JTERA (Jurnal Teknologi Rekayasa) is peer-review journal providing original research papers, case studies, and articles review in engineering technology field. The journal can be used as an authoritative source of scientific information for researchers, researcher academia or institution, industrial engineer, and government agencies. Paper topics of JTERA focused on engineering including informatics engineering, computer engineering, electrical engineering, mechanical engineering, industrial engineering, civil engineering, environmental engineering, and other topics related to engineering. JTERA is published by Politeknik Sukabumi and managed to be issued twice in every volume on June and December. This journal has been registered at ISSN 2548-737X (printed version) and ISSN 2548-8678 (online version). JTERA has been accredited Grade "3" (SINTA 3) by Ministry of Research, Technology, and Higher Education of Republic of Indonesia with Decree No. 30/E/KPT/2018 October 24, 2018 and Decree No. 85/M/KPT/2020 April 1, 2020. JTERA indexed/abstracted by: Google Scholar GARUDA (Garba Rujukan Digital) Indonesia One Search (IOS) Science and Technology Index (SINTA) Index Copernicus International (ICI) Directory of Open Access Scholarly Resources (ROAD) etc. JTERA Editorial Board
Authors who publish with this journal agree to the following terms: The copyright to this article is transferred to JTERA if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to JTERA. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment. The copyright transfer form will be sent via email before the article published. The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Identification of Traffic Accident-Prone Locations (Black Spots) Based on Road Geometry in Purbalingga Regency
The number of traffic accidents in Indonesia is very high, reaching 1,150,025 accidents with 27,329 fatalities in 2024. Efforts to reduce the number of accidents include identifying and addressing accident-prone areas. The method used to determine black-spot is based on the Equivalent Accident Number (EAN) and Upper Control Limit (UCL). The weakness of the EAN-UCL method is that black-spot can only be determined after data on accidents and casualties, both life and property, is available. Therefore, research is needed to find new methods and approaches to determine black-spot locations without having to wait for accidents and casualties. The aim of this research is to formulate a model for determining black-spot locations based on road geometric facilities. Each road geometric parameter is compared with the standard road geometric design according to its function and is weighted. The total weighted value is analyzed statistically to determine whether the road section is categorized as a black-spot location or not. Six geometric road parameters include vehicle speed, lane width, shoulder width, sidewalk width, median width, and the difference in elevation between shoulder and edge of the road pavement (drop-off). Black-spot locations in Purbalingga Regency include Bayeman Tlahab Lor Karangreja road, Majasari Bukateja road, Bojongsari road, Mayjend Sungkono Kalimanah Wetan road, Gembong Bojongsari road, Kejobong road, Selanegara Kaligondang road, Kebutuh Bukateja road, Bojong Bukateja road, and Bojanegara Padamara. There are differences in the ranking of black-spot locations between the EAN-UCL method and the weighting method based on road geometric facilities.
Copyright (c) 2026 JTERA (Jurnal Teknologi Rekayasa) http://creativecommons.org/licenses/by-nc-sa/4.0
Sentiment Analysis of Digital Korlantas Polri Apps Service Based on LSTM and SVM Methods
Advancements in digital technology have encouraged numerous innovations in public services, one of which is the Digital Korlantas Polri app. This application makes it easier for the public to access traffic services such as driver’s license issuance and renewal, vehicle data checking, and accident reporting. However, despite the convenience it offers, there are still various user reviews that point to technical issues and dissatisfaction with the quality of service. This study applies sentiment analysis to understand public perception of the Digital Korlantas app, providing a basis for improving its quality. The collection of the dataset was achieved by web scraping 2,000 user reviews from the Google Play Store spanning the period from December 2023 to March 2025. The phases of the research encompass gathering data, pre-processing text, assigning sentiment labels based on lexicons, applying TF-IDF for word weighting, and performing classification using the Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) algorithms. The performance of the model was assessed through a confusion matrix, utilizing accuracy, precision, recall, and F1-score as evaluation metrics. The findings indicated that, out of 2,000 reviews, 1,402 were identified as positive, 538 were categorized as negative, and 60 were considered neutral. The SVM model demonstrated the highest performance, obtaining an accuracy of 96.8%, a precision of 65.6%, a recall of 50.0%, and an F1-score of 55.0%. At the same time, the LSTM model attained an accuracy of 94.5%, with a precision of 31.5%, a recall of 33.3%, and an F1-score of 32.4%. These results show that SVM is superior at handling high-dimensional data, while LSTM remains effective at capturing long-term context patterns in review texts.
Peningkatan Sifat Fisik dan Mekanik Baja AISI 1030 dengan proses Pack Chromizing dengan variasi waktu penahanan
The aim of this research is to determine the effect of variation in holding time of pack chromizing on the physical and mechanical properties of AISI 1030 steel. The pack chromizing process uses a mixture of chromium (Cr2O3) and ammonium chloride (NH4Cl) in a ratio of 20:1. The pack chromizing process is carried out at a temperature of 1000℃ with holding time variations of 4, 5, and 6 hours. The tests conducted were chemical composition testing, microstructure observation, hardness testing, wear testing, and corrosion testing. Chemical composition tests show that the material used is classified as medium carbon steel. The chromium content increased after undergoing the chromizing process. The highest chromium content is found at a holding time of 6 hours, which is 0.49%, followed by a holding time of 5 hours with a value of 0.3139%, and the lowest chromium content is at a holding time of 4 hours with a value of 0.1136%. Microstructure observation shows that AISI 1030 steel and the results of pack chromizing have a microstructure consisting of pearlite and ferrite. The results of hardness testing using the Vickers microhardness method indicate that the longer the holding time in the pack chromizing process, the higher the hardness value becomes. The pack chromizing process reduces the wear value of the material. The highest reduction occurs at a holding time of 6 hours, amounting to 41.82%. The corrosion testing results indicate that AISI 1030 steel has a corrosion resistance value classified as good, with a value of 0.11 mmpy, whereas the results of pack chromizing for all variations of holding time fall into the very good category (0.02 – 0.1 mmpy).
Effect of Electrode Material on Surface Roughness and Hardness of Jaw Implants
Long processing time and decreased material hardness are the main problems in developing jaw implants using the EDM die-sinking method. Therefore, research is needed to speed up the process and minimize the decrease in hardness of jaw implant materials made using EDM die-sinking. This study aims to investigate the right type of electrode material to speed up the process time and minimize the decrease in hardness of jaw implant materials made using EDM die-sinking. The effect of electrode material in the manufacture of jaw implants was also investigated. The manufacture of jaw implants used copper, tungsten, brass, and high-speed steel (HSS) electrode materials. Pulse current and pulse on time used 6 A and 22 μs, respectively. The results showed that the surface roughness and decrease in hardness of jaw implant materials were influenced by the thermal conductivity of the electrode material. The best electrode material in the manufacture of jaw implants using EDM die-sinking is copper. The manufacturing process of jaw implants with copper material was approximately 7.5 hours. The surface roughness and hardness of jaw implants were approximately 1.97 μm and 150 HV, respectively.
Analisa Power Quality Auxiliary Trafo Daya di PLTU Paiton Menggunakan Algoritma FFT (Fast Fourier Transform) Berbasis MATLAB – Simulink
This study analyzes the harmonics characteristics in the auxiliary power transformer system of the Paiton Unit 6 coal-fired power plant using the MATLAB-Simulink-based Fast Fourier Transform (FFT) algorithm. The research method includes modeling the auxiliary electrical system with a Single Line Diagram (SLD) in Simulink, validating the model with operational data from the Central Control Room (CCR), and implementing the FFT algorithm for spectral signal analysis. The system data, consisting of generator specifications, transformers, 10 kV busbar configuration, and 22 auxiliary motor loads with a sampling frequency parameter of 10 kHz, produced 20,000 samples for harmonic detection up to the 100th order. FFT analysis revealed that the Paiton PLTU auxiliary system experienced harmonic distortion with THD on Bus 4 of 6.37% and Bus 5 of 6.15%, both exceeding the IEEE 519-2014 limit of 5.0%. Implementation of a tuned LC passive filter with a capacity of 6.00 MVAr. After installing the filter, THD was successfully reduced to 3.20% on Bus 4 and 3.22% on Bus 5, with a reduction efficiency of 49.6% and 49.5%. These results show that the system has met the IEEE 519-2014 standard. This study provides solutions related to harmonic analysis and recommendations for filters that should be used.
Analisis Kerusakan Gripper, Cavity, Bearing, dan Locking Shaft Mesin Injection Molding Sidel SBO18 di PT.XYZ
The Sidel SBO 18 is a machine used for manufacturing plastic bottles or bottled drinking water packaging. At PT XYZ, the Sidel SBO 18 injection blow molding machine plays a crucial role in forming PET bottle preforms and is capable of producing up to 40,000 bottles per hour. During operation, this machine frequently experiences significant failures in components such as the gripper, cavity , bearing, and locking shaft. These failures result in reduced production efficiency and increased operational costs. Therefore, an analysis is required to identify the causes of the failures and to design improvement strategies to enhance machine efficiency. The objectives of this study are to analyze the factors causing the failures, evaluate their impacts, design improvement strategies, and conduct testing of the proposed failure prevention solutions. The research methodology includes failure identification, literature review, interviews and observations, fishbone diagram analysis, development of solutions and recommendations, modification design, fabrication, testing, result analysis, and conclusion. The identified problems and recommendations include operators’ limited understanding of machine operation, tight production schedules, the absence of support for the blow nozzle, failure in preform transfer to the mold cavity , the absence of a gap between the blow nozzle and the gripper, incorrect timing, sensor reading errors, an ineffective Cam safety stretching unit design, the entry of foreign components other than preform material, and excessive pressure on the bearing and locking shaft. The improvement strategies proposed include redesigning the Cam Safety Stretching unit, providing operator training, preventing incorrect machine settings, and rescheduling maintenance activities. The test results show an increase in machine productivity from 89.92% in July to 92.53% in August, and 91.87% in September.
Fine-Tuning IndoBERT Untuk Analisis Sentimen Berita Saham Berbahasa Indonesia Dengan Hyperparameter Optimization
This study aims to improve sentiment analysis performance on Indonesian stock news using transformer-based models. The rapid growth of the Indonesian capital market has led to an increase in financial news information, creating the need for accurate automated sentiment analysis systems to support data-driven investment decisions. The proposed method applies fine-tuning on the IndoBERT model with hyperparameter optimization using Bayesian Optimization through the Optuna framework. The dataset consists of 23,108 Indonesian stock news articles classified into three sentiment classes: positive, neutral, and negative. Model evaluation is conducted using accuracy, precision, recall, F1-score, Macro-F1, confusion matrix, and ROC-AUC with a one-vs-rest approach for multi-class classification. The results indicate that IndoBERT-Base-Uncased with optimal hyperparameter configuration achieves the best performance, with an accuracy of 0.8269 and an F1-score of 0.7816. The application of hyperparameter optimization significantly improves model performance compared to the baseline. This study contributes to the advancement of Indonesian-language sentiment analysis in the financial domain and provides an effective approach to improving model performance through hyperparameter optimization.
Implementasi Multi-Platform Naïve Bayes untuk Prediksi Keberhasilan Mahasiswa secara Real Time
Educational Data Mining has emerged as a critical technology for predictive analytics in higher education. However, implementation consistency across different computational platforms remains an understudied area. Student graduation prediction requires robust algorithmic approaches with validated performance across multiple computational environments to support reliable data-driven academic decision making. This research implements and evaluates the performance of Naïve Bayes algorithm in Educational Data Mining for student graduation prediction. The study provides comprehensive multi-platform comparative analysis between WEKA and Python implementations with rigorous statistical validation. Academic data from graduated students of D-3 Information Systems Study Program, Politeknik Negeri Subang were utilized as the training dataset. Implementation was conducted using a dual-platform approach: WEKA and Python with scikit-learn library. Performance evaluation employed stratified 10-fold cross-validation with comprehensive metrics including accuracy, precision, recall, F1-score, and Kappa statistic. Comparative analysis was conducted against Decision Tree, Support Vector Machine, Random Forest, and Logistic Regression algorithms. The results demonstrate that Naïve Bayes achieved adequate accuracy with exceptional cross-platform consistency. Comparative analysis revealed competitive performance with superior computational efficiency. Multi-platform implementation validates Naïve Bayes effectiveness for Educational Data Mining applications with consistent performance across computational environments, making it suitable for real-time educational analytics deployment in vocational education institutions.
Analisis Peningkatan Kinerja Model InceptionResNetV2 dalam Identifikasi Wajah pada Dataset In-the-Wild melalui Optimasi Hyperparameter
Deep learning-powered facial recognition is essential across various applications, including security systems, authentication processes, and identity verification. However, model performance on in-the-wild datasets faces challenges due to variations in lighting conditions, pose differences, diverse facial expressions, and background complexity. This study aims to analyze the performance improvement of the InceptionResNetV2 model through hyperparameter optimization for the face identification task, using a primary dataset comprising 5,180 face images of 153 individuals captured in in‑the‑wild conditions. The model employs transfer learning, utilizing initial weights from ImageNet, with fine-tuning applied to the final 50 layers. Hyperparameter optimization uses the grid search method, combining learning rates of 0.001 and 0.0005, and batch sizes of 8 and 16. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics, supported by confusion matrix analysis and prediction case visualizations. Results show that the optimized model improved accuracy from 0.8830 to 0.9148, with consistent gains in precision (0.9063 to 0.9249), recall (0.9016 to 0.9302), and F1-score (0.8839 to 0.9139). Confusion matrix analysis indicates an increase in correct predictions across different classes, while improvement case visualizations demonstrate the model's ability to correct prediction errors under complex conditions. McNemar's statistical test yields a p-value of 0.000639 (< 0.05), indicating that the performance improvement is statistically significant. Therefore, hyperparameter optimization is effective in enhancing model performance on in-the-wild datasets.
Identifikasi Malware pada Jaringan Internet sebagai Tindakan Preventif untuk Ancaman Siber berbasis Deep Learning
Network security is a crucial aspect for Internet Service Providers (ISPs), especially in the face of the ever-growing threat of malware. Malware is a malicious device designed to attack the operating system or exploit system vulnerabilities. This threat can lead to the theft of critical data and significant losses for users. One of the company networks and internet service providers used by government agencies in South Sumatra faces the challenge of minimizing the risk of malware attacks on the networks it manages. In this study, a deep learning method was applied with the Convolutional Neural Network (CNN) algorithm to identify and classify network traffic indicated by malware proposed. The process was carried out through the stages of collecting network traffic datasets, data pre-processing, training a CNN model, and evaluating model performance. The test results showed that the CNN method was able to detect malware attacks with an accuracy rate of 83%, a precision 83%, and a recall 82%. The application of this method provides a fast and accurate detection system, enabling servers and clients to be more aware of cyber threats and avoid the theft of crucial data.
Sistem Prediksi Tingkat Keamanan Objek pada Area Blind Spot Unit Forklift Menggunakan Artificial Neural Networks dengan Dua Skenario
Forklifts are widely used in the construction and industrial sectors; however, their operation involves a high risk of accidents, particularly in blind spot areas that are not visible to the operator. Therefore, this study aims to develop a safety level prediction system for objects in forklift blind spot areas to improve operational safety. The proposed method integrates a fuzzy logic approach with machine learning based on Artificial Neural Networks (ANN). The dataset was generated using a Python program with fuzzy logic and a sensor based-system, with input parameters including distance (0–300 cm), angle (0°–180°), and steering position (0°–180°). The safety level is classified into three categories: safe, caution, and danger. The ANN model employs a feed-forward architecture with variations in training parameters. The results indicate that the optimal configuration is achieved with 4 hidden layers, 100 hidden nodes, 1500 epochs, and the Adam optimization algorithm. The developed model demonstrates strong performance based on precision, recall, and F1-score evaluation metrics. These findings indicate that the integration of fuzzy logic and ANN is effective in predicting safety levels in forklift blind spot areas. This approach has the potential to be further developed into a real-time safety monitoring system for industrial vehicles.
Wind Turbine With Dynamic Optimal Power Flow Control For Improved Short Circuit Fault Ratio
Large-scale wind turbines are integrated into the grid, causing the short-circuit ratio to decrease. This also weakens the ability to regulate voltage, and faults can more easily lead to greater voltage fluctuations during renewable energy unit and off-grid accidents. A synchronization configuration method that considers improving the short-circuit ratio is studied for power grids with a high proportion of wind power. First, an analysis is conducted on the multi-renewable-station short-circuit ratio/multi-renewable-station short-circuit ratio (MRSCR), which can explain interactions among sites. Second, system security constraints are used to develop an ideal synchronous generator power flow control model based on the power grid topology. Next, the Beetle Swarm Optimization/Beetle Swarm Optimization (BSO) algorithm is used to determine the ideal MRSCR. This produces the best voltage and power settings for synchronous generators. Finally, a case study is carried out on a power grid with a high proportion of wind power to verify that the proposed optimization method can significantly improve the MRSCR of wind turbines based on PSD-BPA.
Comparative Evaluation of Machine Learning Algorithms Using SMOTENC in Classifying Toddler Nutritional Status Based on Anthropometric Data
Class imbalance in under-five nutritional status data can make classification models appear accurate while limiting minority-class recognition. This study evaluated Support Vector Machine, K-Nearest Neighbors, Naive Bayes, Decision Tree, and Random Forest for multiclass classification of under-five nutritional status using anthropometric data. The dataset contained 3,716 Posyandu Desa Ujunggenteng records from 2025, with sex, age, weight, and height as features and four target classes: undernutrition, good nutritional status, risk of overnutrition, and overnutrition. Labels were treated as operational records, not as recalculated WHO/Indonesian Ministry of Health z-score standards. The experiment used an 80:20 stratified train-test split, 5-fold Stratified K-Fold Cross-Validation, limited candidate-based parameter search, numerical-categorical preprocessing, and SMOTENC only on training data or training folds. Cross-validation showed that SVM achieved the highest CV Macro F1-score of 0.829578. On the test set, Random Forest obtained the highest Macro F1-score of 0.827132, while KNN achieved the highest accuracy of 0.956989 and balanced accuracy of 0.883393. Model selection should consider Macro F1-score, balanced accuracy, confusion matrix, and class-wise performance. Based on cross-validation, SVM was selected as the final model, while Random Forest was reported as the best test-set model based on Macro F1-score.
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Rancang Bangun Mesin Pemecah Kedelai Kapasitas 30Kg/Jam Untuk Produksi Susu Kedelai Menggunakan Metode French
The manual process of separating soybean hulls in the home-scale soy milk industry is relatively time-consuming and inefficient. This study aims to design and evaluate the performance of a soybean cracking machine with a target capacity of 30 kg/h using the French method. The systematic approach of the French method was applied from the task clarification stage to product conceptualization to produce an optimal machine design. The designed machine utilizes a 300-Watt (1400 rpm) AC electric motor as the prime mover, a single stainless steel roller cylinder with a diameter of 3 inches, and an enclosure made of food-grade 304 stainless steel. The experimental results indicated that the soybean cracking machine was capable of operating at an actual capacity of 40.3 kg/h. This performance achievement demonstrated that the machine's actual working capacity was 34% higher than the initial planning target. Based on the technical evaluation results, the application of the French method successfully produced a hygienic, robust, and efficiently operating soybean cracking machine design to support productivity improvement in the soy milk industry.
Short-Term Capacity Estimation of 4S LiFePO4 Battery Under Partial Constant Current Cycling Based on Temporal Convolutional Network
Lithium iron phosphate (LiFePO₄) batteries are widely utilized in renewable energy storage systems due to their high thermal stability and long cycle life. However, accurate battery capacity estimation remains a significant challenge, particularly for small-scale batteries with limited datasets. This study proposes a short-term capacity estimation framework based on a Temporal Convolutional Network (TCN) for a 4S LiFePO₄ 12.8 V 6 Ah battery pack under constant-current cycling conditions. A custom data acquisition (DAQ) system was developed using a Raspberry Pi Pico microcontroller integrated with ADS1115, INA226, and DS18B20 sensors to monitor voltage, current, and temperature in real time during charging and discharging experiments. A dataset consisting of 76 cycles was collected experimentally, from which 13 anomalous early-cycle samples were removed due to electrochemical stabilization phenomena, resulting in 63 effective samples. The TCN model was trained using a sliding-window approach with a sequence length of 5 timesteps, incorporating causal and dilated convolutions, residual connections, and dropout regularization. Evaluation on the testing dataset produced an RMSE of 0.02114 Ah, MAE of 0.01288 Ah, and MAPE of 0.827%, indicating high prediction accuracy with an average relative deviation below 1%. The negative value of −5.8753 was attributed to the statistical limitations of the metric when applied to small-scale datasets with low variance. The results demonstrate that the proposed TCN architecture is capable of learning short-term temporal degradation characteristics from limited battery cycling data with high relative accuracy.
SISTEM MONITORING KONDISI SEHAT (STATE OF HEALTH) BATERAI LiFePO₄ BERBASIS WEB DENGAN INTEGRASI DATA SUHU, ARUS, DAN TEGANGAN
The use of Lithium Iron Phosphate (LiFePO4) batteries demands accurate State of Health (SoH) monitoring to prevent premature degradation. Real-time monitoring of critical parameters is essential as a proactive effort in enhancing safety and optimizing lifespan. Therefore, this research aims to develop an integrated web-based monitoring system to facilitate comprehensive data supervision access for users. The method implements an ESP32 microcontroller as a data processor from a 0–25V voltage sensor, an ACS758 current sensor, and an MAX6675 temperature sensor. SoH estimation is calculated using the coulomb counting method through current integration over time to measure actual battery capacity. Test results indicate the web dashboard successfully displays interactive operational data with highly stable wireless transmission. The voltage sensor precisely records cycle variations, the ACS758 sensor excellently detects dynamic current flows, and the MAX6675 consistently detects cell temperature fluctuations. The coulomb counting algorithm smoothly records electrical charge accumulation, yielding SoH estimates that accurately represent the battery's physical condition. Overall, the hardware and software integration operates harmoniously as a highly effective battery performance maintenance solution.
Implementasi dan Analisis Sistem Keamanan Web pada planetelektronikindonesia.com Menggunakan Teknologi SSL/TLS dan WAF
The use of websites as platforms for delivering digital information increases exposure to web-based cyber threats, particularly when security mechanisms are not properly implemented. This study focuses on the implementation and evaluation of Secure Socket Layer/Transport Layer Security (SSL/TLS) and a Web Application Firewall (WAF) to enhance the security of the planetelektronikindonesia.com website. The research methodology includes deploying SSL/TLS to secure data transmission and configuring a WAF to protect the application layer, followed by security testing using Qualys SSL Labs, OWASP ZAP, Burp Suite Community Edition, and an analysis of WAF attack logs over an observation period of approximately 14 days. The evaluation results show that the SSL/TLS implementation achieved an A rating in Qualys SSL Labs with support for TLS 1.2 and TLS 1.3, while no common vulnerabilities were detected. Vulnerability assessment using OWASP ZAP reported no high-risk findings, and manual testing confirmed that attacks such as SQL injection, cross-site scripting (XSS), and brute force were successfully prevented. In addition, WAF log analysis indicates a reduction in detected attacks from 140 to 28, corresponding to a decrease of approximately ±80%. These results indicate that the combined application of SSL/TLS and WAF effectively improves website security, safeguards user data, and significantly reduces the risk of web application exploitation without adversely affecting system performance.
Analisis Hubung Singkat Inter Bus Transformer (IBT) 500 MVA Paralel Empat Unit di GITET Kediri
GITET Kediri is an extra-high-voltage substation that operates 500 kV, 150 kV, and 20 kV systems in an integrated manner. As of 2015, this facility operated three 500/150 kV Inter-Bus Transformers (IBTs) with a capacity of 500 MVA per unit, and was later expanded with the addition of a fourth IBT in 2020[1]. The bay configuration on the 150 kV side has been in place since 2017 with no changes to the equipment to date. Based on the provisions of IEC 60076-1 regarding transformer parallel operation, the four IBTs are deemed to meet the requirements because the impedance differences among the units fall within the ±7.5% range, namely 3.3%, 13.16%, 14.452%, and 14.375%[2]. The comparison results between simulation and manual calculations show the following short-circuit current values: a three-phase fault produces 26.978 kA (simulation) and 26.548 kA (calculation); a single-phase-to-ground fault produces 29.659 kA and 30.097 kA; a phase-to-phase fault of 23,363 kA and 23,066 kA; and a two-phase-to-ground fault resulting in 28,374 kA and 28,924 kA for phase AB, and 28,788 kA and 28,996 kA for phase BC. The breaking capacity of the PMT at IBT 1, 2, 4, and the Kertosono, Jayakertas, Tulungagung 1&2, Banaran 1&2, Trafo 1, Kopel 1&2, and Section A&B bays is recorded at 40 kA, while IBT 3 and Trafo 2 have a capacity of 50 kA. This study concludes that parallel operation of the four IBTs is feasible, given that the impedance difference remains within standard limits, and all 150 kV PMTs are deemed operational because the fault current does not exceed the PMTs’ capacity.
Kajian Faktor Yang Mempengaruhi Keterlambatan Pekerjaan Pada Proyek Landed Housing Di Podomoro Park Bandung
Project delays in construction are often influenced by the managerial roles of Owners, Consultants, and Contractors. These three parties are crucial to project success, with competency being a key contributing factor. Low competency can lead to failure, while adequate competency increases the likelihood of success. Relevant competency standards can be referred to in the Project Manager Competency Development (PMCD) and the Indonesian National Work Competency Standards (SKKNI). This study aims to identify significant factors causing delays in the construction of landed housing projects at Podomoro Park Bandung, developed by PT Agung Podomoro Land. Delay-related factors were collected from PMCD, SKKNI, and previous studies, then analyzed using matrix and frequency methods, SPSS, and PLS-SEM. The analysis resulted in 12 main factors and 52 sub-factors related to Owners, 19 main factors and 87 sub-factors related to Consultants/Supervisors, and 17 main factors and 76 sub-factors related to Contractors. A pilot study involving 10 respondents was conducted to test validity and reliability using SPSS v.24, showing Cronbach’s Alpha values above 0.7. The full questionnaire was then distributed to 78 respondents and analyzed using PLS-SEM 4.1. The results indicate that factors under the Owner variable (X1), along with its 52 sub-factors, significantly influence project delays. This study is expected to serve as a reference for stakeholders in improving competency and minimizing delays in future landed housing project
Rancang Bangun Dan Uji Kinerja Mesin Pencacah Kulit Kacang Tanah Kapasitas 15 Kg/Jam
Peanut shells are one of the major agricultural wastes generated from peanut processing in Indonesia and are still underutilized, despite their potential use as animal feed. This study aims to design, fabricate, and evaluate the performance of a peanut shell chopping machine with a design capacity of 15 kg/h. The machine was developed using the systematic engineering design approach proposed by Pahl and Beitz, supported by morphological analysis to select the optimal design concept. The chopping mechanism is driven by a 0.75 hp three-phase AC electric motor and employs a V-belt and pulley transmission system. Performance tests were conducted at a shaft speed of 1191 rpm using four chopping blades, with blade spacing variations of 20 mm, 10 mm, and 5 mm. The results show that blade spacing significantly affects chopping performance. The highest chopping capacity, reaching 21.46 kg/h with a target particle size of 3 mm, was obtained at a blade spacing of 20 mm. These results indicate that the proposed machine meets the design objectives and is suitable for small-scale applications to improve the utilization of peanut shell waste as animal feed.
Rancang Bangun Sistem Proteksi Over Current Relay Dengan Karakteristik Inverse Sesuai IEC 60255 Terintegrasi IoT Untuk Monitoring Jarak Jauh
This research aims to design and develop a prototype overcurrent relay (OCR) protection system based on the ESP32 microcontroller that implements inverse-time characteristics in accordance with IEC 60255 and is integrated with the Internet of Things (IoT) for remote monitoring. The work is motivated by the need of medium-scale industries for reliable yet economical overcurrent protection devices, since conventional industrial OCRs are generally expensive and not fully equipped with real-time monitoring capabilities. The proposed system employs a PZEM-004T module to measure electrical parameters including voltage, current, power, energy, frequency, and power factor. These measurements are processed by the ESP32 to calculate the Plug Setting Multiplier (PSM) and determine the relay operating time based on three IEC 60255 inverse curves, namely Standard Inverse, Very Inverse, and Extremely Inverse. Trip decisions are executed through a power relay and buzzer as alarm indicators, while electrical parameters and protection status are displayed on an I2C 20×4 LCD and transmitted in real time to the Blynk IoT platform, enabling monitoring and remote reset via smartphone. Experimental tests on resistive and dynamic loads within a current range of 1–20 A were conducted to evaluate sensor accuracy, conformity of trip time with theoretical inverse curves, and the reliability of IoT communication. Overall, the developed prototype is expected to serve as a low-cost digital OCR alternative that is more informative, flexible, and easy to integrate into protection systems for medium-scale industrial loads.
Performance Comparison of YOLOv8n and Faster R-CNN for Automatic Detection of School Uniform Attributes
Manual inspection of students’ uniform completeness is still commonly performed in schools, especially in vocational schools with a large number of students. This process is time-consuming, prone to subjectivity, and difficult to document consistently. This study aims to compare the performance of YOLOv8n and Faster R-CNN MobileNetV3 320 FPN in automatically detecting school uniform attributes. The dataset used in this study consists of 638 school uniform images with five object classes, namely epolet, name_tag, sabuk, sepatu_hitam, and topi. Both models were trained using the same dataset and evaluated based on Intersection over Union (IoU), mean Average Precision (mAP), and Frames per Second (FPS) under the experimental configuration used in this study. The experimental results show that YOLOv8n achieved an IoU of 0.8713, mAP50 of 0.9193, mAP50-95 of 0.6695, and an inference speed of 83.5921 FPS. Meanwhile, Faster R-CNN achieved an IoU of 0.6714, mAP50 of 0.6640, mAP50-95 of 0.3853, and an inference speed of 9.5801 FPS. These results indicate that, under the experimental configuration and evaluation subsets used in this study, YOLOv8n provides a better balance between localization accuracy, detection accuracy, and inference efficiency. Therefore, YOLOv8n has the potential to be more suitable as a basis for developing a semi-real-time school uniform completeness monitoring system.
From Exposed Risk to Controlled Risk: Evaluating MikroTik Firewall Rules in Network Security
The increasing dependence of government institutions on internet-based services has made network security a strategic requirement for preserving data confidentiality, service availability, and public trust. This study evaluates the reliability and mitigation effectiveness of MikroTik firewall rules in securing the internet network of a local government institution against three attack scenarios: packet sniffing, port scanning, and Distributed Denial of Service (DDoS). An experimental design was employed to compare network conditions before and after the activation of firewall rules. ARP spoofing-based packet sniffing was simulated using Ettercap, port scanning was performed using Zenmap, and DDoS traffic was generated using Hping3. In contrast, Wireshark and Winbox were used to monitor network traffic and firewall responses. The initial results show that before firewall activation, the network was exposed to multidimensional risks: packet sniffing revealed DHCP packets and IP-MAC metadata, port scanning identified 8 open ports, and the DDoS attack increased CPU utilization to 100% and elevated memory usage. After the firewall rules were activated, all three attacks were successfully mitigated. Sniffing activity was limited to normal DHCP Discover traffic, port scanning no longer revealed open ports, and DDoS packets were dropped before establishing effective connections. Overall, the MikroTik firewall achieved 100% reliability within the tested scenarios, demonstrating its effectiveness in reducing traffic exposure, controlling service visibility, and maintaining system availability in a government network environment.
Tinjauan Sistematis terhadap Ensemble SemiSupervised Network Intrusion Detection Systemdengan Perspektif Adversarial Robustness
Sistem deteksi intrusi jaringan atau Network Intrusion Detection System (NIDS) berbasis pembelajaran mesin menjadi pilar utama keamanan siber modern. Namun, model berbasis data berlabel penuh sering kali terbatas dalam generalisasi dan rentan terhadap serangan adversarial. Pendekatan semi-supervised learning memanfaatkan data tak berlabel untuk memperluas cakupan pelatihan, sementara ensemble learning meningkatkan stabilitas melalui keragaman model. Penelitian ini melakukan Systematic Literature Review (SLR) untuk menganalisis tren, arah perkembangan, dan research gap pada kombinasi ensemble semi-supervised NIDS, dengan fokus utama pada robustness terhadap serangan adversarial. Proses review mengikuti pedoman PRISMA dengan sumber data dari IEEE Xplore, Scopus, SpringerLink, dan ScienceDirect untuk periode 2018–2025. Dari 147 artikel awal, 65 memenuhi kriteria inklusi. Hasil analisis menunjukkan sebagian besar penelitian menitikberatkan peningkatan akurasi (F1 > 95%), namun hanya 18% yang menguji ketahanan terhadap serangan adversarial secara eksplisit. Belum ditemukan studi yang mengintegrasikan ensemble multi-paradigma, semi-supervised learning, dan evaluasi adversarial attack secara terpadu. Kontribusi utama penelitian ini adalah penyusunan kerangka konseptual baru, Adversarial-Aware Hybrid Ensemble Framework, yang menempatkan proses adversarial design sebagai bagian integral dari pelatihan semi-supervised ensemble NIDS untuk meningkatkan keandalan sistem deteksi intrusi modern.