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All Journal Indonesian Journal of Electronics and Instrumentation Systems JAREE (Journal on Advanced Research in Electrical Engineering)
Tri Arief Sardjono
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Control & Systems Engineering Electrical & Electronics Engineering

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Semi-Automatic Image Segmentation on X-ray Image of Spine using Active Contour Method Tri Arief Sardjono; Ahmad Fauzi Habiba Chozin; Muhammad Nuh
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 5, No 2 (2021): October
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v5i2.166

Abstract

Currently, many image analysis methods have been developed on X-Ray of scoliotic patients. However, segmentation of spinal curvature is still a challenge, and needs to be improved. In this research, we proposed a semi-automatic spinal image segmentation of scoliotic patients from X-Ray images. This method is divided into 2 steps: preprocessing and segmentation process. A conversion process from RGB to grayscale and CLAHE (Contrast Limited Adequate Histogram Equalization) method was used in image preprocessing. The active contour method was used for the segmentation process. The result shows that segmentation of spinal X-ray images of scoliotic patients using active contour method interactively, can give better results. The average of ME and RAE values are 12.98% and 26.75 %. instead of using the interactive region splitting method which gets 21.17% and 89.27%. Keywords: active contour, interactive segmentation, pre-processing, scoliosis. 
Analisis Photoplethysmography Jarak Jauh dalam berbagai Kondisi Pencahayaan Atar Fuady Babgei; Muhammad Wikan Sasongko; Tri Arief Sardjono
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 12, No 2 (2022): Oktober
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.78715

Abstract

Photoplethysmography (PPG) konvensional untuk mengukur kecepatan jantung memiliki keterbatasan tersendiri, salah satunya yaitu diperlukannya kontak langsung dengan bagian tubuh pasien. rPPG (remote-Photoplethysmography) dapat digunakan untuk melakukan pemantauan jantung dari seorang pasien berbasis citra. Sama halnya dengan sistem lain yang berbasis kamera, algoritma rPPG sangat bergantung pada kondisi pencahayaan. Oleh karena itu diperlukan suatu studi analisis yang mengindahkan aspek tentang pengaruh kondisi dan arah cahaya terhadap subjek yang diamati terhadap hasil estimasi laju denyut jantung dengan algoritma rPPG. Pada penelitian ini diimplementasikan algoritma rPPG dengan Short-Time Fourier Transform (STFT) untuk memperkirakan laju detak jantung dalam berbagai kondisi cahaya. Hasil yang diperoleh merupakan analisa spektral dari perubahan frame video pada area dahi terhadap perubahan waktu dari model input warna Green-Channel dan HSV (Hue, Saturation, Value). Perbandingan dengan pengukuran ground truth pada pencahayaan 260 lux, 19 lux, dan 11 lux, estimasi laju detak jantung yang didapatkan dari input Green Channel menghasilkan persentase error rata-rata 0,038, 0,118, dan 0,229, dimana hasil persentase rata-rata error ini lebih rendah dari masukan HSV, yaitu 0,095, 0,212, dan 0,24.
Comparative Performance of Various Wavelet Transformation for the Detection of Normal and Arrhythmia ECG Signal Mu'thiana Gusnam; Hendra Kusuma; Tri Arief Sardjono
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 7, No 1 (2023): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v7i1.343

Abstract

Cardiac Activity forms a signal of electrical potential waves in the heart that can be recorded using an Electrocardiogram (ECG). The results of the ECG signal can determine the conditions and abnormalities experienced by the heart, such as arrhythmias. Medical personnel diagnoses normal and arrhythmia heart conditions by looking at R peaks and R-R interval features. Normal conditions have regular R peaks and R-R intervals, whereas arrhythmias are irregular. The challenges in diagnosing ECG signals are that sometimes the signal has some noises that need reducing noise (denoising) are not required in the signal so it can be easier to detect abnormalities. This paper is a brief study of the comparison of the best performance in detecting ECG signals using various wavelet transforms and optimal threshold values based on empirical methods to obtain R peaks and R-R interval features. Wavelet transform describes the signals that can compress the ECG signal and reduce noise without losing important clinical information that can be achieved by medical personnel. The wavelet transform is suitable for approaching data with a discontinuity signal, so the frequency component will increase if noise or anomalies occur in the ECG signal. The various wavelet transforms used Daubechies (db4), Symlets (sym4), Coiflets (coif4), and Biorthogonal (bior3.7) with four types of Detail and Approximate levels; they are Level 1, 2, 3, and 4. The comparison result for the best performance of the various wavelet transforms is using Daubechies wavelet, and biorthogonal wavelet with an accuracy percentage of 100% at level 2 for diagnosing arrhythmia and 93.1% at level 1 for normal diagnosis from 31 data for arrhythmia and 18 for Normal sourced of the MIT-BIH Database. Hence, the total accuracy results obtained from all the data tested is 96.55%.
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 Fauzi Habiba Chozin Ahmad Rafiqan Atar Fuady Babgei Hendra Kusuma Mu'thiana Gusnam Muhammad Wikan Sasongko R. Sapto Hendri Boedi Soesatyo Rachmad Setiawan