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All Journal Jurnal Sains dan Teknologi Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) International Journal of Advances in Data and Information Systems Jurnal Infortech Journal of Computer Scine and Information Technology Jurnal Ekonomika Dan Bisnis Jurnal Sains Informatika Terapan (JSIT) Jurnal Komtekinfo Knowbase : International Journal of Knowledge in Database
Yenila, Firna
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Materials Science & Nanotechnology

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Smart Health Monitoring: Analisis Suhu Tubuh Dan Respirasi Menggunakan Kamera Termal Wahyuni, Suci; Yenila, Firna; Wiyandra, Yogi
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.842

Abstract

The advancement of digital technology and artificial intelligence has opened vast opportunities for intelligent health monitoring systems that operate automatically, in real time, and without physical contact. This study aims to develop a system for detecting human body temperature and respiratory patterns using an infrared thermal camera based on digital image processing and machine learning. The research method involves thermal data acquisition on facial areas (forehead, nose, and mouth), image preprocessing using two-point temperature calibration and Gaussian filtering for noise reduction, and segmentation of the respiratory region using the adaptive thresholding method. Feature extraction is performed by analyzing temperature variations in the nose and mouth regions as thermal signals, which are converted into the frequency domain using the Fast Fourier Transform (FFT) algorithm to determine the respiration rate. Classification is carried out using the Support Vector Machine (SVM) algorithm to distinguish three physiological conditions: normal, fever, and respiratory disorder. The dataset consists of 550 thermal images, divided into 385 images (70%) for training and 165 images (30%) for testing. Experimental results show that the system achieves an accuracy of 98.32%, with an estimated forehead temperature of 145.23°C (a relative value from initial calibration) and a respiration rate of 6.6 bpm, indicating the subject’s condition as fever. This study demonstrates that the combination of thermal image processing, FFT algorithms, and SVM classification is effective for non-invasive, high-precision, and efficient health monitoring systems. The proposed system has the potential to support the development of the Internet of Medical Things (IoMT) for safe, accurate, and adaptive remote health monitoring in response to patients’ physiological changes
Identification and Classification of Cracks in Traditional Pottery from West Sumatra Using Digital Image Processing Mahessya, Raja Ayu; Yenila, Firna
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12156

Abstract

Cracks in traditional West Sumatran pottery are a major challenge in preserving this cultural heritage. With age and the manual manufacturing process, pottery becomes highly susceptible to physical damage, particularly cracks on the surface and internal structure. These cracks not only affect the functional and aesthetic value but also reduce the cultural and economic value of the pottery. Therefore, an accurate early identification system is crucial to ensure the survival and preservation of this culture. This study developed a digital image processing-based system to detect and classify cracks in traditional pottery. The system integrates image preprocessing, including cropping, resizing, grayscale conversion, contrast stretching, and histogram equalization to improve image quality and highlight thin and irregular cracks. Image segmentation was performed using the Multi-Threshold Otsu method to separate cracks from the background, while classification was performed using a convolutional neural network (CNN). Experimental results show that this system is able to achieve an accuracy of 94.8%, precision of 93.5%, recall of 92.3%, and F1-score of 92.9%, indicating the system's ability to accurately detect cracks. Comparisons with other segmentation and classification methods are needed to provide a more comprehensive picture of the effectiveness of this approach. The implementation of this system is expected to support the preservation of traditional Minangkabau pottery through digitalization, provide an ornament database that can be accessed by researchers, artists, and the general public, and assist in more efficient cultural documentation and archiving.
A Multilevel Image Processing Approach for Minangkabau Ornament Detection Using CLAHE, Multi Threshold Otsu, and CNN Wahyuni, Suci; Wiyandra, Yogi; Yenila, Firna
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1520

Abstract

Traditional Minangkabau ornaments such as pucuak rabuang, itiak pulang patang, kaluak paku, and rabuang sanjo represent a form of visual cultural heritage with high aesthetic and philosophical value. However, the digital documentation and preservation of these ornaments still face significant challenges, particularly due to variations in media, fine surface textures, uneven illumination, and complex image backgrounds. These conditions complicate the separation of ornament motifs from the background and consequently affect the accuracy of identification and classification processes. This study aims to develop an image processing approach for the detection and identification of Minangkabau ornaments through image quality enhancement and multi-level segmentation. The proposed method begins with a preprocessing stage that includes motif area cropping, image size normalization, noise reduction through filtering, contrast stretching, and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance local contrast. Subsequently, segmentation is performed using the Multi-Threshold Otsu method to divide the image into multiple intensity classes, enabling a more detailed separation of ornament structures. The segmentation results are evaluated using morphological analysis and further tested using a Convolutional Neural Network (CNN) to assess classification performance. Experiments were conducted on a dataset of 1,024 images, with a training–testing split of 70% and 30%, respectively. The experimental results demonstrate that the proposed approach produces representative motif segmentation and achieves a classification accuracy of 99.67%. These findings indicate that the integration of systematic preprocessing, multi-threshold segmentation, and CNN-based classification is effective in supporting the digital preservation of Minangkabau ornaments.
Co-Authors Defit , Sarjon Febri Hadi Khairul, Khairul Zaman Kurnia, Hezy Mahessya, Raja Ayu Marfalino, Hari Permana, Randy Pradana, Taufan Rahman, Sepsa Nur Ranni, Larissa Navia Rianti, Eva Rozi, Fachrul Ruri Hartika Zain Sovia, Rini Suci Wahyuni Syaiffullah, Afif Wiyandra, Yogi Yogi Wiyandra Yuhandri Yuhandri, Yuhandri Yusvi Diana Zefriyenni Zefriyenni, Zefriyenni