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All Journal G-Tech : Jurnal Teknologi Terapan Indonesian Journal of Computing, Engineering, and Design Journal of Community Services: Sustainability and Empowerment Jurnal Polimesin
Ariana Tulus Purnomo
Sampoerna University, Indonesia
Arts Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Enhancing Respiratory Disease Diagnosis through FMCW Radar and Machine Learning Techniques Ariana Tulus Purnomo; Raffy Frandito; Edrick Hansel Limantoro; Rafie Djajasoepena; Muhammad Agni Catur Bhakti; Ding-Bing Lin
G-Tech: Jurnal Teknologi Terapan Vol 8 No 1 (2024): G-Tech, Vol. 8 No. 1 Januari 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i1.3693

Abstract

Respiratory diseases require early diagnosis and continuous monitoring, but existing methods involve risky physical contact. This study proposes a new system that uses FMCW radar and machine learning to monitor breathing without contact. FMCW radar can detect respiratory movements in real-time, while machine learning can classify respiratory waveforms. This study evaluates the system with cross-validation Shuffle Split, K-fold, and Stratified K-fold. The results show that Random Forest has the highest accuracy of 94.6% and Naïve Bayes has the shortest time of 0.055 seconds. Shuffle Split performs best overall. This study shows the feasibility and potential of the system for the detection, response, and tracking of respiratory diseases in emergencies.
Introducing Smart Machines Technology and Netiquette for Highschool Students Rahim, Noorfi Azizah; Djajasoepena, Rafie; Purnomo, Ariana Tulus; Setiawan, Iwan; Triawan, Farid; Bhakti, Muhammad Agni Catur; Lestari, Tika Endah; Sulistyo, Eko; Solihin, Unang; Eryanto, Ery; Wandy, Wandy
Journal of Community Services: Sustainability and Empowerment Vol. 3 No. 02 (2023): September 2023
Publisher : Center for Research and Community Service of Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/jcsse.v3i2.388

Abstract

Artificial intelligence is evolving and changing what work means in industrial settings. Smart machines are replacing numerous tasks normally done by human workers. Thus, the young generation needs to be prepared and guided, making them aware of what competencies they should acquire before entering the society. The present community service activity aims to communicate the importance of understanding Smart Machines Technology and Netiquette for Highschool-Students in a public senior high school in Cirebon city, namely SMAN 6 Cirebon. A seminar about smart machines and netiquette was conducted. The young generation frequently uses social networking sites to communicate with friends. Ethics in communication must be appropriately implemented in online platforms. In parallel, a digital library system was developed in the school. Literature review, interviews with digital technology professionals, and discussions with school librarians were carried out throughout this community service activity.
Utilization of Artificial Intelligence to Support the Development of Teaching and Project Modules Djajasoepena, Rafie; Setiawan, Iwan; Bhakti, Muhammad Agni Catur; Purnomo, Ariana Tulus; Ayu, Media Anugerah; Alibasa, Muhammad Johan; Wandy, Wandy
Journal of Community Services: Sustainability and Empowerment Vol. 4 No. 01 (2024): March 2024
Publisher : Center for Research and Community Service of Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/jcsse.v4i1.440

Abstract

Artificial Intelligence (AI) is now commonly used in many sectors, including education. Utilizing AI technology to support learning does not mean replacing the role of educators. The critical role of educators is to teach, educate, and train students to build their skills, knowledge, and morals, which are irreplaceable by AI. SMP Negeri 174 Jakarta is a public junior high school in East Jakarta and would like to receive guidance to upgrade teachers’ competencies in AI. The event lasted half a day and about 39 teachers participated in this agenda. The overall community service activities began in early April 2024 and ended on May 15, 2024, and all main activities were completed 100%. The session was successfully delivered in around two hours with days of preparations. Future recommendation is to include similar topics related to academic activities and technology.
Unleashing the Power of Deep Learning: Revolutionizing Facial Recognition with GhostFaceNets Ariana Tulus Purnomo; Edrick Hansel Limantoro; Muhammad Nafis Aimanurrohman
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6459

Abstract

Facial recognition technology has advanced significantly due to the development of deep learning algorithms. This paper explores deep learning, a branch of machine learning that employs multi-layered neural networks to simulate human decision-making processes in facial recognition. It provides a brief literature review of significant works in various deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The core of the study is the implementation of the GhostFaceNets model, an enhancement of GhostNets, which is specifically designed for efficient and accurate facial recognition. By using Ghost Modules, this model reduces computational redundancy in generating additional feature maps through linear operations. An integrated attention mechanism is used in this study to emphasize critical facial features. Additionally, this study also employs the ArcFace loss function to improve class separation accuracy. The VGG2-FP dataset was used to train and evaluate this model and achieved an accuracy of 94.45%. This study contributes to the evolution of facial recognition systems, particularly in constrained computational environments.
Introducing Artificial Intelligence Utilization in Learning to High School Teachers Djajasoepena, Rafie; Syamsuri, Ady; Nurfais, Ahmad; Bahagia, Katherine Luckman; Kusuma, Felicia; Dewa, Gilang Raka Rayuda; Purnomo, Ariana Tulus; Bhakti, Muhammad Agni Catur; Wandy, Wandy; Triawan, Farid; Githa, Arum; Lestari, Tika Endah; Setiawan, Iwan
Journal of Community Services: Sustainability and Empowerment Vol. 5 No. 01 (2025): March 2025
Publisher : Center for Research and Community Service of Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/jcsse.v5i1.515

Abstract

The development of artificial intelligence (AI) has significantly impacted various sectors, including education. However, based on observation, no AI tool-integrated course has been utilized by teachers at SMA 6 Cirebon. Moreover, based on the pretest assignment, the average understanding of teachers in SMAN 6 Cirebon regarding AI technology was only 55.48%, indicating challenges in implementing AI tools due to a lack of knowledge and practical guidance. To address this issue, a community service activity was held to empower teachers with applicable AI knowledge and skills through a seminar titled "How AI Learns Like a Brain: Implementasi AI dalam Pembelajaran". A qualitative approach was employed, beginning with seminar preparation, AI literature review, and interactive team discussions. Pre- and post-tests showed an increase in understanding of AI technology, with the mean score rising from 55.48% to 67.22% and the median score increasing from 60% to 80%. Finally, this community service recommends ongoing training, the development of AI-integrated lesson plans, hands-on workshops, and collaboration with educational authorities to support the further implementation of AI in teaching.
Machine fault detection through sound analysis using MFCC and machine learning Chang, Steven Henderson; Purnomo, Ariana Tulus; Bhakti, Muhammad Agni Catur; Mulia, Vania Katherine; Rizky, Agyl Fajar; Fernandez, Nikolas Krisma Hadi; Triawan, Farid
Jurnal Polimesin Vol 23, No 3 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v23i3.6653

Abstract

This study addresses the need for automated damage and failure detection in industrial machinery through sound analysis and machine learning. Traditional methods rely on human experts to identify faults using microphones, which can be time-consuming, stressful, and prone to errors such as limited perception, subjectivity, and inconsistency. This study leverages machine learning to create a more objective and efficient alternative. Mel-Frequency Cepstral Coefficients (MFCCs) were employed for feature extraction, capturing intricate sound patterns associated with machinery faults. Through rigorous experimentation, 11 MFCC coefficients were identified as optimal. The Support Vector Machine (SVM) emerged as the best-performing classifier compared to LightGBM and XGBoost, achieving a training accuracy of 83.12% and testing accuracy of 82.50%. The dataset was split between 80% for training and 20% for testing. The small gap between training and testing accuracy indicates an ideal model with no signs of over fitting, under fitting, or data leakage. Real-world simulations validated the model’s efficacy under various operational scenarios, demonstrating its readiness for industrial deployment. This study highlights the effectiveness of sound analysis and SVM classification in proactive maintenance, offering a reliable tool to reduce downtime and maintenance costs while enhancing operational efficiency and reliability.
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
Emotion Recognition in Javanese Music: A Comparative Study of Classifier Models with a Human-Annotated Dataset Septianto, Moh Erwin; Purnomo, Ariana Tulus; Lin, Ding Bing; Kim, Chang Soo
Indonesian Journal of Computing, Engineering, and Design (IJoCED) Vol. 7 No. 2 (2025): IJoCED
Publisher : Faculty of Engineering and Technology, Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/ijoced.v7i2.544

Abstract

With advancements in machine learning and the increasing availability of music datasets, Music Emotion Recognition (MER) has gained significant attention. However, research focusing on Indonesian traditional music, particularly Javanese music, remains limited. Understanding emotions in Javanese music is crucial for preserving cultural heritage and enabling emotion-aware applications tailored to Indonesian traditional music. This study investigates the effectiveness of three well-established machine learning models, 1D Convolutional Neural Networks (1D-CNNs), support Vector Machines (SVMs), and XGBoost, in classifying emotions in Javanese music using a manually annotated dataset. The dataset consists of 100 Javanese songs from various genres, including Dangdut, Koplo, and Campur Sari, annotated based on the Thayer emotion model. The models’ performance was assessed using different data split ratios, with accuracy rates exceeding 70%. Among the tested classifiers, SVM exhibited the highest and most stable accuracy. 
Co-Authors AA Sudharmawan, AA Bahagia, Katherine Luckman Chang, Steven Henderson Dewa, Gilang Raka Rayuda Ding-Bing Lin Edrick Hansel Limantoro Eryanto, Ery Fernandez, Nikolas Krisma Hadi Frandito, Raffy Githa, Arum Iwan Setiawan Kim, Chang Soo Kusuma, Felicia Lestari, Tika Endah Limantoro, Edrick Hensel Lin, Ding Bing Media Anugerah Ayu, Media Anugerah Muhammad Agni Catur Bhakti Muhammad Johan Alibasa Muhammad Nafis Aimanurrohman Mulia, Vania Katherine Noorfi Azizah Rahim Nurfais, Ahmad Raffy Frandito Rafie Djajasoepena Rizky, Agyl Fajar Septianto, Moh Erwin Syamsuri, Ady Triawan, Farid Unang Solihin Wandy Wandy