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All Journal Journal JCONES Jurnal Teknik Elektro ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Prosiding Seminar Nasional Sains dan Teknologi Terapan Jurnal Leverage, Engagement, Empowerment of Community (LeECOM) ASAWIKA: Media Sosialisasi Abdimas Widya Karya EKOBIS ABDIMAS : Jurnal Pengabdian Masyarakat Jurnal Teknik Elektro
Ira Puspasari
STMIK STIKOM SURABAYA
Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Social Sciences Other

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Implementasi Pengamanan Transmisi Sinyal EKG (Elektrokardiogram) secara Daring dengan Metode Anonimasi JUSAK, JUSAK; SETIAWAN, BRAMASTA AGNANDA; SOLEHUDIN, SONY; PUSPASARI, IRA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 7, No 1: Published January 2019
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v7i1.85

Abstract

ABSTRAKData World Health Organization (WHO) pada tahun 2014 menunjukkan bahwa di Indonesia sebanyak 37% dari seluruh penyebab kematian adalah penyakit yang berhubungan dengan jantung. Kehadiran teknologi dan pemanfaatan Internet of Things (IoT) diharapkan dapat membantu mengurangi resiko kematian akibat penyakit jantung tersebut. Pada penelitian ini, pengukuran dan pengamatan sinyal jantung melalui tele-auskultasi sinyal elektrokardiogram (EKG) dilakukan. Untuk mengamankan sinyal EKG dalam proses transmisi melalui jaringan Internet digunakan metode anonimasi sinyal berbasis algoritma Jusak-Seedahmed. Hasil pengujian menunjukkkan bahwa algoritma Jusak-Seedahmed dapat melakukan proses anonimasi dan proses rekonstruksi sinyal dengan baik. Pengujian korelasi silang antara sinyal hasil rekonstruksi dan sinyal EKG asli sebelum anonimasi menghasilkan korelasi sebesar 1 pada lag=0. Sinyal EKG hasil rekonstruksi ditampilkan dalam aplikasi mobile untuk memudahkan analisis oleh dokter.Kata kunci: elektrokardiogram, keamanan, anonimasi, IoT, FFT ABSTRACTBased on the latest data released by the World Health Organization in 2014, deaths caused by cardiovascular disease in 2012 have reached 37% of the total number of non-communicable diseases deaths in Indonesia. Therefore, it is expected that the applications of the Internet of Things (IoT) might be used to reduce the risk of death due to the heart related problems. In this research, a tele-auscultation technique for measuring and monitoring electrocardiogram (ECG) signal was built. To secure transmission of the ECG signal over the Internet, we implemented a recently proposed Jusak-Seedahmed algorithm. Our examinations showed that the algorithm performed the anonymization and reconstruction processes well. Crosscorrelation analysis showed that correlation between the reconstructed and the original ECG signal at lag=0 was 1. Furthermore, a mobile-based application had been built to display the reconstructed ECG signal for further analysis.Keywords: electrocardiogram, security, anonimization, IoT, FFT
Prediksi Jarak Bola pada Citra Kamera Katadioptrik menggunakan metode Artifical Neural Network PERMANA, ZENDI ZAKARIA RAGA; RASMANA, SUSIJANTO TRI; PUSPASARI, IRA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 9, No 2: Published April 2021
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v9i2.279

Abstract

ABSTRAKSaat ini, kecerdasan buatan memungkinkan untuk dikembangkan dalam dunia robotika, khususnya untuk pengaturan gerakan robot berdasarkan pengolahan citra. Penelitian ini mengembangkan sebuah mobile robot yang dilengkapi dengan kamera katadioptrik dengan sudut pandang 3600. Citra yang didapatkan, dikonversi dari RGB menjadi HSV. Selanjutnya disesuaikan dengan proses morfologi. Nilai jarak yang terbaca oleh kamera (piksel) dengan jarak sebenarnya (cm) dihitung menggunakan Euclidean Distance. Nilai ini sebagai ekstraksi ciri data jarak yang dilatihkan pada sistem. Sistem yang dibuat pada penelitian ini memiliki iterasi sebanyak 1.000.000, dengan tingkat kelinieran R2=0.9982 dan keakuratan prediksi sebesar 99,03%.Kata kunci: Robot, HSV, Euclidean Distance, Kamera katadioptrik, Artifical Neural NetworkABSTRACTRecently, artificial intelligence is possible to be developed in robotic, specifically for robot movements control based on image processing. This research develops a mobile robot with a 3600 perspective catadioptric camera is equipped. The camera captured images were converting from RGB to HSV. Furthermore, it adapted to the morphological process. The distance value read by the camera (pixels) to the actual distance (cm) is measured using Euclidean Distance. This value is a feature extraction of distance data that has training on the system. The system built in this study has 1,000,000 iterations, with a linearity level of R2 = 0.9982 and prediction accuracy of 99.03%.Keywords: Robot, HSV, Euclidean Distance, Catadioptric Camera, Artifical Neural Network
Webinar Perkembangan AI dan Robotika di Industri Kesehatan Puspasari, Ira; Kusumawati, Weny Indah
Ekobis Abdimas Vol 6 No 1 (2025): Juni 2025
Publisher : Fakultas Ekonomi, Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/ekobisabdimas.5.1.10346

Abstract

The development of artificial intelligence (AI) and robotics has revolutionized various aspects of modern healthcare, including diagnosis, surgery, and rehabilitation. The webinar, titled "Developments in AI and Robotics in the Health Industry," was organized by the Dinamika University Computer Engineering Undergraduate Study Program as a form of community service to increase technological literacy in the health sector. This activity aimed to introduce AI technology and medical robotics and explain the application and ethical, technical, and policy challenges in its implementation. The webinar, which was held online via Zoom, was attended by 160 participants from various backgrounds, such as students, lecturers, health practitioners, and technology developers. The material covered the evolution of robotic systems, the level of autonomy of medical robots, and the potential integration of AI for clinical decision-making. Survey results showed that participants were very enthusiastic and highly rated the relevance of the theme, the competence of the speakers, and the usefulness of the material. This positive feedback from the participants reassures the audience about the quality and relevance of the webinar's content, instilling confidence in the potential of AI and robotics in healthcare. The webinar also had a significant impact on interdisciplinary research, inspiring new collaborations and open discourse on academic cooperation. The webinar also encouraged the utilization of AI and robotics technology in the Indonesian health system, contributing to the digital transformation of the health sector and expanding public understanding of the latest medical technology innovations.
WORKSHOP IMPLEMENTASI IOT “KENDALI TANDON AIR” DI SMAN 1 TAMAN SIDOARJO Weny Indah Kusumawati; Susanto, Pauladie; Harianto, Harianto; Praktikno, Heri; Puspasari, Ira
Asawika : Media Sosialisasi Abdimas Widya Karya Vol 10 No 03 (2025): Desember: Asawika
Publisher : LPPM Unika Widya Karya Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Program Pengabdian Kepada Masyarakat (PKM) ini bertujuan untuk meningkatkan pengetahuan dan keterampilan siswa-siswi di SMAN 1 Taman Sidoarjo mengenai Teknologi Informasi dan Komunikasi (TIK), dengan fokus pada Internet of Things (IoT). Selama empat sesi, workshop ini memberikan pengalaman praktis kepada peserta tentang sistem pengendalian dan pemantauan tandon air menggunakan mikrokontroler ESP32, sensor level air, sensor jarak laser, serta komunikasi data berbasis MQTT. Siswa-siswi dilibatkan secara langsung dalam proses merancang, membangun, dan menguji sistem kendali air yang berbasis IoT. Dari kegiatan ini, dihasilkan dua set alat “Kendali Tandon Air” yang dapat digunakan, buku panduan, dan peningkatan yang signifikan dalam minat serta pemahaman peserta tentang teknologi IoT. Hasil evaluasi melalui kuesioner menunjukkan bahwa peserta merasa puas, dan pihak sekolah sangat mendukung jika kegiatan serupa diadakan lagi di masa yang akan datang.
Externally Validated Deep Learning Model for Multi-Disease Classification of Chest X-Rays Kusumawati, Weny Indah; Permana, Zendi Zakaria Raga; Puspasari, Ira
Jurnal Teknik Elektro Vol. 17 No. 2 (2025)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v17i2.29892

Abstract

Accurate classification of chest X-ray (CXR) images is vital for early detection of thoracic diseases such as COVID-19, Tuberculosis, and Pneumonia, particularly in regions with limited radiological expertise. While deep learning has shown promise in CXR interpretation, many existing models rely solely on internal datasets, risking overfitting and poor generalizability. Furthermore, inadequate tuning of network architectures may limit robustness across varied imaging conditions. This study presents an externally validated deep learning framework based on Convolutional Neural Networks (CNNs) for multi-disease CXR classification. This study compared a baseline CNN with two convolutional layers against a tuned architecture with three layers across multiple image resolutions (64×64, 112×112, 224×224). The proposed model employs transfer learning with a pre-trained CNN, fine-tuned for four-class classification using a softmax output layer. Training was performed with the Adam optimizer (learning rate: 0.0001, batch size: 32) and categorical cross-entropy loss, for up to 50 epochs with early stopping. Internal validation showed the tuned model outperformed the baseline, achieving 0.97 accuracy and an F1-score of 0.89. External validation confirmed superior generalizability, with the tuned model attaining an F1-score of 0.83 and an AUC of 0.97 at 112×112 resolution, compared to the baseline’s F1-score of 0.79 and AUC of 0.94. These results highlight the potential of optimized CNN architectures as reliable, scalable tools for radiological decision support in resource-limited healthcare systems. Future work will incorporate explainable AI methods and real-world clinical validation to ensure safe, interpretable deployment.
Co-Authors Adilayahya, Mochammad Norca Al Gaufiqy, Muh Harist Asmoro, Yudo Christian Andrean Pradigdya Christoforus Surjoputro Effendy, Edwin David Eka Sari Oktarina Ekinasti, Anggi Tiara Citra Febriani, Suci Firdaus, Deddy Mohammad Fransiscus Daso, Fransiscus Harianto Harianto Harianto, Gaguk Helmy Widyantara Hermawan, Adji Jaya, Toni Setiawan Jusak Irawan Jusak Jusak Khoirul, Mochammad Kurnia, Lassa Nusti Ma'arif, Muhammad Nur Hidayat Nugraha, Hasbi Fardian Oktaviano, Toni Abriyanto Pauladie Susanto PERMANA, ZENDI ZAKARIA RAGA Prabandaru, Bayu Praktikno, Heri Prastyawan, Afif Wahyu Prastyo, Pangky Ari Wibowo Pratama, I Gede Brahmanda Ekamartahadi Putra, Dwi Elmik Winata Rachmawati Oktaria Mardiyanto, Rachmawati Oktaria Safii, Cahya Anugrah Christian Sembiring, Rinawati SETIAWAN, BRAMASTA AGNANDA SETIAWAN, BRAMASTA AGNANDA Setyawan, Ian Mahesa Gilang Shochibah Yatimatul Asmak, Shochibah Yatimatul SOLEHUDIN, SONY SOLEHUDIN, SONY Sugiarto, Wahyu Rokhman Sugiharto, Tutus Sukamto, Ika Sumiyarsi Sultan, Duma Reza Ahmad Surobramantyo, Jagadlanang Susijanto Tri Rasmana Tandiallo, Joseph Masarani Viranda, Rizkyana Surya Wangi, Mega Pandan Weny Indah Kusumawati Widipratama, Mohamad Zulfi Yosefine Triwidyastuti