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All Journal 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
Hudzaly, Muhammad
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Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Public Health Other

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Pendampingan Proses Belajar Daring Pada Anak Sekolah Dasar Kampung KateKate Kota Ambon Hatala, Zulkarnaen; Hudzaly, Muhammad
Journal of Community Service (JCOS) Vol. 3 No. 1 (2025)
Publisher : EDUPEDIA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56855/jcos.v3i1.1250

Abstract

Sektor pendidikan dasar formal masih memiliki banyak tantangan. Di era teknologi internet dan media sosial, anak usia belajar mengalami penurunan minat untuk belajar secara konvensional dengan membaca buku teks dan mode kertas lainnya. Daya tarik smartphone (HP) sangat bersifat desktruktif terhadap konsentrasi dan minat anak didik terhadap pelajaran. Banyak waktu dihabiskan remaja untuk bermain HP dibandingkan untuk belajar. Disini akan diterapkan pendekatan untuk meningkatkan minat dan kognitif anak usia didik terhadap pelajaran. Perangkat smartphone yang menjadi sumber masalah, akan dimanfaatkan sebagai media untuk pembelajaran itu sendiri. Bertujuan agar proses pembelajaran menjadi lebih menarik minat siswa maka proses pembelajaran bisa memanfaatkan daya tarik smartphone dengan menggunakan proses pembelajaran secara daring (online). Kami menerapkan teknologi E-learning untuk pembelajaran anak didik melalui media HP dengan terhubung ke jaringan wifi serta internet. Hasil yang diperoleh adalah antusias yang meningkat serta capaian pemahaman yang baik oleh peserta didik.
Smartphone Photos Categorization Using Markov Model with Limited Training Data Hatala, Zulkarnaen; Hudzaly, Muhammad
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6943

Abstract

This writing investigates the classification of images taken using a smartphone. Due to the large number of photos and the large number of photo categories, it is necessary to automatically categorize these photos. Photos are classified using two different approaches. The first method uses Hidden Markov Model (HMM) and the second technique employs Siamese Network from Convolutional Neural Network (CNN) architecture. 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. For recognition of the HMM, Viterbi algorithm is applied. Performances of both procedures were measured. For only 109 test samples HMM achieve 98% accuracy, while twin network achieves 90%. The use of HMM has advantage over Siamese in term of faster computation. HMM opens the opportunity of the smartphone with low computation capability to categorize photos automatically.
Validasi Otomatis Dokumen Transkrip Nilai Mahasiswa Menggunakan Metoda Optical Character Recognition Hatala, Zulkarnaen; Thariq , Ahmad; Hudzaly, Muhammad; Burhan, Muhammad Ikhwan
KAKIFIKOM : Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer Volume 5 Nomor 1 Tahun 2023
Publisher : UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

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%.
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.
Co-Authors Hatala, Zulkarnaen Muhammad Ikhwan Burhan Thariq , Ahmad