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All Journal International Journal of Informatics and Computation Journal of Innovation Information Technology and Application (JINITA) Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Teknologi Computing and Education Technology Journal Kenduri: Jurnal Pengabdian Dan Pemberdayaan Masyarakat
Better Swengky
Politeknik Manufaktur Negeri Bangka Belitung
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Social Sciences Other

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 1 
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.
Rancang Bangun Sistem Monitoring Kesehatan Balita Berbasis Aplikasi Mobile pada Posyandu Sripemandang Aulia, Rosaura; Fujiyanti, Linda; Swengky, Better
Jurnal Teknologi Vol 25, No 3 (2025): Desember 2025
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/teknologi.v25i3.8367

Abstract

The rapid advancement of information technology has significantly impacted various sectors, including healthcare. One of the community health services that requires digital innovation is Posyandu, which plays a vital role in monitoring the growth and development of toddlers. The manual data recording process often causes problems such as data loss and delayed information delivery. This research aims to design and develop a mobile-based information system for Posyandu to simplify the process of recording, managing, and monitoring toddler health data in an integrated manner. The development method used is the Prototyping Method, which allows iterative system development based on user requirements. Data were collected through interviews and direct observations at Posyandu Sripemandang to identify user needs, followed by system design using UML diagrams (Use Case, Activity, Sequence, and Class Diagrams). The implemented system includes main features such as child registration, digital medical records, growth charts, Posyandu schedules, and health information updates. System testing was conducted using User Acceptance Testing (UAT) and Blackbox Testing methods to evaluate functionality and user satisfaction. The test results show an index value of 85.48%, indicating that the system is highly feasible and that all features function as intended. Overall, the system effectively improves efficiency, accuracy, and accessibility of toddler health data management for midwives, Posyandu cadres, and parents in Posyandu Sripemandang.
Rancangan Sistem Informasi Perawatan Preventif Berbasis Website di Bengkel Mekanik Politeknik Manufaktur Negeri Bangka Belitung Christianty, Licia; Irwan, Irwan; Swengky, Better; Napitupulu, Robert
Jurnal Teknologi Vol 25, No 3 (2025): Desember 2025
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/teknologi.v25i3.7858

Abstract

Machine maintenance plays a crucial role in ensuring the smooth operation of practical activities at the Mechanical Workshop of Politeknik Manufaktur Negeri Bangka Belitung. The current manual maintenance process often causes scheduling delays, recording errors, and difficulties in monitoring machine conditions. This study aims to design and implement a web-based preventive maintenance information system to enhance the efficiency and effectiveness of machine management. The development process applies the Agile method using the Laravel framework and MariaDB as the database management system. The system includes key features such as machine data management, maintenance scheduling, submission and approval workflows, automated email notifications, integration with the Google Calendar API, and maintenance and spare part reporting modules. The system was tested using the black box testing method, which confirmed that all functions operated according to user requirements. Questionnaire results from laboratory staff, lecturers, and students indicated that the system improved scheduling consistency, data recording accuracy, and the effectiveness of notification features in reminding users of maintenance schedules. Approximately 50% of respondents agreed that the system successfully manages maintenance scheduling in a structured and computerized manner, while the notification feature helps reduce the risk of maintenance negligence. Therefore, the developed system serves as an effective digital solution to support preventive maintenance management in vocational education environments
Pelatihan Pembuatan Bunga dari Kawat Bulu sebagai Upaya Peningkatan Kualitas SDM dalam Pembelajaran Seni di Sekolah Dasar Helda Susianti; Dora Palupi; Yus Dwi Yanti; Mahmudin Mahmudin; Muhamad Galy Njoman Ari Pribowo; Better Swengky; Amanda Saputri
KENDURI : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 6 No. 1 (2026): January-April
Publisher : Yayasan Darussalam Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62159/kenduri.v6i1.2106

Abstract

Arts and crafts learning in elementary schools (ES) is often considered less interesting due to the minimal use of interactive learning media, resulting in low student interest and understanding. This community service program aims to improve the quality of arts learning and student skills through training in making flowers from wire feathers as a creative and applicable medium. The method used is a participatory approach through socialization, demonstrations, direct practice, and mentoring for teachers. This activity was carried out from November - December 2025. The results of the activity showed an increase in student creativity, skills, and interest in learning. In addition, this provided innovation in developing interesting and contextual learning media. This activity also instilled the value of sustainability by utilizing simple, environmentally friendly materials. Overall, this community service had a positive impact in creating more interactive, enjoyable, and meaningful arts learning for students elementary schools (ES).
RiceGuard: A Lightweight PSO-Optimized MobileNetV2 Framework for Stable Rice Leaf Disease Classification M. Hizbul Wathan; Better Swengky; Indra Irawan; Fatir Atthariq Zami
Computing and Education Technology Journal Vol 6, No 1 (2026): APRIL
Publisher : Pendidikan Komputer FKIP Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/cetj.v6i1.18663

Abstract

Rice leaf diseases such as blast, brown spot, bacterial blight, and tungro pose a serious threat to agricultural productivity, with potential yield losses exceeding 50% under epidemic conditions. Therefore, rapid and accurate early detection is essential to support sustainable food security and precision agriculture. This study proposes a rice leaf disease classification system based on the MobileNetV2 architecture optimized using Particle Swarm Optimization (PSO). A dataset consisting of four rice leaf disease classes, collected from public repositories and sources representing real field conditions, is used to evaluate the proposed approach. Transfer learning is employed to improve training efficiency, while PSO is applied to optimize key hyperparameters to enhance model stability and convergence.Experimental results show that MobileNetV2 optimized with PSO consistently achieves superior classification performance and improved training stability compared to the standard MobileNetV2 baseline. The baseline MobileNetV2 achieves 92% accuracy, with the highest F1-score of 0.99 on the tungro class and the lowest performance of 0.88 on the blast class. In contrast, MobileNetV2–PSO demonstrates a significant improvement, reaching 99% accuracy, with F1-scores of 0.98 for bacterial blight, 0.98 for blast, 0.99 for brown spot, and 1.00 for tungro. The largest improvement occurs in the blast class, with a 10-point increase in F1-score, indicating that PSO optimization provides greater sensitivity to complex disease patterns that were previously difficult to classify.These findings indicate that the proposed framework provides an accurate and lightweight solution with strong potential for deployment on mobile and resource-constrained platforms to support intelligent rice disease diagnosis.
Development of Prototype IoT-Based Control and Monitoring System for Pakcoy Better Swengky; Lesta; Peprizal; Rian Ardiansyah; Yosar Leman; Aldian Kurniawan
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3247

Abstract

This research aims to design and develop an Internet of Things (IoT)-based automatic control and monitoring system for hydroponic plants. This system measures, displays, and regulates nutrient parameters, pH, and water volume in real-time to improve water use efficiency, nutrient stability, and plant productivity. The method used is Research and Development (R&D) with a prototyping model. This system integrates an ESP32 microcontroller with a DFRobot pH sensor, a TDS (Total Dissolved Solids) sensor, and an ultrasonic sensor to monitor water levels. Data from the sensors is processed and sent to a cloud database to be monitored and controlled through a website interface. The test results show that the system has high accuracy, with a MAPE (Mean Absolute Percentage Error) value for the pH sensor of 6.06% (93.94% accuracy), a TDS sensor of 4.81% (95.19% accuracy), and an ultrasonic sensor of 7.98% (92.02% accuracy). All MAPE values ​​are within the <10% range, indicating good system performance for automated monitoring applications. This system implementation provides an innovative solution to the limitations of conventional, manual hydroponic systems. It can serve as an initial model for the future development of IoT-based precision agriculture.
Co-Authors Aldian Kurniawan Amanda Saputri Aulia, Rosaura Bradika Almandin Wisesa Cahyadi, Irsan Christianty, Licia Dora Palupi Evvin Faristasari Fatir Atthariq Zami Indra Irawan Indra Irawan Irwan Irwan Lesta LINDA FUJIYANTI M. Hizbul Wathan Mahmudin Mahmudin Muhamad Galy Njoman Ari Pribowo Peprizal Ramadani, Ardi Rian Ardiansyah Robert Napitupulu Silvia Agustin Sirlus Andreanto Jasman Duli Susianti, Helda Vivin Mahat Putri Yosar Leman Yus Dwi Yanti Zain, M Syafrizal