Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electronics and Instrumentation Systems Jurnal Teknik Informatika (JUTIF)
Ika Candradewi, Ika
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PENGENALAN TEKS BAHASA INDONESIA PADA CITRA TULISAN TANGAN BERBASIS TRANSFORMER Rahmawati, Dianita Alfi; Candradewi, Ika
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 14, No 1 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.86926

Abstract

Digitalisasi dokumen dapat dipercepat berkat kemajuan teknologi. Banyak upaya telah dilakukan untuk mengenali teks dari foto. Banyak arsitektur mampu mengenali teks, khususnya citra tulisan tangan salah satunya adalah transformer. Pada penelitian sebelumnya masih banyak yang menggunakan dataset citra dengan aksara tegak sehingga kurang variatif. Untuk meningkatkan keahlian pemodelan pembelajaran, proyek ini berfokus pada pengimplementasian dan pengembangan sistem pada Transformers dengan pengujian dataset yang lebih bervariasi.Dataset yang digunakan terdiri dari foto dengan tulisan Indonesia. setelah langkah pra-pemrosesan kemudian akan diubah menjadi token dengan label kelas dan koordinat kotak pembatas untuk anotasi gambar. Dataset akan dilatih menggunakan arsitektur transformer. Encoder-decoder merupakan dasar dari arsitektur Transformer ini. Pengujian data dilakukan setelah model dilatih menggunakan mean Average Precision (mAP).Sistem yang dibuat mampu mengenali dan mengklasifikasikan objek secara akurat dari data gambar tulisan tangan, termasuk objek yang mewakili kata-kata bahasa Indonesia. Hyperparameter yang paling optimal didapatkan batch dan jumlah epoch masing-masing 32 dan 40. Dengan menggunakan parameter terbaik, evaluasi model menghasilkan data dari sampel latih dan uji dengan masing-masing nilai mAP 0,97 dan 0,95.
Comparative Analysis of Face Mask Detection using Lightweight CNN and Bag of Visual Word-based Classifier for Real-Time Surveillance Candradewi, Ika; Aldino Ardi S, Bakhtiar; Harjoko, Agus; Dharmawan, Andi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4461

Abstract

Face mask detection has become increasingly important across various sectors, including healthcare, food processing industries, and public safety, to ensure adherence to health and hygiene protocols and minimize the risks of contamination. Manual supervision of mask usage is often inefficient, labor-intensive, and prone to inconsistency. To address this challenge, this study proposes an automated face mask detection system utilizing computer vision technology, designed for real-time monitoring on resource-limited edge devices, such as the Raspberry Pi 4 with a Google Coral USB Accelerator. The system integrates Multi-task Cascaded Convolutional Neural Networks (MTCNN) for face detection and a modified lightweight Convolutional Neural Network (CNN) for binary mask classification. Deployed via a web-based platform, it captures images of non-compliant individuals and triggers alerts. To evaluate model performance, the modified CNN is compared with the Bag of Visual Words (BoVW) method using SVM and MLP classifiers on two datasets: the 12k-Face Mask Dataset (Kaggle) and a newly proposed dataset. The CNN model demonstrated higher classification performance than both BoVW-SVM and BoVW-MLP, with AUC improvements of 49% and 43% on the proposed and 12k-Face Mask datasets, respectively. This study contributes to the advancement of computer vision-based public health monitoring by offering a robust, scalable, and real-time face mask detection system. The findings highlight the practical advantages of deep learning approaches over traditional feature extraction techniques, supporting the development of intelligent, automated surveillance systems and policy enforcement in high-risk environments, which will facilitate future advancements in AI-driven public safety solutions.
Co-Authors Agus Harjoko Aldino Ardi S, Bakhtiar Andi Dharmawan Rahmawati, Dianita Alfi