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All Journal Indonesian Journal of Electronics and Instrumentation Systems JAREE (Journal on Advanced Research in Electrical Engineering) Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Tri Arief Sardjono
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy 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%.
Voice Command Recognition Using CNN-LSTM Parallel Architecture Santoso; Tri Arief Sardjono; Djoko Purwanto
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 15 No 1: Februari 2026
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v15i1.23855

Abstract

A parallel convolutional neural network–long short-term memory (CNN–LSTM) architecture is introduced for voice command recognition, designed to simultaneously extract spatial and temporal features from speech signals. Conventional serial architectures process these components sequentially, which can lead to the loss of temporal information after CNN-based spatial compression. This study aimed to improve recognition performance by preserving complementary spectral and temporal representations through parallel feature modeling. In the proposed approach, the CNN branch extracted spectral features from Mel-frequency cepstral coefficients (MFCCs), while the LSTM branch independently modeled long-term temporal dependencies from the same input stream. The outputs from both branches were fused through concatenation to form a comprehensive acoustic representation enhancing discrimination between phonetically similar commands. The model was trained and evaluated using a dataset containing eight classes of spoken commands. During training, the proposed model achieved a loss of 0.0186 and an accuracy of 99.87%, indicating effective learning. On the validation and test datasets, the model reached an accuracy of 89.16%, demonstrating stable convergence and consistent generalization performance. Evaluation using precision, recall, and F1 score metrics confirmed balanced recognition across classes, with particularly high accuracy for commands such as “stop,” “right,” and “yes,” while “go” and “no” showed lower accuracy due to acoustic similarity. In conclusion, the proposed parallel CNN–LSTM architecture effectively integrates convolutional and recurrent learning, resulting in improved recognition accuracy and robust performance with strong potential for real-time voice control and embedded applications.
Co-Authors Ahmad Fauzi Habiba Chozin Atar Fuady Babgei Djoko Purwanto Hendra Kusuma Mu'thiana Gusnam Muhammad Wikan Sasongko R. Sapto Hendri Boedi Soesatyo Santoso