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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JAREE (Journal on Advanced Research in Electrical Engineering)
Rachmad Setiawan
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Perbandingan Algoritma Levenberg-Marquardt dengan Metoda Backpropagation pada Proses Learning Jaringan Saraf Tiruan untuk Pengenalan Pola Sinyal Elektrokardiograf Rahmat Rahmat; Rachmad Setiawan; Mauridhi Hery Purnomo
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2006
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Pengenalan pola EKG (Elektrokardiograf) merupakan suatu proses yang penting dalam menganalisa keaadan jantung pasien. Makalah ini menjelaskan tentang sebuah system pengenalan pola sinyal EKG menggunakann multi layer perseptron dan dilatih dengan algoritma Levenberg–Marquardt (LM). Algoritma (LM) merupakan pengembangan algoritma Backpropagation (BP) standar. Pada algoritma BP standar proses update bobot dan bias menggunakan negative gradient descent secara langsung sedangkan pada algoritma Levenberg-Marquardt menggunakan pendekatan matrik Hesian.Pada penelitian ini dilakukan pengenalan terhadap lima jenis kelainan sinyal EKG yang berasal dari generator sinyal EKG secara on-line. Pra-proses dimulai dengan mengurangi noise yang menyertai sinyal EKG dengan teknik downsampling Haar wavelet, kemudian diambil spektrum frekuensi hasil downsampling ini untuk masukan Jaringan Saraf Tiruan.Hasil learning jaringan dengan konfigurasi 100-15-5 dan error limit 0.0001 dibutuhkan 35 iterasi untuk mencapai konvergen pada LM, sedangkan dengan BP dibutuhkan 480 iterasi. Pengujian data learning dengan LM menghasilkan error terkecil 1 X 10-6 dan error terbesar terbesar 1 X 10-4, untuk metoda BP error terkecil dicapai 1 X 10-4 dan error terbesar 1 X 10-3.Pengujian dengan data uji dari generator yang sama untuk 100 kali pengujian semua pola yang diuji dapat dikenali 100%.Kata Kunci: Levenberg-Marquardt, Backpropagation, Jaringan Saraf Tiruan, Elektrokardiograf
A Computer Vision Approach for Non-contact Psychophysiological Assessment: Speaking-Aware Video-Based Stress Detection Using Extended TSST Protocol Ahmad Rafiqan; Rachmad Setiawan; Tri Arief Sardjono
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 1 No. 10 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.556

Abstract

The assessment of psychological stress through non-contact computer vision methods faces significant challenges when facial expressions are affected by motions caused by speech. In this paper, we introduce a novel video-based stress detection system with speaking awareness that dynamically processes facial features according to online detection of speech activities. The system employs an extended 41-minute Trier Social Stress Test protocol with a controlled baseline, moderate stress induction, peak stress increase, and recovery stages to comprehensively record the dynamics of stress development. Facial landmark detection is handled by MediaPipe Face Mesh and provides 468 three-dimensional landmarks with sub-pixel accuracy, while the speaking-aware processing strategy dynamically selects the most appropriate feature sets: seven robust ones during speech-containing intervals like face movement, eye aspect ratio, and forehead wrinkles, and nine full-featured ones during silent intervals with additional mouth and jaw metrics. The adaptive strategy addresses the intrinsic limitation of traditional facial analysis, which treats speaking and non-speaking states homogeneously. Deployment on the Raspberry Pi CM4 edge computing device enables real-time operation with privacy preservation via localized processing. Empirical testing with 71 participants illustrates strong performance with an F1-score of 83.16%, outperforming conventional methods by 4.24%. Cross-population validation affirms good generalization potential across various populations for the entire 41-minute protocol, thereby demonstrating the system's applicability to real-world applications in stress tracking across healthcare, educational, and workplace well-being contexts.
Co-Authors Ahmad Rafiqan Mauridhi Hery Purnomo Rahmat Rahmat Tri Arief Sardjono