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All Journal Jurnal Informatika dan Teknik Elektro Terapan JURNAL SIMETRIK JSAI (Journal Scientific and Applied Informatics) KAKIFIKOM : Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer Journal of Community Service (JCOS) Journal Of Artificial Intelligence And Software Engineering Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Hatala, Zulkarnaen
Politeknik Negeri Ambon
Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Public Health Other

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Few-shot Classification of Smartphone Photos using Hidden Markov Model and Siamese Network Hatala, Zulkarnaen; Hudzaly, Muhammad
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.116

Abstract

Images from the increasing use of smartphones are so large that they are nearly impossible to handle by hand. The problem arises when a person needs to classify these photos into groups or classes. Smartphones are low-performance devices in contrast to desktop or cloud-based computers. Many solutions of image classification using various types of Convolutional Neural Network (CNN) are performed on massive cloud-based supercomputers. These computers often equipped with very high-end additional specialized graphics processing units (GPUs) at remarkable prices. In fact, to implement classification in most smartphones currently on the market, we need an algorithm that has less computation. Based on this fact, we propose HMM that requires fewer parameters. The aim of this research is to examine HMM method for classification of photos taken with a smartphone. For a comparison we also outline the results from Siamese CNN. The same data are used for training and testing for both models. For HMM, we use Discrete Cosine Transform (DCT) to extract salient features of images. The number of training examples is very small compared to the test set. Here we carried out few-shot classification method. In the training phase, we used Maximum Likelihood (ML) criterion-based, Baum-welch algorithm. Two versions are used; isolated training is applied first and later followed by jointly-embedded Baum-welch estimation of parameters. For recognition of the HMM, Viterbi algorithm is applied. Performances of both procedures were measured. Based on the test results, HMM achieves 0,94 precision, 0.85 recall, F1 score 0.89 and accuracy 0.90 while Siamese claims 0.87, 0.98, 0.92 and 0.91. The result shows that HMM, which has advantage over Siamese in term of fewer parameters number, still competes Siamese CNN with only slight decrease in performance. We conclude that HMM are suitable over Siamese CNN to be implemented in low-performance devices such as cellphones.
VERIFIKASI DOKUMEN TRANSKRIP NILAI SEMESTER MENGGUNAKAN METODA OPTICAL CHARACTER RECOGNITION hatala, zulkarnaen
Jurnal Informatika dan Teknik Elektro Terapan Vol. 11 No. 3 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i3.3277

Abstract

At the Ambon State Polytechnic, students' semester grade reports are still manually typed. This causes frequent typo errors which can result in the invalidity of the document, let alone incorrect grades, student identification numbers and many other label values. Here a java application has been implemented to detect these errors. This application is primarily intended for officials of the Head of Study Program, Head of the Department before signing and validating the report. Officials who legalize it will be greatly assisted because tedious validation work can be replaced by computers. The validation process is carried out by utilizing the optical character recognition technique from the open source library Tesseract-OCR. From the experimental results the verification process can be improved by using OCR  specific on specific regions of interest (ROI) after using template matching method from OpenCV. The consideration of the Levehnstein distance in the comparison of label values against the reference database also improves the success rate of the algorithm. The database used has been tested for about 800 grade report documents, with successful verification result above 90%.
Co-Authors Hudzaly, Muhammad Laisina, L. H. Marceau Haurissa Muhammad Ikhwan Burhan Thariq , Ahmad