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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) Jurnal Infortech Journal of Computer Scine and Information Technology Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer 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 Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering

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PENERAPAN TEOREMA BAYES PADA SISTEM PAKAR DIAGNOSA GASTROINTESTINAL Wahyuni, Suci; Wiyandra, Yogi; Zain, Ruri Hartika; Kurnia, Hezy; Yenila, Firna
Journal of Information System Management (JOISM) Vol. 5 No. 2 (2024): Januari
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2024v5i2.1396

Abstract

Gastrointestinal merupakan penyakit yang disebabkan oleh permaslaahan pada bagian pencernaan yang memiliki fungsi yang tidak maksimal. Hal tersebut terjadi disebabkan karena proses penyerapan makanan dan nutrisi menjadi tidak seimbang. Sistem gastrointestinal melibatkan semua organ dalam dari mulut sampai anus. Pentingnya pemahaman tentang permasalahan gastrointestinal perlu disosialisasikan untuk memberikan edukasi kepada Masyarakat mengenai kondisi tersebut. Salah satu alasan dalam melakukan penelitian ini adalah memberikan informasi berbasis pengetahuan melalui aplikasi yang disampaikan oleh pakar dalam memberikan edukasi kepada Masyarakat mengenai gastrointestinal. Penelitian ini dilakukan dengan menggunakan aplikasi berbasis online berupa sistem pakar dengan mengusung metode teorema bayes yang mampu menghubungkan tingkat keyakinan user (prior) kepada keyakinan baru (posterior) setelah adanya suatu observasi baru (evidence) berdasarkan kemungkinan tertentu. Hasil penelitian ini terhadap ujicoba salah satu rule yang diberikan memberikan nilai keyakinan 32.04% sehingga pengujian tersebut memberikan nilai sesuai dengan ketentuan yang telah ditetapkan oleh pakar.
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.
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 Trisna, Novi Wiyandra, Yogi Yogi Wiyandra Yuhandri Yuhandri, Yuhandri Yusvi Diana Zefriyenni Zefriyenni, Zefriyenni