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Annisa Sarah
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Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta BSD City, Jl. Cisauk, Sampora, Cisauk Tangerang, Banten 15345 Tel. : +62 21 570 8826 Fax : +62 21 579 00573
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INDONESIA
JURNAL ELEKTRO
Published by Universitas Katolik Indonesia Atma Jaya
ISSN : 19799780     EISSN : 27464288     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Jurnal Elektro diterbitkan oleh Program Studi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta. Jurnal Elektro terbuka untuk penelitian dalam bidang-bidang teknik elektro seperti elektro arus kuat, elektronika, sistem kontrol atau kendali, telekomunikasi, komputer dan berbagai sub topik yang relevan terhadap perkembangan dan implementasi teknik elektro. Jurnal Elektro is published by the Electrical Engineering Bachelor Program, Faculty of Engineering, Atma Jaya Catholic University of Indonesia, Jakarta. Jurnal Elektro is open to research in the fields of electrical engineering such as power, electronics, control or control systems, telecommunications, computers and various sub-topics relevant to the development and implementation of electrical engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 247 Documents
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Daftar Isi Jurnal Elektro Journal Editor
Jurnal Elektro Vol 13 No 1 (2020): Jurnal Elektro
Publisher : Program Studi Teknik Elektro, Fakultas Biosains, Teknologi, dan Inovasi, Universitas Katolik Indonesia Atma Jaya

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Template Jurnal Elektro Journal Editor
Jurnal Elektro Vol 13 No 1 (2020): Jurnal Elektro
Publisher : Program Studi Teknik Elektro, Fakultas Biosains, Teknologi, dan Inovasi, Universitas Katolik Indonesia Atma Jaya

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Analysis of Power Cable Sizing and Voltage Drop in the Upgrading Stasiun Pengumpul Puspa Asri 2 Project Putri Wulandari; Linda Wijayanti
Jurnal Elektro Vol 19 No 2 (2026): Jurnal Elektro : October 2026
Publisher : Program Studi Teknik Elektro, Fakultas Biosains, Teknologi, dan Inovasi, Universitas Katolik Indonesia Atma Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v19i2.8139

Abstract

The upgrading of oil and gas production facilities requires an electrical power distribution system that is reliable, safe, and compliant with voltage quality limits. This article discusses power cable sizing and voltage drop evaluation for the Puspa Asri Gathering Station Phase 2 Upgrading Project in Jambi, based on engineering documents for calculating cable sizing and voltage drop in the Puspa Asri Phase 2 Collector Station Upgrading Project. The method includes load identification, full-load current calculation, determination of cable current-carrying capacity after temperature and grouping correction factors, verification of voltage drop under steady-state and motor-starting conditions, and assessment of thermal withstand capability under short-circuit conditions. The design criteria refer to IEC 60364-5-52 and related standards, with voltage drop limits of 2% for switchgear/MCC-to-switchgear/MCC routes, 5% for motor terminals under normal operating conditions, 15% during motor starting, and 3% for distribution panel feeders. The evaluation of 16 main cable routes shows that all selected cables satisfy the requirements for ampacity, voltage drop, and short-circuit withstand capability. The selected cable sizes range from 3C+E 4 mm² to 4C+E 95 mm², using a temperature correction factor of 0.91 and a grouping derating factor of 0.79 for aboveground installation. The Water Injection Pump C case study shows that the selected 4C+E 35 mm² cable has a corrected current-carrying capacity of 90.58 A, which is greater than the design current of 47.03 A, with a steady-state voltage drop of 1.33% and a motor-starting voltage drop of 3.95%. Therefore, the cable design is considered technically feasible to support reliable operation of the production facility.
Generalized versus Panel-specific Machine Learning Models for Photovoltaic Power Estimation Under Limited Multi-Module Field Data Giovanni Dimas Prenata; Ahmad Ridho'i
Jurnal Elektro Vol 19 No 2 (2026): Jurnal Elektro : October 2026
Publisher : Program Studi Teknik Elektro, Fakultas Biosains, Teknologi, dan Inovasi, Universitas Katolik Indonesia Atma Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v19i2.8268

Abstract

Limited multi-module field data creates a trade-off between generalized models that pool observations from several photovoltaic modules and panel-specific models that preserve module dependent behavior but use fewer training samples. This study compares three modeling scopes for photovoltaic power estimation using 63 observations from three nominally 100 W modules measured over four days. Generalized-Shared, Generalized-Panel-Aware, and Panel-Specific scopes were evaluated using Ridge Regression, K-Nearest Neighbor Regression, Radial Basis Function Network Regression, and Random Forest Regression under nested leave-one-day-out validation. A datasheet physical power estimate was also evaluated as a non-machine-learning baseline. Panel-Specific Ridge achieved the best overall performance with an RMSE of 7.3236 W, MAE of 5.6750 W, and R2 of 0.8788, reducing RMSE by 60.23% relative to the physical baseline (18.4161 W). Generalized-Panel-Aware Random Forest achieved an RMSE of 7.7876 W overall and became the best configuration for P3 (6.3310 W) and for the Tuesday left-out day (4.9565 W). Generalized KNN and panel-aware RBF also benefited from pooled training data, whereas Panel-Specific Ridge remained the strongest regularized linear configuration. Wilcoxon signed-rank tests with Holm correction found no statistically significant scope differences across the 12 panel-day units; the largest scope effect was observed for panel-aware Random Forest (rank-biserial = -0.4872, adjusted p = 0.3027). The results show that the benefit of pooling or specialization is algorithm and operating condition dependent and should be interpreted within the limitations of the small single-site field dataset.
A Semantic Segmentation Approach to River Sediment Identification Ahmad Zaky Iswani; Kristiawan Dwi Usmanto
Jurnal Elektro Vol 19 No 2 (2026): Jurnal Elektro : October 2026
Publisher : Program Studi Teknik Elektro, Fakultas Biosains, Teknologi, dan Inovasi, Universitas Katolik Indonesia Atma Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v19i2.8271

Abstract

This study investigates the use of semantic segmentation to identify river sedimentation as part of soil erosion monitoring. Three neural network architectures, namely U-Net, LinkNet, and Feature Pyramid Network (FPN), were evaluated using 2,800 non-overlapping patches generated from 100 images of sediment formations from the IIUM River. After patch extraction, the image-mask pairs were divided into training and validation subsets using a 75:25 ratio, producing 2,100 training patches and 700 validation patches. Based on sediment-class Intersection over Union (IoU) under patch-level validation, U-Net achieved 0.8345, followed by LinkNet at 0.8189 and FPN at 0.2039. U-Net and LinkNet identified relatively uniform sediment formations more successfully, whereas all models struggled with irregular formations or sediment that was visually similar to river water. Because the data split was performed at the patch level, patches originating from the same source photograph may have been present in both the training and validation subsets. Therefore, the reported results should be interpreted as internal patch-level validation rather than as an estimate of generalisation to completely unseen source river images.
An mRMR-Based Feature Optimization Framework for Gaming Addiction Classification Rizki Surya Permana; Arif Rahman Hakim; Seshariana Rahma Melati; Muhammad Farhan Maulana
Jurnal Elektro Vol 19 No 2 (2026): Jurnal Elektro : October 2026
Publisher : Program Studi Teknik Elektro, Fakultas Biosains, Teknologi, dan Inovasi, Universitas Katolik Indonesia Atma Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v19i2.8273

Abstract

Gaming addiction classification is challenging because problematic gaming behavior is influenced by multiple behavioral, psychological, lifestyle, and contextual factors, while redundant or weakly informative features may reduce the effectiveness of machine learning models. To address this issue, this study investigates minimum Redundancy Maximum Relevance (mRMR) feature selection to obtain a more compact and discriminative feature representation for gaming addiction classification. Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbors (KNN) were evaluated using two configurations: a baseline using the complete predictor set and an mRMR-based configuration using the 15 highest-ranked features. All preprocessing, feature selection, hyperparameter tuning, and cross-validation were performed on the training data, while the independent test set was used only for final evaluation. The results show that the effect of mRMR varies across classifiers, indicating that feature reduction does not universally improve classification performance. The strongest improvement was obtained by SVM, whose accuracy increased from 0.940 to 0.960 and F1-score from 0.824 to 0.857 after applying mRMR. The mRMR–SVM model also achieved a precision of 1.000 and a recall of 0.750. In contrast, the baseline XGBoost model achieved a higher recall of 0.914, indicating a trade-off between false-positive reduction and positive-case detection. These findings demonstrate that mRMR can improve classification when the selected feature space is well aligned with the learning mechanism of the classifier, particularly for margin-based SVM. The selected features further indicate that gaming addiction classification is associated not only with gaming intensity but also with psychosocial and lifestyle characteristics. Therefore, the proposed mRMR–SVM configuration provides a compact and effective representation for gaming addiction classification, although validation on larger and more diverse datasets is required before broader generalization.
Quality Prediction Based on Artificial Neural Networks in the Plastic Injection Molding Process Riana Magdalena Silitonga; Ferdian Aditya Pratama
Jurnal Elektro Vol 19 No 2 (2026): Jurnal Elektro : October 2026
Publisher : Program Studi Teknik Elektro, Fakultas Biosains, Teknologi, dan Inovasi, Universitas Katolik Indonesia Atma Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v19i2.8319

Abstract

This study proposes a data-driven approach for predicting product quality in plastic injection molding based on actual manufacturing data. The dataset comprises 500 production observations, including 461 non-defective and 39 defective products. The data were divided into training and testing sets using an 80:20 ratio, with SMOTE applied exclusively to the training set to mitigate the substantial class imbalance. Regression analysis was subsequently conducted to identify the most relevant process parameters from five candidate variables. This analysis identified melt end point and melting time as statistically significant predictors, which were then used to develop three classification models: Backpropagation Neural Network (BPNN), Random Forest, and Support Vector Machine. Each model was evaluated over ten experimental repetitions, with Accuracy and AUC serving as the primary performance measures, while Precision, Recall, F1-score, and confusion matrices provided additional evaluation. Across the experiments, BPNN produced the strongest overall predictive performance. The main contribution of this study is the integration of statistically informed feature selection and imbalance treatment with computationally lightweight machine-learning models, demonstrating a practical approach to quality prediction when only a limited amount of real-world manufacturing data is available.

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