Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Indonesian Journal of Computing, Engineering, and Design
Frandito, Raffy
Unknown Affiliation
Arts Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Economical 3D-Printed Robotic Arm for Educational Training Purpose Hadisujoto, Budi; Budiarta, Wahyu Nur; Frandito, Raffy; Saptaji, Kushendarsyah; Triawan, Farid; Wibowo, Djati; Ong, Janice
Jurnal Teknologi Vol. 17 No. 2 (2025): Jurnal Teknologi
Publisher : Faculty of Engineering Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/jurtek.17.2.89-100

Abstract

The advancement of Engineering technology requires universities as the frontier to educate engineering students with the latest skillset. Fast progress of Artificial Intelligence (AI) shown by the recently released of open AI such as ChatGPT, Genius, etc., has opened a new trend of technological tools. Hence, it is necessary or it can be said a must to train future engineers how to at least use these tools. Robotics and automation have long been used to assist humans in manufacture, logistic, health, and many other areas. Implementing AI into robotics creates intelligent systems which are predicted to be widely used. However, for educational institutions, especially in developing countries, the cost to afford the training robot equipment is still pricy. Here, we present designing and building of an economical robotic arm using 3D printed parts and open sources. The robot arm has six Degrees of Freedoms (DoF) and capable of lifting about 450 grams of maximum load. Some suggestions include future development are presented.
An FMCW Radar-Based Intelligent System for Non-Contact Detection and Monitoring of Pneumonia Symptoms Purnomo, Ariana Tulus; Frandito, Raffy; Limantoro, Edrick Hensel; Djajasoepena, Rafie; Bhakti, Muhammad Agni Catur; Lin, Ding Bing
Indonesian Journal of Computing, Engineering, and Design (IJoCED) Vol. 6 No. 1 (2024): IJoCED
Publisher : Faculty of Engineering and Technology, Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/ijoced.v6i1.395

Abstract

Pneumonia is one of the most common contagious respiratory diseases, and one of its symptoms is shortness of breath. This symptom underscores the need for non-contact monitoring methods, which our paper addresses by proposing a strategy that uses Frequency-Modulated Continuous Wave (FMCW) radar to extract breathing waveforms and then classifies them with an eXtreme Gradient Boosting (XGBoost) model. The model performs well on our dataset, using stratified k-fold cross-validation and Mel-Frequency Cepstral Coefficients (MFCC) feature extraction. This intelligent system can correctly identify deep and deep-quick breathing patterns with 98% and 87.5% recall scores, respectively. Integrating FMCW and XGBoost offers a promising solution for early detection and real-time monitoring of pneumonia
Co-Authors Ariana Tulus Purnomo Budiarta, Wahyu Nur Hadisujoto, Budi Kushendarsyah Saptaji Limantoro, Edrick Hensel Lin, Ding Bing Muhammad Agni Catur Bhakti Ong, Janice Rafie Djajasoepena Triawan, Farid Wibowo, Djati