Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Cermin Dunia Kedokteran
Wijaya, Bryan
Unknown Affiliation
Computer Science & IT Education Health Professions

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

Found 3 Documents
Search

 1 
Implementasi Sistem Tanya Jawab Berbasis Skenario untuk Mendukung Proses Akademik dengan IBM Watson Assistant Toba, Hapnes; Wijaya, Bryan
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 6, No 2 (2020): Volume 6 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v6i2.40715

Abstract

Dalam makalah ini disampaikan sebuah hasil penelitian dengan memanfaatkan teknologi dari IBM, yaitu Watson Assistant. Watson Assistant digunakan untuk membuat chatbot terkait proses akademik. Analisis dan pengumpulan data dilakukan dengan berbasiskan skenario. Data-data tersebut dibuat ke dalam sebuah graph search. Watson Assistant akan menentukan node dengan nilai kepercayaan tertinggi untuk diberikan sebagai jawaban. Skenario percakapan yang ditanamkan dalam chatbot ini telah diimplementasikan ke dalam bentuk laman web, Facebook Messenger, dan Slack untuk membantu interaksi antara pihak fakultas dengan mahasiswa. Chatbot berperan pula sebagai sistem pendamping forum tanya jawab di dalam course learning system (CLS) untuk pertanyaan-pertanyaan rutin. Berdasarkan hasil uji coba, chatbot berbasis skenario telah dapat menjawab kebutuhan dasar mahasiswa untuk bertanya seputar hal akademis, sebagaimana tercantum dalam buku panduan, khususnya untuk proses perwalian dan deskripsi mata kuliah.
Application of Support Vector Machine in Measuring Stress Levels Based on EEG Signals Wijaya, Bryan; Sitanggang, Delima; Lee, Brandon; Angie, Vicky; Siahaan, Eric Simon Giovanni
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 1 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i1.6584

Abstract

This study aims to classify stress levels based on electroencephalography (EEG) signals using the Support Vector Machine (SVM) algorithm. The data used in this study came from 21 subjects with a total of 379 datasets, which included the main variables of Subject, Electrode Channel (E), Theta, Beta 1, and Beta 2. Preprocessing was done to ensure data quality, including blank data elimination, normalization, and feature engineering. One of the main features developed was the Beta Average, which was obtained by calculating the average between Beta 1 and Beta 2, and stress level classification, which was determined based on the comparison between the Beta Average and Theta. The SVM algorithm was applied to build a stress classification model with an initial stage of manual calculation to understand the basic concepts, followed by the Python programming language implementation. The evaluation results show that the developed model has an accuracy of 92.76%, with the highest precision, recall, and f1-score values reaching 100% and the lowest value of 85%. The confusion matrix analysis showed that the model could classify low stress with 100% accuracy, while it reached 87.8% for high stress. The findings of this study prove that the SVM algorithm effectively classifies EEG signal-based stress levels. This model can be the basis for further development of stress detection methods, especially in mental health and neuroinformatics applications.
Efektivitas Vaksin Influenza Berbasis mRNA dalam Meningkatkan Imunitas dan Potensinya Sebagai Pelindung Anak dari Risiko Pneumonia: Analisis Wijaya, Bryan; Setiawan, Fiona Valencia; Firmansyah, Yohanes
Cermin Dunia Kedokteran Vol 52 No 6 (2025): Kesehatan Jiwa
Publisher : PT Kalbe Farma Tbk.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55175/cdk.v52i6.1619

Abstract

Influenza viruses have posed a global threat since the 19th century, presenting serious challenges and threats to public health worldwide. Influenza viruses are among the major seasonal outbreaks impacting health and the economy yearly. Numerous new strains emerge over time due to the antigenic drift of influenza viruses, which undergo mutations. Therefore, vaccines and medical technologies continuously need innovative approaches to stimulate immune responses effectively. mRNA-based influenza vaccines are potentially more effective because of the inclusion of a broader range of antigens that can enhance cellular immunity or expand protection beyond just hemagglutinin (HA) and neuraminidase (NA), they can incorporate more than four antigens (HA) in a single formulation, offering advantages in effectiveness, safety, and rapid large-scale production. mRNA-based influenza vaccines hold significant potential and are expected to benefit children due to their various advantages.
Co-Authors Angie, Vicky Delima Sitanggang, Delima Hapnes Toba Lee, Brandon Setiawan, Fiona Valencia Siahaan, Eric Simon Giovanni Yohanes Firmansyah