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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 139 Documents
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Sistem Pemilahan Barang Berdasarkan Deteksi Label Menggunakan Vision Sensorr Henry, Carolus; Mulyadi, Melisa; Ghozali, Theresia; Wijayanti, Linda; Indriati, Kumala
Jurnal Elektro Vol 17 No 1 (2024): Jurnal Elektro: April 2024
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v17i1.5407

Abstract

Sorting goods based on the results of checking packaging labels is an important process in controlling production quality in industry. Many industries still carry out manual sorting and label checking processes, which results in low productivity levels and is susceptible to human error. This research develops an automation system for sorting goods based on label inspection using vision sensors, programmable logic controller (PLC), and robot arm. The system controlled by a PLC will read and detect damage to packaging labels by using VeriSens vision sensor and sort them using a robot arm according to predetermined stock keeping unit (SKU) categories, namely SKU 1, SKU 2, SKU 3, SKU 4, and rejected goods. The pneumatic system is used as an actuator to push goods onto the conveyor, moving the robot arm with three degrees of freedom and vacuum. Detection is carried out by applying the edge detection concept to read text, images and code that are reprocessed with the VeriSens Application Suite software. The success rate of the goods sorting system reached 90% with a reading speed of 0.389 seconds and a work process duration ranging from 21.54 seconds to 28.99 seconds.
Comparison of Actual Results and PVSyst Simulation in the Design of Off-Grid Solar Power Generation System (PLTS) in Karuni Village, Southwest Sumba Siregar, Marsul; Pardosi, Cristoni Hasiholan; Bachri, Karel Octavianus; Nur, Tajuddin; Pandjaitan, Lanny W.
Jurnal Elektro Vol 17 No 1 (2024): Jurnal Elektro: April 2024
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v17i1.5419

Abstract

This research aims to compare the actual production with the simulations using the PVSyst software for the Off-Grid Solar Power Plant (PLTS) in Karuni Village, Southwest Sumba. The Off-Grid PLTS in Karuni Village is a vital solution for improving remote areas' electricity access. Actual energy production data from the PLTS were obtained from monitoring systems, while simulation results were obtained through PVSyst. The analysis results indicate a difference of approximately 10% between the actual and simulated results. It observed that it is influenced by variability in local weather conditions, maintenance, system management levels, and limitations of the simulation model. The implications of this research emphasize the importance of using accurate data in simulations, improving PLTS system maintenance, and developing more sophisticated simulation models. Recommendations for further research include further analysis of factors influencing the differences in results. This study provides valuable insights into the planning and management of Off-Grid PLTS. It offers perspectives on enhancing the accuracy of future PLTS system planning and management.
Perbandingan Algoritma Machine Learning menggunakan Orange Data Mining untuk Klasifikasi Jenis Kendaraan pada Sistem Tilang Digital Pranadjaya, Egipta; Pangestu, Evan Sudira; Sereati, Catherine Olivia; Octaviani, Sandra; Darmawan, Marten
Jurnal Elektro Vol 17 No 1 (2024): Jurnal Elektro: April 2024
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v17i1.5429

Abstract

This paper discusses the application of the Orange Data Mining application to compare several machine learning algorithms for classifying vehicle types in digital ticket systems. This research compares and analyzes the logistic regression algorithm, Support Vector Machine (SVM) and Neural Network (NN) to solve vehicle classification problems in digital traffic tickets. The research results show that in the training process and based on the dataset used, the algorithms that have the highest level of accuracy are Logistic Regression, Neural Network and Support Vector Machine. Meanwhile, during the testing process, all algorithms in the model were able to carry out classification with 100% accuracy
Perancangan Aplikasi Koperasi Berbasis Android Jones, Danzel Maxdriel Samuel; Sereati, Catherine Olivia; Christanto, Henoch Juli; Wijayanti, Linda; Mulyadi, Melisa
Jurnal Elektro Vol 17 No 2 (2024): Jurnal Elektro: Oktober 2024
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v17i2.5453

Abstract

The development of information technology cannot be separated from the use of mobile applications, which are increasingly varied. Mobile applications allow users to easily connect to internet services anywhere. Cooperatives are joint business entities that generally still use the manual method for recording members, transactions and so on, which can lead to human errors and time inefficiencies. The existence of a website-based cooperative mobile application that is implemented on smartphones using the Xamarin.Forms framework can reduce these problems. Smartphones will make online cooperatives more effective because they are portable, easy to use and more affordable.
PENGUKURAN BACKUP TIME UNINTERRUPTIBLE POWER SUPPLY UNTUK PENENTUAN KAPASITAS BATERAI VRLA Jong, Susanto; Melisa Mulyadi; Wijayanti, Linda
Jurnal Elektro Vol 18 No 1 (2025): Jurnal Elektro: April 2025
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v18i1.6264

Abstract

A battery is a device that stores power in an Uninterruptible Power Supply (UPS) system. The most common type of UPS battery found today is Valve Regulated Lead Acid (VRLA). So that the UPS can work optimally according to its capacity and backup time, it is necessary to determine the battery capacity. In this research, the capacity of the VRLA battery on a particular UPS was determined based on direct testing and based on calculations. VRLA battery capacity calculations refer to the UPS and battery datasheet. The calculation stage starts from calculating the current and battery power used according to the load power absorbed by the UPS and referring to the table of constant current discharge and constant power discharge characteristics. Determining battery capacity based on testing is done by measuring the backup time of the UPS. The calculation results and test results directly show that there is a difference in backup time because the battery used is an old stock battery. The research results also prove that determining battery capacity by calculation can ensure that the battery capacity is more appropriate or not too large so it is more economical.
Analisis Performansi Jaringan Saraf Dalam terhadap Dataset Digit Berderau Bachri, Karel Octavianus
Jurnal Elektro Vol 18 No 1 (2025): Jurnal Elektro: April 2025
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v18i1.6613

Abstract

This work investigates the impact of noise on model performance by training a neural network on a digit dataset with varying Signal-to-Noise Ratios (SNR) to assess its resilience and generalization ability. The experimental setup involved training the model on datasets with noise levels ranging from clean images to highly distorted ones (SNR 5%–25%), analyzing accuracy, mini-batch loss, and training time. Results indicate that while the model achieves high accuracy (96.88%) at mild noise levels (SNR 5%), performance declines significantly at higher noise levels, with accuracy dropping to 78.91% at SNR 25%. The analysis of mini-batch loss and training time reveals that noise slows convergence and increases computational complexity. The confusion matrix further confirms that while the model effectively distinguishes between classes, noise-induced misclassifications become more frequent at lower SNRs. These findings emphasize the importance of noise reduction techniques and data preprocessing to improve model robustness in real-world applications.
Classification Of Multi-Class Face Expression Using Modification Of VGG-16 Model Priyatama, Aryadana; Sugiyanto, Sugiyanto
Jurnal Elektro Vol 18 No 1 (2025): Jurnal Elektro: April 2025
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v18i1.6653

Abstract

In the era of modern technology, facial recognition has become an important application in various fields, such as security, education and health. One method used to recognize faces is a Convolutional Neural Network (CNN), specifically the VGG-16 architecture which is known for its consistent performance. But even though CNN can recognize faces, its accuracy in recognizing faces is inadequate. This research aims to increase the accuracy of facial expression classification so that it is more optimal by modifying the CNN VGG-16 architecture. This research uses GridSearch techniques, K-Fold Cross Validation, and utilizes multiple datasets. The dataset used consists of two image datasets, namely SMIC and SAMM facial-micro expressions, each of which has been normalized and converted to a grayscale scale measuring 48x48 pixels. The GridSearch process is applied to optimize parameters such as the number of filters, learning rate, dropout rate, activation function, and batch size. The K-Fold Cross Validation technique with five folds was used to ensure the generalization of the model to new data. The research results show that this modification is able to achieve validation accuracy of up to 98.31% in the training process, showing a significant improvement compared to the standard method. And showed an increase in accuracy in testing of 98.04% in research.
Deep Learning-Based Brain Tumor Classification Using Convolutional Neural Network Sasita, Naumi; Futri Zalzabilah Ray; M.Fauzanil Wildan A.R.; Tantowi Hutagalung; Rokhmat Febrianto
Jurnal Elektro Vol 18 No 1 (2025): Jurnal Elektro: April 2025
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v18i1.6658

Abstract

An essential noninvasive medical diagnostic technique is magnetic resonance imaging (MRI), which is particularly useful for identifying brain cancers. While earlier algorithms proved effective on smaller MRI datasets, their performance suffered on bigger datasets. This study addresses the need for a swift and reliable brain tumor classification system capable of sustaining optimal performance across comprehensive MRI datasets. The convolutional neural network is implemented using the Keras library, incorporating the ResNet50 architecture as a pre-trained model. The ResNet50 model is fine-tuned for the specific brain tumor classification task, with a Global Average Pooling layer, dropout, and a final dense layer with softmax activation. Data augmentation techniques are employed to enhance the model’s robustness, including rotation, width and height shifts, and horizontal flips. The training process involves optimizing the model using the Adam optimizer with a learning rate of 0.0001. Early stopping, learning rate reduction on plateau, and model checkpointing are implemented as callbacks to ensure efficient training and prevent overfitting. The proposed model achieves a remarkable accuracy of 99.28 percent after 15 epochs. The classification task involves distinguishing among four classes: glioma, meningioma, pituitary, and no tumor.
Design and Implementation of a Vision-based Wheeled Mobile Robot Using HSV Color Segmentation and P-D Control Aditama, Wira; Herianto, David; Fernando, Nico; Henry, Carolus; Budiyanta, Nova Eka
Jurnal Elektro Vol 18 No 1 (2025): Jurnal Elektro: April 2025
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v18i1.6892

Abstract

This study presents the design and implementation of a wheeled mobile robot capable of detecting and tracking a ping-pong ball using vision-based processing. The system integrates a Raspberry Pi 3 Model B+ as the main controller, a Raspberry Pi Camera Rev 1.3 for visual input, and DC motors driven by an L298N motor driver for actuation. Object detection is achieved through color segmentation in the HSV color space using the OpenCV library, followed by morphological filtering and contour analysis. A proportional-derivative (PD) control algorithm is employed to adjust motor speeds dynamically based on the ball's horizontal position in the frame. The experimental results demonstrate that the robot can successfully detect and follow a ping-pong ball, although it exhibits limitations in processing speed and motion stability. The average frame rate during operation was 5 FPS, which is sufficient for basic tracking tasks but suboptimal for high-speed applications. This project highlights the feasibility of vision based robotic systems for simple object tracking tasks.

Page 14 of 14 | Total Record : 139

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