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All Journal HAYATI Journal of Biosciences ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sinergitas PkM & CSR KACANEGARA Jurnal Pengabdian pada Masyarakat Building of Informatics, Technology and Science Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Robotics and Control (JRC) Jurnal Teknologi Bahan dan Barang Teknik Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun eProceedings of Engineering Jurnal Rekayasa elektrika
Bayu Erfianto
Universitas Telkom
Aerospace Engineering Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences

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Enhancing Contactless Respiratory Rate Measurement Accuracy: Integration of 24GHz FMCW Radar and XGBoost Machine Learning Arisandy, -; Erfianto, Bayu; Setyorini, -
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2654

Abstract

Advancements in non-contact vital sign monitoring are crucial for enhancing patient measurements' accuracy and overall patient experiences. This research explores the integration of 24GHz Frequency-Modulated Continuous-Wave (FMCW) radar with the XGBoost machine learning algorithm to improve the detection of respiratory rate (RR). This innovative approach offers a promising alternative to traditional contact-based methods. The study utilizes FMCW radar to detect respiratory motion, while signal patterns are analyzed using XGBoost to ensure accuracy across various healthcare environments. The method involves collecting signals, pre-processing to remove noise and irrelevant data, and extracting features to be analyzed by the XGBoost algorithm. The collected dataset, which includes controlled and randomized respiratory rates from a diverse subject pool, establishes a solid basis for the algorithm's training and validation, ensuring extensive adaptability and precision. Empirical results show that XGBoost surpasses other machine learning models' accuracy and reliability. Importantly, this method significantly reduces error margins compared to established benchmarks, leading to substantial improvements in RR measurement. The implications of this study are wide-ranging, indicating that such a system could significantly enhance patient care standards by providing continuous, accurate, and non-intrusive monitoring, especially in settings where traditional methods are impractical or uncomfortable. Future research should aim to refine the system's real-world applicability, assess long-term reliability, and optimize the technology for integration into existing healthcare frameworks, thereby further transforming the landscape of patient monitoring technologies.
Pembelajaran interaktif melalui game edukatif selama masa pandemi di TK AL Ghifari Bandung Suryani, Vera; Erfianto, Bayu; Rakhmatsyah, Andrian; Yulianto, Fazmah Arif
KACANEGARA Jurnal Pengabdian pada Masyarakat Vol 5, No 1 (2022): Januari
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/kacanegara.v5i1.979

Abstract

Kurikulum sekolah TK meliputi aspek motorik,maupun kognitif bagi anak usia pra-sekolah. Pembelajaran daring selama pandemi membuat anak cepat bosan, karena masa konsentrasi mereka belum bisa lama seperti halnya orang dewasa. Dibutuhkan perangkat bantu agar penyampaian materi lebih menarik dan tidak membosankan. Permainan merupakan salah satu cara penyampaian materi agar anak TK dapat belajar secara menyenangkan. Game edukatif merupakan perangkat bantu yang bersifat menarik bagi anak TK, dan melalui game ini materi pembelajaran dapat disampaikan secara menarik. Tujuan kegiatan pengabdian masyarakat ini ialah melakukan penyuluhan kepada guru PAUD TK Al-Ghifari Sukabirus mengenai cara penyampaian materi pembelajaran melalui game edukatif, serta pelatihan kepada anak TK mengenai cara penggunaan game edukatif tersebut. Game edukatif bertujuan untuk meningkatkan motivasi belajar melalui story telling, peningkatan konsentrasi, serta aspek computational thinking untuk usia pra-sekolah. Dari hasil pelaksanaan kegiatan pengabdian masyarakat di TK Al Ghifari Bandung di dapatkan bahwa perangkat dan modul pembelajaran yang diberikan sangat membantu proses belajar mengajar di TK Al Ghifari. Poin utama yang disasar adalah aspek motivasi dan konsentrasi anak usia TK.
Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation Erfianto, Bayu; Rahmatsyah, Andrian
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.889

Abstract

Air quality monitoring is a process that determines the number of pollutants in the air, one of which is indoor air quality. The Fuzzy Indoor Air Quality Index was developed in this research. It is a method for determining the indoor air quality index using sensor fusion and fuzzy logic. By combining several different time series determinants of air quality, a fuzzy logic-based sensor fusion method is used to build a knowledge base about indoor air quality levels. Without the use of complicated calculation models, fuzzy logic-based fusion will make it easier to determine indoor air quality levels based on various sensor parameters. The input for fuzzy-based data fusion is obtained from the ARIMA method with Kalman Filter's air quality parameter values estimation. The application of ARIMA with a Kalman Filter was used to improve the accuracy of indoor air quality estimation in this study. ARIMA(3,1,3) had a MAPE of 0.1 percent on the CO2 dataset, and ARIMA(1,0,1) had a MAPE of 0.63 percent on the TVOC dataset based on approximately three experimental days. ARIMA (3,1,3) estimation with a Kalman Filter results in a MAPE of 0.03 percent for the CO2 dataset and a MAPE of 0.24 percent for ARIMA(1,0,1) Kalman Filter estimation on TVOC dataset. As a result, the Fuzzy Indoor Air Quality Index (FIAQI) developed in this research reasonably estimates indoor air quality. This can be seen by examining the percentage of estimation errors obtained from the experiment.
Co-Authors Adhan Mulya Rahmawan Adysti Adrianne Andrian Rahmatsyah Andrian Rakhmatsyah Angga Juliat Saputra Arisandy, - Bedy Purnama Benedictus Augusta Vianney Benedictus Augusta Vianney Cahyani, Niken Dwi Catur Wirawan Wijiutomo Fauzi, Roki Fazmah Arif Y, Fazmah Gita Indah Hapsari Ilhamdaniah Ilhamdaniah Ilhamdaniah, Ilhamdaniah Indrarini Dyah Irawati Intan Muchtadi-Alamsyah Irma Yunita Masturi Juntriesta, Vera Khoiri, Isfa' Bil Maman Abdurohman Mas'ud Adhi Saputra Monterico Adrian Muhammad Hanif Abdillah Novian Anggis Suwastika Nugraha, Shinta Jitny Ayu Nusantara, Hadi Dharma Pahlevi , Rizka Reza Parman Sukarno Prihatni, Delita Rahmatsyah, Andrian Rahmawan, Adhan Mulya Ramzy Rashaun Arif Rendy Munadi Satrio Nurcahya SETIAWAN, ALDRY HERNANDA Setyorini Setyorini, - Shandikri, Rheza Sidik Prabowo Vera Juntriesta Vera Suryani Wirawan, Ilo Raditio