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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jusikom: Jurnal Sistem Informasi Ilmu Komputer INTI Nusa Mandiri REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Brilliance: Research of Artificial Intelligence Sisfo: Jurnal Ilmiah Sistem Informasi The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Rizal, Reyhan Achmad
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Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Mechanical Engineering Other

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Classification Of Hypertension Using K-Nearest Neighbor Based On Photoplethysmograph Data And Blood Pressure Estimator Sinaga, Jasmin William Natanael; Tampubolon, Tasya Rouli Christy; Simanjuntak, Ester Farida; Sitanggang, Delima; Rizal, Reyhan Achmad
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): Juli
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/zswzf122

Abstract

Hypertension is a persistent cardiovascular condition, often termed the “silent killer” because it typically presents no symptoms in its early stages. To address the shortcomings of traditional blood pressure monitoring methods, this study develops a classification system that leverages photoplethysmography (PPG) signals in combination with the K-Nearest Neighbor (KNN) algorithm. PPG provides a promising non-invasive solution that is readily adaptable to portable devices. The classification process employs the Euclidean Distance method to determine the similarity between new data samples and previously labeled instances. Data were collected from 276 individuals spanning various age groups using PPG sensors connected to the MR-IAT Robot Covid platform. The system categorizes individuals into normotensive, prehypertensive, stage 1, and stage 2 hypertension groups. The study evaluates the performance of the KNN algorithm based on its ability to predict blood pressure categories from morphological features extracted from the PPG signals. Ultimately, the outcomes of this research are expected to advance the development of efficient, real-time, continuous blood pressure monitoring systems through user-friendly machine learning approaches.
Real Time Chicken Egg Size Classification Using Yolov4 Algorithm Sandy, Cut Lika Mestika; Husna, Asmaul; Rizal, Reyhan Achmad
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4496

Abstract

The common problem currently faced by MSMEs producing chicken eggs is experiencing difficulties in grouping egg sizes every day. Currently, grouping egg sizes is still done manually, this is less than optimal and prone to errors so that many business owners often experience losses. Grouping egg sizes before being sold is very important to note because each size affects the selling price of eggs. The use of technology on a MSME scale in laying hen farmers has not been widely adopted, this is due to limited access and understanding of technology so that to improve and strengthen productivity, management, and marketing in this business, technological innovation is needed. One alternative solution to deal with this problem is to build a real-time computerized system that can group eggs according to their size. This study aims to evaluate the performance of the Yolov4 algorithm in grouping egg sizes based on their size in real time. Based on the results of the tests carried out, the Yolov4 algorithm is able to group chicken eggs in real time with an F1-Score value: 0.89 where the F1-Score value approaching 1 indicates that the system performance has been running well. The results of this classification can be used to create a real-time egg size grouping application that can help MSMEs to monitor the productivity of chicken eggs every day.
Effectiveness of Using ArchiCAD in Interactive 3D Visualization in Building Drawing Engineering Learning Media Syahputra, Dinur; Sandy, Cut Lika Mestika; Sukiman, T. Sukma Achriadi; Manurung, Ericky Benna Perolihin; Rizal, Reyhan Achmad
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6342

Abstract

This study aims to analyze the effectiveness of using ArchiCAD software as a tool for interactive 3D visualization in the context of learning media for building drawing engineering. Traditional methods of teaching technical drawing often rely on two-dimensional representations, which can limit students’ spatial understanding and comprehension of complex architectural forms. By integrating ArchiCAD, a Building Information Modeling (BIM)-based software, students are exposed to a more immersive and realistic learning experience, enabling them to visualize construction elements more clearly. The research employs a quasi-experimental method involving two groups: an experimental group using ArchiCAD-based interactive media and a control group using conventional methods. Data collection was conducted through pre-tests and post-tests, as well as student perception questionnaires. The results indicate a significant improvement in the learning outcomes of the experimental group, both in terms of cognitive understanding and design skills. Furthermore, student responses show a high level of satisfaction and engagement when using 3D interactive media. These findings suggest that ArchiCAD can be effectively implemented as a digital learning medium in vocational and technical education settings, especially in the field of architectural drawing. The study recommends broader integration of BIM-based tools to support competency-based learning and enhance the quality of engineering education.
Co-Authors -, Mauliza -, mutammimul -, Syarkawi Aisyah - Andy Chandra Asmaul Husna Bintang, Lidya Haryati Delima Sitanggang, Delima Ericky Benna Perolihin Manurung Giovano, Steven Girsang, Imron Sanjaya Harly, Jesslyn HS, Christnatalis Jonathan, Jovi Manik, Brian Fernanda Manurung, Ericky Benna Perolihin Mutammimul Ula Nababan, Marlince Prasetiyo, Sidik Apriyadi Sandy, Cut Lika Mestika Sayed Fachrurrazi Sembiring, Lysheeba Abbygail Simanjuntak, Ester Farida Sinaga, Jasmin William Natanael Sukiman, T. Sukma Achriadi Susanto, Mario Syahputra, Dinur Tampubolon, Tasya Rouli Christy Ulva , Ananda Faridhatul Yennimar, Yennimar Zulfikri, Abdi