Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer dan Informatika Brilliance: Research of Artificial Intelligence MDP Student Conference
Budi, Raden George Samuel
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Analisis Sentimen Masyarakat terhadap Kasus Korupsi PT. Timah Menggunakan Metode Support Vector Machine Caroline, Fionna; Budi, Raden George Samuel; Rivan, Muhammad Ezar Al
Jurnal Ilmu Komputer dan Informatika Vol 4 No 1 (2024): JIKI - Juni 2024
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jiki.141

Abstract

Korupsi adalah penyalahgunaan jabatan publik untuk keuntungan pribadi yang dimana korupsi ini dapat memberikan kerugian besar bagi negara maupun masyarakat. Topik yang dipilih untuk penelitian ini adalah kasus korupsi PT. Timah yang sedang hangat dibicarakan dikarenakan kerugian negara yang mencapai 271 T. Untuk membantu analisis dalam penelitian ini, dibangunlah sebuah sistem yang dapat mendeteksi sentimen publik yang sudah dikumpulkan dari platform Youtube dengan metode Support Vector Machine. Model yang sudah dilatih dengan dataset akan diseimbangkan dengan SMOTE karena tidak meratanya kelas klasifikasi. Model klasifikasi yang telah dibangun dengan support vektor machine mendapatkan hasil presisi pada sentimen negatif 91% dan sentimen positif 44%, recall pada sentimen negatif 96% dan sentimen positif 22%, F1-Score pada sentimen negatif 93% dan sentimen positif 30%, serta jumlah sample pada kelas sentimen negatif 140 dan kelas sentimen positif 18.
Aplikasi Create History Stok Barang pada PT. Singa Perkasa Abadi Budi, Raden George Samuel; Hartati, Ery
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11184

Abstract

PT. Singa Perkasa Abadi is a company engaged in the production and sale of iron materials, such as nails, long iron, light steel and barbed wire. Currently, the process of recording the expenditure and entry of goods still uses paper notes, which have the potential to cause errors in recording and calculating stock of goods, and delays in managing stock of goods, and when shipping goods only relying on delivery letters which can cause errors during shipping of goods. This study aims to develop a website-based digitalization solution for stock management in order to reduce errors and loss of recording, increase efficiency and accuracy of recording. By implementing the Iterative development method and using the Laravel and Bootstrap frameworks. The implementation of this system can increase the company's productivity in managing stock of goods more quickly, accurately, and in an organized manner.
Detection Of Left-Behind Bags Based On YOLOv11 And DeepSORT Budi, Raden George Samuel; Wijaya, Novan
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7349

Abstract

Incidents of bags being left behind in public facilities such as transportation hubs, offices, and educational environments continue to pose security challenges, especially when monitoring relies solely on human operators. To address the limitations of manual CCTV observation, this study presents an automated system capable of identifying abandoned bags by integrating the YOLOv11n detection model with the DeepSORT tracking algorithm. The dataset used consists of 1000 annotated bag images, combined with a pre-trained YOLOv11 human detector. Prior to training, image preprocessing and augmentation were applied to ensure that the model remained robust under varying illumination, distance, and viewpoint conditions. Model training was carried out in Google Colab using PyTorch with 20 epochs, a learning rate of 0.002, and a batch size of 8. Experimental results indicate that YOLOv11n delivers strong detection performance, achieving a mAP@0.5 of 0.787, a precision score of 0.837, a recall of 0.690, and an F1-Score of 0.755. When combined with DeepSORT, the system operates efficiently in real time, reaching an average of 28.30 FPS with a latency of 35.34 ms per frame. The system effectively distinguishes bags that are separated from their owners through correlation analysis between human and bag movements. Overall, the proposed approach is capable of supporting real-time surveillance needs, although future enhancement of dataset diversity and adaptive thresholding is recommended to improve detection in more complex environments.
Co-Authors Caroline, Fionna Hartati, Ery M Ezar Al Rivan Novan Wijaya