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All Journal JIKO (Jurnal Informatika dan Komputer) International Journal of Informatics and Computation Jurnal Pengembangan Sistem Informasi dan Informatika Journal of Dinda : Data Science, Information Technology, and Data Analytics J-Intech (Journal of Information and Technology) Journal Of Artificial Intelligence And Software Engineering
Bradika Almandin Wisesa
Politeknik Manufaktur Negeri Bangka Belitung
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science 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.
Analisis Sentimen pada Ulasan Produk dengan SVM dan Word2Vec ANDRIYANI, WIDYASTUTI; Astuti, Yuli; Wisesa, Bradika Almandin; Hengki, Hengki
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 1 (2025): Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i1.1498

Abstract

Analisis sentimen adalah salah satu cabang pemrosesan bahasa alami (NLP) yang bertujuan untuk mengidentifikasi opini dalam teks. Penelitian ini mengusulkan model analisis sentimen dengan menggunakan kombinasi Word2Vec sebagai teknik representasi fitur dan Support Vector Machine (SVM) sebagai algoritma klasifikasi. Dataset yang digunakan adalah Amazon Customer Reviews, dengan 500 ribu sampel ulasan produk yang dilabeli sebagai sentimen positif atau negatif. Model yang diusulkan dibandingkan dengan baseline seperti Naive Bayes dan Logistic Regression, yang menggunakan representasi fitur berbasis TF-IDF.Hasil evaluasi menunjukkan bahwa SVM dengan Word2Vec menghasilkan akurasi 91.3\%, precision 90.8\%, recall 92.1\%, dan F1-score 91.4\%, lebih unggul dibandingkan model baseline. Grafik Precision-Recall Curve dan ROC Curve memperkuat temuan bahwa Word2Vec memberikan representasi fitur yang lebih informatif, yang secara signifikan meningkatkan performa SVM dalam tugas klasifikasi teks.Penelitian ini membuktikan efektivitas kombinasi Word2Vec dan SVM untuk analisis sentimen pada dataset besar dan kompleks. Pendekatan ini relevan untuk berbagai domain, seperti e-commerce dan analisis opini di media sosial, serta membuka peluang untuk pengembangan lebih lanjut menggunakan model berbasis transformer.
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.
Penerapan YOLOv11 untuk Penghitungan Otomatis Jumping Jack pada Video Latihan Fisik Wisesa, Bradika Almandin; Putri, Vivin Mahat; Faristasari, Evvin; Duli, Sirlus Andreanto Jasman; Irawan, Indra; Agustin, Silvia
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.v6i3.2795

Abstract

The Jumping Jack Counter is an image processing-based application developed to automatically count the number of jumping jack movements in exercise videos. This study aims to implement the YOLOv11 model to detect and count jumping jack movements by analyzing body posture. YOLOv11 is utilized to identify body positions categorized into two main classes: "open" (arms and legs spread apart) and "closed" (arms and legs together). The dataset consists of 15,000 video frames collected from various exercise videos, with research stages including data collection, data labeling, preprocessing, model training, and testing. The results demonstrate that YOLOv11 achieves a 92% accuracy rate in counting jumping jack movements. These findings are expected to assist coaches and users in monitoring physical exercise in real-time, thereby enhancing training effectiveness. The majority of movement detections (78%) were for the open position, followed by the closed position (20%), with 2% detection errors attributed to lighting variations or camera angles. [1].
Laplacian Kernel and Deep Learning for Palmprint Classification Duli, Sirlus Andreanto Jasman; Wisesa, Bradika Almandin; Faristasari, Evvin; Peprizal, Peprizal; Putri, Vivin Mahat; Fadila, Resma
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6978

Abstract

Palmprint classification is a robust biometric method for personal identification due to its uniqueness and stability. This study explores the use of deep learning combined with the Laplacian Kernel and Deep Morphological Processing Network (DMPN) for palmprint classification. We trained the proposed system on a dataset of palmprint images collected from 10 participants, each contributing 10 palm images. The results demonstrated that the model achieved an accuracy of 90%, with weighted precision, recall, and F1-score all at 0.9007, indicating a well-balanced classification performance. Additionally, the model achieved a weighted precision of 0.9045, emphasizing its ability to minimize false positives. The average Equal Error Rate (EER) of 0.0917 indicates an effective balance between the false acceptance rate (FAR) and false rejection rate (FRR). The system was tested under various conditions, including different orientations, lighting, and backgrounds, demonstrating its robustness in real-world scenarios. This study also compares the results with recent palmprint classification techniques, such as deep learning, GANs, and few-shot learning, and discusses potential improvements, including incorporating multi-spectral data fusion and few-shot learning to enhance performance in real-world applications.
Preventive Attendance Record using Photo from Mobile Phone and Printed Paper using CNN Wisesa, Bradika Almandin; Mahat Putri, Vivin; Faristasari, Evvin; Jasman Duli, Sirlus Andreanto; Agustin, Silvia
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1927

Abstract

Face-based attendance systems are increasingly popular for their ease of use, but they are susceptible to fraud, such as using photos or videos for unauthorized attendance. This study introduces a digital attendance system that combines facial recognition with liveness detection powered by Convolutional Neural Networks (CNN). Liveness verification is achieved by analyzing subtle movements and responses to ambient lighting. The dataset includes 30 facial images, encompassing both authentic and fraudulent samples. Testing demonstrates a facial recognition accuracy of 91.3% and effective spoofing detection in static and dynamic settings. This system provides a secure, fraud-resistant attendance solution ideal for educational and corporate settings. Further enhancements are suggested to improve performance across diverse facial expressions and lighting conditions.
Illegal Motorcycle Parking Detection in The Car Area Isnaeni, Nenen -; Wisesa, Bradika Almandin; Lisda, Lisda; Febrianto, Dany Candra
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.1948

Abstract

Illegal motorcycle parking in designated car areas at Politeknik Manufaktur Negeri Bangka Belitung (Polman Babel) disrupts campus parking management, reduces space availability, and poses safety risks. This paper proposes an automated detection system using computer vision and license plate recognition to identify motorcycles parked in car areas and notify their owners via WhatsApp and email alerts. The system integrates CCTV cameras with YOLOv11 for vehicle detection and EasyOCR for license plate recognition, coupled with a database for owner identification. Upon detection, owners receive immediate notifications to rectify the violation. Experiments in Polman Babel’s parking lot show a 94% accuracy in motorcycle detection and 88% in license plate recognition under diverse conditions. The system enhances parking enforcement efficiency, reduces manual intervention, and supports smart campus initiatives. This work offers a scalable, cost-effective solution adaptable to other institutions facing similar parking challenges.
Developing an NLP-Based Chatbot for Waste Management Education in Sungailiat Wisesa, Bradika Almandin; Mahat Putri, Vivin; Faristasari, Evvin; Jasman Duli, Sirlus Andreanto; Lionza, Rahmat
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7522

Abstract

Penelitianinimemaparkanpengembangan dan evaluasi menyeluruh terhadap chatbot berbasis Natural Language Processing (NLP) yang dirancang untuk meningkatkan pendidikan pengelolaan sampah di Bank Sampah Sungailiat, Indonesia. Dengan mengintegrasikan logika fuzzy untuk pencocokan Pertanyaan yang Sering Diajukan (FAQ) secara akurat dan memanfaatkan model NLP berbasis transformer, DialoGPT-medium, chatbot inimemberikan respons yang relevan secara kontekstual terhadap pertanyaan pengguna mengenaioperasional bank sampah, termasuk pemilahan sampah, proses daur ulang, dan insentif ekonomi. Penelitian ini menangani masalah rendahnya kesadaran masyarakat terhadap praktik pengelolaan sampah yang tepat, yang menghambat partisipasi efektif dalam program daurulang. Sistem hibrida ini mencapai akurasi respons sebesar 85% dalam p engujian pengguna, divalidasi melalui analisis matriks konfusi yang mendetail. Temuan utama menunjukkan peningkatan signifikan dalam keterlibatan pengguna, retensi pengetahuan, dan kesadaran masyarakat, menunjukkan potensi chatbot sebagai solusi pendidikan lingkungan yang berbasis teknologi dan dapat diskalakan untuk konteks serupa di seluruh Indonesia
Co-Authors Andriyani, Widyastuti Better Swengky Dany Candra Febrianto Evvin Faristasari Fadila, Resma Hengki Hengki Indra Irawan Jasman Duli, Sirlus Andreanto Lionza, Rahmat Lisda Lisda Nenen Isnaeni Peprizal, Peprizal Putri, Vivin Mahat Silvia Agustin Silvia Agustin Sirlus Andreanto Jasman Duli Wathan, M.Hizbul Yuli Astuti Zain, M Syafrizal