framework for analysing performance degradation in static photovoltaic systems.
creator: Andi Makkulau
creator: Ramlah
creator: Alex Fernandes
creator: Syaripudin Ardiansyah
rights: Copyright (c) 2026 Andi Makkulau, Ramlah, Alex Fernandes , Syaripudin Ardiansyah https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 8
number: 2
starting page: 179
ending page: 187
doi: 10.36526/ztr.v8i2.8306

Study of Smart Microgrid Technology on a Laboratory Scale Grid System
type: item
description: The utilization of renewable energy sources to meet electricity demand has become increasingly important. However, comparative studies on the technical and economic performance of on-grid and off-grid smart microgrid systems remain limited. This study aims to evaluate the configuration and performance of a smart microgrid system by comparing different operating configurations using HOMER software. The system was modeled by defining the electrical load, simulating the proposed configurations, and evaluating their technical and economic performance based on power output, Net Present Cost (NPC), and Cost of Energy (COE). The results show that the proposed grid-connected smart microgrid provides the best overall performance, with approximately 80% of the electrical energy demand supplied by the photovoltaic (PV) system and the remaining 20% provided by the PLN grid, demonstrating effective utilization of renewable energy resources. Economically, the proposed system achieves an NPC of Rp. 828,391,700 and a COE of Rp. 419.28, which are lower than those of the off-grid configuration (NPC of Rp. 2,664,679,000 and COE of Rp. 3,151.07) and the grid-only configuration (NPC of Rp. 906,104,800 and COE of Rp. 1,071.00). These findings indicate that integrating photovoltaic generation with the utility grid offers a more cost-effective and reliable solution while significantly increasing the contribution of renewable energy to the electricity supply. Therefore, the proposed smart microgrid configuration can serve as a practical reference for the design and economic evaluation of future grid-connected renewable energy systems.
creator: Heri Suyanto
creator: Erlina
rights: Copyright (c) 2026 Heri Suyanto, Erlina https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 8
number: 2
starting page: 188
ending page: 195
doi: 10.36526/ztr.v8i2.8309

Analysis of the Achievement of Indonesia's Nationally Determined Contribution Target in 2030
type: item
description: Global climate diplomacy through the Paris Agreement obligates every member state to update their greenhouse gas (GHG) emission reduction commitments through the Nationally Determined Contribution (NDC) document. As a form of national implementation, the Government of Indonesia sets an emission reduction target in 2030 of 31.89% for the CM1 or unconditional scenario in the Enhanced NDC (ENDC) document. Furthermore, PT PLN (Persero) is mandated to contribute directly to GHG emission reductions by 127 Million Tons of CO2 in 2030 based on the unconditional scenario (CM1). However, along with the progress of GHG emission reduction achievements which have dynamics, there is a potential gap in achieving the GHG emission reduction target. Thus, an analysis of the fulfillment of the GHG emission reduction target by PT PLN (Persero) in 2030 is needed, as well as mapping the achievement prognosis and the challenges per mitigation activity. The use of a descriptive-analytical method based on literature study and review of regulatory documents of the Government of Indonesia and PT PLN (Persero), as well as emission reduction calculations, is carried out based on mathematical formulas that refer to the BAU scenario calculation in the ENDC. The analysis is focused on the comparison of three main data trends: the initial target of the ENDC document for the 2021–2030 period, the historical realization of emission reductions for the 2021–2025 period, and the prognosis of emission reductions for the 2026–2030 period across four main activities of PLN.
creator: Musa Partahi Marbun
creator: Dandi Jiwo Lazuardi
rights: Copyright (c) 2026 Musa Partahi Marbun, Dandi Jiwo Lazuardi https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 8
number: 2
starting page: 196
ending page: 202
doi: 10.36526/ztr.v8i2.8372

Analysis of Overloading Impact on Harmonic Content and Loss of Life in Distribution Transformers
type: item
description: In the wake of Indonesia’s accelerating power infrastructure growth, maintaining distribution system integrity is essential to meet the increasing electricity demand from both residential and industrial sectors. This study investigates the operational performance of the PGCE distribution transformer at the Lemah Abang Customer Service Unit (ULP), which experienced severe overloading with a peak loading of 113.82%, exceeding the maximum allowable loading limit of 80% specified in SE No. 0017.E/DIR/2014. The overload condition resulted in excessive thermal stress and a 17% voltage drop, exceeding the permissible service voltage variation specified in SPLN 1:1995. To address these problems, a load-splitting strategy was implemented through the installation of an SNTR insertion transformer. A comparative quantitative (before–after) analysis was conducted using field measurement data to evaluate transformer loading, voltage drop, and transformer loss of life before and after the technical intervention. The results show that the loading of the PGCE distribution transformer was reduced to approximately 76–77%, restoring its operation to within the recommended loading limit. In addition, the voltage drop decreased from 17% to 4.5%, satisfying the service voltage requirement of +5% to −10%. The reduction in transformer loading also lowered thermal stress on the winding insulation, thereby reducing the transformer loss of life and improving asset reliability. These findings demonstrate that load splitting combined with the installation of an insertion transformer is an effective engineering solution for mitigating transformer overloading, improving voltage quality, and extending transformer service life.
creator: Kartika Tresya M
creator: Alex Fernandes
creator: Welly Okke
creator: Nurmiati Pasra⁠
creator: Samsurizal
creator: Tony Koerniawan
rights: Copyright (c) 2026 Kartika Tresya M, Alex Fernandes, Welly Okke, Nurmiati Pasra⁠, Samsurizal, Tony Koerniawan https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 8
number: 2
starting page: 203
ending page: 210
doi: 10.36526/ztr.v8i2.8136

Transforming Investment Structures in Capital-Intensive Electricity Utilities: A Managed Service Approach for CapEx-to-OpEx Transformation
type: item
description: Electric utilities operate in a capital-intensive environment characterized by substantial infrastructure investment requirements, long asset life cycles, regulated tariffs, and relatively stable financial returns. Increasing electricity demand and energy transition initiatives have intensified the need for alternative financing mechanisms that support infrastructure expansion while maintaining service reliability and affordability. This study evaluates the transformation of conventional Capital Expenditure (CapEx)-based investment structures into Operating Expenditure (OpEx)-based arrangements through managed service models in electricity utilities. Using a mixed-method explanatory case study and techno-economic analysis, the study examines managed service implementation for 160 kVA distribution transformers in Indonesia's electricity distribution sector. The analysis incorporates lifecycle cost evaluation, cash flow assessment, and financial indicators including Net Present Value (NPV) and Internal Rate of Return (IRR). The results show that the managed service model improves financial flexibility by replacing large upfront investments with predictable periodic payments and transferring operational risks to service providers. However, this flexibility is accompanied by an approximately 15% increase in lifecycle NPV compared with the conventional Total Expenditure (TOTEX) model. These findings demonstrate that managed services represent a viable alternative financing strategy for capital-intensive electricity utilities facing investment constraints
creator: Ignatius Rendroyoko
creator: Najahul Imtihan
creator: Ishak Sinaga
rights: Copyright (c) 2026 Ignatius Rendroyoko, Najahul Imtihan, Ishak Sinaga https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 8
number: 2
starting page: 211
ending page: 220
doi: 10.36526/ztr.v8i2.8135

Comparing Machine Learning and Neuro-Fuzzy Models for Hydropower Prediction Under Seasonal Operating Conditions
type: item
description: Accurate prediction of hydropower energy production is essential for operational planning and decision-making under varying hydrological conditions. However, comparative evaluations of high-accuracy data-driven models and interpretable intelligent models using real operational data remain limited. This study aims to compare the performance of Machine Learning and Neuro-Fuzzy approaches for predicting energy production at the Sengguruh Hydropower Plant, Indonesia. One year of engineering-validated operational data consisting of discharge, reservoir and tailrace water levels, operation time, turbine efficiency, and derived hydraulic head were used for model development and evaluation. Three Machine Learning models, namely Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Regression (SVR), were compared with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results indicate that Machine Learning models achieved superior predictive performance, with Random Forest providing the highest accuracy (RMSE = 8.42 MWh/day, MAE = 6.35 MWh/day, and R² = 0.982). In contrast, the ANFIS model produced lower prediction accuracy but offered a more interpretable representation of hydropower operating behavior. Correlation analysis further indicates that operation time and inflow discharge are the most influential operational variables associated with energy production. The findings demonstrate that Machine Learning approaches are more suitable for high-accuracy forecasting and operational optimization, whereas Neuro-Fuzzy models provide advantages in interpretability and operational representation. These results contribute to the development of intelligent hydropower forecasting systems that balance predictive performance and engineering understanding.
creator: Daniel Rohi
creator: Akhmad Solikin
rights: Copyright (c) 2026 Daniel Rohi, Akhmad Solikin https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
volume: 8
number: 2
starting page: 221
ending page: 229
doi: 10.36526/ztr.v8i2.8441