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All Journal International Journal of Informatics and Computation Jurnal Pengembangan Sistem Informasi dan Informatika
Better Swengky
Politeknik Manufaktur Negeri Bangka Belitung
Computer Science & IT Social Sciences

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Vehicle Theft Detection Using YOLO Based on License Plates and Vehicle Ownership Bradika Almandin Wisesa; M. Hizbul Wathan; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Silvia Agustin; Better Swengky
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.105

Abstract

Detection of vehicle theft requires innovative approaches to address an increasing number of cases in Indonesia. This study presents a YOLOv11-based system for detecting vehicle theft by combining real-time object detection with a vehicle ownership database. The proposed system identifies license plates, detects vehicle owners using facial recognition, and analyzes suspicious activity to determine theft occurrences. The proposed method can produce model effectiveness with an accuracy = 70%. Key improvements in architecture, including enhanced feature fusion and dynamic anchor assignment, contribute to the object’s detection in complex environments. This research can be a potential technique to provide efficient, scalable, and real-time security solutions in dynamic surveillance applications.
A GAN-Based Approach for Identifying Fake Accounts on Twitter Zain, M Syafrizal; Swengky, Better; Wisesa, Bradika Almandin; Putri, Vivin Mahat
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 1 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i1.2671

Abstract

The multiple security threats on the network make the need for robust security measures a major concern. The increasing presence of fake accounts and malicious actors on online platforms poses significant challenges, requiring sophisticated detection techniques to maintain network integrity. To address these issues, we propose a novel method for detecting fake accounts by leveraging Generative Adversarial Networks (GANs). By analyzing data extracted from platform APIs, our approach leverages the unique characteristics of GANs to improve the accuracy and efficiency of the detection process. In this study, we develop a GANs-based model specifically designed to detect fake accounts. The model is built through several key stages: first, we collect a comprehensive dataset, then perform data processing and preprocessing to make it suitable for machine learning applications. Next, the model is trained using various hyperparameters to optimize accuracy, thus learning the underlying patterns associated with fake accounts. After the training stage, the model is tested on previously unseen data to evaluate its generalization and performance in real-world scenarios. Experimental results show that our model achieves a threshold value of 0.0054779826. This value plays a crucial role in determining the accuracy of the detection system. The smaller the threshold value, the higher the model accuracy, as it shows a lower error rate in distinguishing between real and fake accounts. The ability of GANs-based models to adaptively learn from data during the training process contributes to high precision in detecting anomalies as well as minimizing false positives.
Implementasi Convolutional Neural Network (CNN) dalam Diagnosa Penyakit Daun Padi Berdasarkan Citra Digital Irawan, Indra; Wathan, M.Hizbul; Swengky, Better; Ramadani, Ardi
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2756

Abstract

This study investigates the implementation of Convolutional Neural Network (CNN) in classifying rice leaf diseases based on digital images. The model classifies three types of diseases: Bacterial Leaf Blight, Rice Blast, and Rice Tungro Virus. A dataset of 240 images was obtained from Kaggle, with 80 images per class. Four training scenarios were applied using 25, 50, 75, and 100 epochs. Preprocessing steps included resizing all images to 150x150 pixels and normalizing pixel values. Evaluation results show that classification accuracy increases with the number of training epochs. The best model was achieved at 100 epochs, yielding a validation accuracy of 91.67% and testing accuracy of 92%. These results demonstrate that CNN is effective in diagnosing rice leaf diseases and can support early detection efforts to strengthen national food security.
Klasifikasi Mata Katarak dan Mata Normal Menggunakan Algoritma Dasar Convolutional Neural Network (CNN) Swengky, Better; Wathan, M Hizbul; Irawan, Indra; Aulia, Rosaura
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2758

Abstract

Eye diseases encompass a wide range of conditions, from mild visual impairments to complete blindness, with cataracts being one of the leading causes. Despite advances in medical imaging, automated classification of cataract versus normal eye images remains a challenging task. This study proposes a classification method using a Convolutional Neural Network (CNN) to distinguish between cataract-affected eyes and normal eyes accurately. The approach involves collecting and preprocessing a labeled dataset, extracting features such as color and vein patterns (including average RGB values), and training the CNN model with optimized parameters. Experimental results demonstrate that the proposed model achieves a high classification accuracy of 95.1%. These findings indicate that CNN-based image classification is a promising tool for supporting automated cataract detection and early diagnosis
InfusCare: Smart Infusion Monitoring System with Real-Time Notifications via ESP32 and Blynk Wathan, M Hizbul; Irawan, Indra; Swengky, Better; Cahyadi, Irsan
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2759

Abstract

Manual infusion monitoring in medical settings can lead to errors and delays in care steps that can put patients at risk. Internet of Things (IoT) technology provides a solution that enhances the accuracy and efficiency of real-time infusion monitoring. This study develops an IoT-based infusion monitoring system with the HX711 module and ESP32 microcontroller, using a connected load cell sensor as a monitoring interface through the Blynk application. This system can accurately measure the volume of infusion fluid and provide automatic notifications when the fluid volume approaches the minimum limit. Tests were conducted with infusion fluid simulation, load cell sensor calibration, and system calibration integration testing. The test results indicate that the system can display data on fluid weight in real time with an accuracy level of 98.5%, and when the fluid volume reaches 1 second in average response time, you can send notifications at the right time. Therefore, this system is expected to be implemented in various medical facilities as a solution for patient safety and the effectiveness of infusion care, as well as for automatic and reliable infusion monitoring.
Co-Authors Aulia, Rosaura Bradika Almandin Wisesa Cahyadi, Irsan Evvin Faristasari Indra Irawan Indra Irawan Putri, Vivin Mahat Ramadani, Ardi Silvia Agustin Sirlus Andreanto Jasman Duli Wathan, M.Hizbul Zain, M Syafrizal