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All Journal Techno.Com: Jurnal Teknologi Informasi Format : Jurnal Imiah Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Insect (Informatics and Security) : Jurnal Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING CARADDE: Jurnal Pengabdian Kepada Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Dharma Laksana JURNAL TEKNIK Jurnal Media Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) TRANSFORMASI : JURNAL PENGABDIAN PADA MASYARAKAT Journal of Advanced Health Informatics Research Jurnal Pengabdian Nasional (JPN) Indonesia FRAMEWORK Jurnal Ilmu Komputer dan Informatika
Dewi Astria Faroek
Universitas Muhammadiyah Sorong
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
A Komparasi Image Matching Menggunakan Metode K-Nearest Neightbor (KNN) dan Support Vector Machine (SVM) Rusydi Umar; Imam Riadi; Dewi Astria Faroek
Journal of Applied Informatics and Computing Vol 4 No 2 (2020): Desember 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v4i2.2226

Abstract

Image matching is the process of finding digital images that have a degree of similarity. matching images using the classification method. In measuring image matching, the images used are original logo images and manipulated logo images. Comparison of classification algorithms from the two methods namely K-Nearest Neighbor (KNN) and Support Vector Machine with Sequential Minimal Optimization (SMO) optimization used to calculate matches based on accuracy values. The K-Nearest Neighbor (KNN) classification method is based on proximity or K calculations while the Support Vector Machine (SVM) classification method measures the distance between the hyperplane and the nearest data. Image match values are measured by Precision, Recall, F1-Score, and Accuracy. The image matching steps start from the preparation of data processing, extraction of HSV color features and shapes, then the classification stage. Digital images are used as many as 10 images consisting of one original logo and 9 manipulated logos. In the classification testing stage, using the WEKA application by applying the 10-fold cross-validation method. From the results of tests conducted that the closest k-neighbor (KNN) classification method is 80% and has a k = 0.889 which is quite good in measuring proximity, while the SVM classification method is 70%. The results of this image matching comparison can be concluded that the K-Nearest Neighbor classification method works better than SVM for image matching.
Image Processing and Object Detection in the Indonesian Sign System (SIBI) for Hearing-Impaired Communication Faroek, Dewi Astria; Yusuf, Muhammad; Haris, Haris
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.v10i1.11395

Abstract

Communication is a fundamental human need, yet individuals with hearing impairments continue to face barriers due to limited access to sign language translation technologies. In Indonesia, the adoption of such technologies remains low, particularly in regions such as Sorong, Southwest Papua, creating a communication gap between the Deaf community and the general public. This study develops a web-based detection system for 36 classes of the Indonesian Sign System (SIBI) using the YOLOv5 algorithm. The dataset consists of 5,682 images of SIBI hand poses with variations in lighting and background, divided into 4,970 training images (87%), 376 validation images (7%), and 335 test images (6%). All data were processed through labeling, preprocessing, augmentation, balancing, and model training. The training was conducted for 150 epochs, and the evaluation results show that YOLOv5 is capable of detecting SIBI signs with significant accuracy. Performance evaluation using a confusion matrix achieved a detection accuracy of 95%, supported by stable precision and recall values and real-time inference performance on common web browsers. Usability testing with 20 respondents indicated satisfaction levels above 72.8%, demonstrating that the system is practical and easy to use. This research presents a validated real-time, web-based SIBI detection system that supports inclusive computer vision applications and enhances accessibility in public services such as education, healthcare, and administrative environments.
Co-Authors Ahistasari, Asih Akbar Akbar Aminuyati Amri, Irman Arief Firmansyah Ariefah Khairina Islahati Elrico Ermin Ermin Faida Musa’ad Fitriyani Tella Hafidz, Muhammad Nasir Hanif, Kharis Hudaiby Haris Haris, Haris Imam Riadi Imam Riadi La Jupriadi Fakhri M, Anggun Moh. Fatkur Riski Nur Yahya Riski Muhammad Baballe Ahmad Muhammad Rizki Setyawan Muhammad Syachrul Muhammad Yusuf Mule, Mikhael Julfian Muzakkir Pangri Ningtias, Arya Ayu Nugraha, Pandu Diaz Nur Miswar Nurdjan, Nirwana nurfitri Purwono P Purwono, Purwono Rahmawati, Miftah Sigit REZKI, REZKI Rusydi Umar Rusydi Umar S Pattanang, Moh Saddam Sakti, Fijaya Bima Dwi Salam, Ristanti Semunya, Afrida Setiawan, Muhammad Rizki Sigit, Miftah Soekarta, Rendra Solossa, Isak Jonter Tella, Fitriyani Ulima, Nurida Virasanty Muslimah Weyai, Saldriani R M Yuslena Sari, Yuslena