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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi IKRA-ITH Informatika : Jurnal Komputer dan Informatika INTEK: Informatika dan Teknologi Informasi JURNAL KAJIAN TEKNIK MESIN JURNAL TEKNOLOGI INFORMASI Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas) G-Tech : Jurnal Teknologi Terapan Jurnal Sistem Komputer dan Informatika (JSON) Indonesian Journal of Computing, Engineering, and Design Aiti: Jurnal Teknologi Informasi Jurnal Restikom : Riset Teknik Informatika dan Komputer Seminar Nasional Inovasi Teknologi Journal of Community Services: Sustainability and Empowerment Jurnal Polimesin
Muhammad Agni Catur Bhakti
Sampoerna University
Religion Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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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.
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
Internet Data Consumption During Synchronous Teaching-from-Home Period at Sampoerna University Muhammad Agni Catur Bhakti; Wandy Wandy; Ajeng Nindi Noviarini
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 4 (2022): Juni 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i4.4121

Abstract

Since the beginning of Covid-19 in Jakarta, synchronous teaching with Internet communication technology and asynchronous teaching supported by the Learning Management System (LMS) have been adopted at Sampoerna University (SU). Synchronous teaching that initially used a web-based conference technology was later changed to using a communication platform that the University has subscribed to. Lecturers were provided with 15GB (15,360MB) of data per month to support these teaching activities. This research aimed to analyze whether the Internet data quota provided by the Government was sufficient to support the teaching process during this research period. This research used a quantitative method, recorded primary data for three months, and descriptive data with non-probability sampling. It was discovered that only 9,281.34MB of data was being used (60.43%) and still had 6,078.66MB (39.57%) remaining. The conclusion that can be drawn was that the Internet quota provided by the Government was adequate to cover one month of synchronous teaching for three courses with three credits each.
Lung cancer prognosis based on salivary biomarkers using Graph Convolutional Networks Gilang Raka Rayuda Dewa; Raisa Imani Sani; Ady Syamsuri; Charles Agustin; Muhammad Agni Catur Bhakti; Ariana Tulus Purnomo
AITI Vol 23 No 2 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i2.245-260

Abstract

Lung cancer remains the leading factors that incur cancer-related deaths worldwide, mainly due to late-stage detection. In 2022, lung cancer affected almost 2.5 million people, with mortality of more than 1.8 million. However, existing prognostic methods are typically invasive, costly, and time-consuming, hindering effective early detection. Therefore, this research proposes a non-invasive prognostic approach using salivary biomarkers to detect lung cancer via Graph Convolutional Networks (GCNs). By transforming features into graph node representations, the proposed algorithm can model feature dependencies and topological relationships, enabling more effective pattern recognition than conventional classifiers. The proposed algorithm also applies feature selection to reduce computational complexity. The evaluation results show that the proposed algorithm achieves 95.65% accuracy, a macro F1-score of 95.62%, and a Matthews Correlation Coefficient of 0.9434. A comparative analysis shows that the proposed algorithm outperforms other graph-based architectures in terms of classification performance and computational complexity.
Co-Authors AA Sudharmawan, AA Aditiya Harjon Ady Syamsuri Agustin, Charles Ajeng Nindi Noviarini Andini, Verrani Yusti Ariana Tulus Purnomo Arifin, Jauharul Azween Abdullah Bahagia, Katherine Luckman Chang, Steven Henderson Charles Agustin Darmawanto, Gabrielle Orla Ding-Bing Lin Edrick Hansel Limantoro Effendi, Nicholas Lasca Effendy, Muhammad Ikhsan Eko Sulistyo Ery Eryanto Eryanto, Ery Faruq, Zaidan Fadlurohman Fernandez, Nikolas Krisma Hadi Frandito, Raffy Gilang Raka Rayuda Dewa Githa, Arum Gunawan, Daisy Christina Gunawan, Samuel Theodore Halim, Enrico Hanum, Az Zahra Azizah Harditya, Arya Ilham, Oryzea Sativa islam, sri susilawati Iwan Setiawan Kinantya, Valereyna Jingga Kusuma, Felicia Lestari, Tika Endah Liman, Surya Danusaputro Limantoro, Edrick Hensel Lin, Ding Bing Low Tan Jung Media Anugerah Ayu, Media Anugerah Mileniawati, Indah Riantantri Muhamad Rausyan Fikri Muhammad Johan Alibasa Mulia, Vania Katherine Noorfi Azizah Rahim Nurfais, Ahmad Nurprihatin, Filscha Putra, Jeremy Triutomo Putri Yuni Lestari Raffy Frandito Rafie Djajasoepena Raisa Imani Sani Rizky, Agyl Fajar Stephen, Daniel Syamsuri, Ady Tarigan, William Hazkel Teddy Mantoro Tombak Matahari Triawan, Farid Unang Solihin Wandy Wandy