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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Journal of Economic, Business and Engineering (JEBE)
Hidayatus Sibyan
Universitas Sains Al-Qur'an
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering

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IMPLEMENTASI YOLOv11 UNTUK DETEKSI KATA BAHASA ISYARAT BISINDO DAN SIBI Ahmad Syaefudin; Hidayatus Sibyan; Rina Mahmudati; M. Fuat Asnawi; Nur Hasanah
Journal of Economic, Business and Engineering (JEBE) Vol. 7 No. 2 (2026): April
Publisher : Universitas Sains Al Qur'an

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32500/jebe.v7i2.11208

Abstract

Komunikasi merupakan hak dasar manusia, namun penyandang disabilitas rungu sering menghadapi hambatan dalam berinteraksi dengan masyarakat umum yang minim pemahaman bahasa isyarat. Penelitian ini bertujuan untuk mengimplementasikan dan mengevaluasi kinerja algoritma YOLOv11 dalam mendeteksi kata pada Bahasa Isyarat Indonesia (BISINDO) dan Sistem Isyarat Bahasa Indonesia (SIBI) secara real-time. Metode yang digunakan meliputi pengumpulan dataset sebanyak 1.745 citra yang mencakup 12 kelas kata, preprocessing menggunakan Roboflow untuk anotasi dan augmentasi, serta pelatihan model menggunakan Google Colab. Hasil penelitian menunjukkan bahwa model YOLOv11 mampu mendeteksi bahasa isyarat dengan sangat baik, mencapai nilai mean Average Precision (mAP@50) sebesar 98%, Precision 96.8%, dan Recall 96.4% pada tahap validasi. Implementasi sistem dilakukan berbasis web menggunakan framework Flask, memungkinkan deteksi interaktif melalui kamera. Secara keseluruhan, penelitian ini menunjukkan bahwa penerapan model YOLOv11 efektif dalam mendukung penerjemahan bahasa isyarat secara real-time. Implikasi dari penelitian ini adalah meningkatnya aksesibilitas komunikasi bagi penyandang disabilitas rungu, khususnya dalam interaksi sehari-hari dengan masyarakat umum, serta membuka peluang pengembangan sistem penerjemah bahasa isyarat yang lebih luas, adaptif, dan terintegrasi pada berbagai platform digital.
LSTM-Based Causal Attribution Modeling of the 2025 Sumatra Flash Flood Discourse on YouTube Kunti Najma Jalia; Adi Suwondo; Hidayatus Sibyan
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 10 No. 1 (2026)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v10i1.2132

Abstract

Existing disaster sentiment analysis mainly focuses on emotional polarity classification, while often over-looking the causal reasoning that shapes public discourse on responsibility for disaster outcomes. This study proposes and assesses a Long Short-Term Memory (LSTM)-based causal attribution classification framework to examine YouTube comments related to the 2025 Sumatra flash flood. It compares LSTM performance with Sup-port Vector Machine (SVM) and Naïve Bayes baselines. A total of 17,503 publicly available comments were collected through the YouTube Data API v3 and processed into a final dataset of 12,299 comments. The com-ments were classified into two causal categories, human factor and nature/prayer factor, using lexicon-based scoring validated by three independent annotators (Cohen's κ = 0.81). The experimental results show that LSTM achieves 98.17% accuracy with strong stability (±0.25% standard deviation) under stratified five-fold cross-validation, substantially outperforming SVM (82.83%) and Naïve Bayes (75.04%). These findings indi-cate that sequence-based architectures can capture the contextual dependencies in causal attribution dis-course, offering a replicable framework for disaster risk communication monitoring systems.
Co-Authors Adi Suwondo Ahmad Syaefudin Kunti Najma Jalia M. Fuat Asnawi Nur Hasanah Rina Mahmudati