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All Journal Jurnal Teknologi Informasi dan Multimedia
Rifqi Hammad
Program Studi Rekayasa Perangkat Lunak, Universitas Bumigora
Computer Science & IT

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Pengenalan Bahasa Isyarat Hijaiyah: Augmentasi Data dengan EfficientnetB7 Tanwir Tanwir; Husain Husain; Rifqi Hammad; Andi Sofyan Anas; Muhammad Azwar
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 4 (2025): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i4.728

Abstract

Sign language plays an important role as the primary means of communication for individuals with hearing impairments. This study aims to improve the accuracy of hijaiyah sign language detection through the application of the EfficientNetB7 architecture and data augmentation tech-niques. The method used, namely the EfficientNetB7 algorithm, was chosen as the base model be-cause of its ability to balance high accuracy with optimal resource utilization by performing data augmentation with rescale, shear, zoom, rotation, and flip horizontal techniques applied to enrich the variation of the original dataset of 6,811 images to 105,615 images. The experimental results show that the combination of EfficientNetB7 and data augmentation produces 99% accuracy on the test data, with consistent performance seen from the confusion matrix and accuracy loss graph for 50 epochs. This study proves that this approach not only improves model generalization but also reduces the risk of overfitting, thus potentially supporting social inclusion through efficient and reliable technology.
Pengembangan Platform Intervensi Status Gizi Ibu Hamil Berbasis Integrasi Case-Based Reasoning dan Teori Dempster–Shafer Muhammad Haris Nasri; Rifqi Hammad; Gede Yogi Pratama
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 1 (2026): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i1.905

Abstract

Nutritional problems among toddlers and pregnant women remain a major public health issue in Indonesia, necessitating a decision-support system capable of providing rapid and accurate nu-tritional diagnosis and intervention. This study develops an expert system integrating Case-Based Reasoning (CBR) and the Dempster–Shafer theory to diagnose the nutritional status of toddlers and pregnant women. The CBR method is employed to identify solutions for new cases based on similarity to previous cases, while the Dempster–Shafer theory is utilized to handle un-certainty and combine multiple forms of evidence derived from anthropometric, clinical, and health history parameters. The system was tested using 20 cases involving variables such as body weight, height, mid-upper arm circumference (MUAC), hemoglobin level (Hb), gestational age, and dietary intake. The results indicate that the system achieved an accuracy of 90%, an average confidence level of 82.7%, and a diagnostic precision of 88% when compared to expert nutrition-ists’ assessments. Diagnostic discrepancies occurred in only two cases (10%), both of which ex-hibited parameter values near the classification thresholds. These findings demonstrate that the integration of CBR and the Dempster–Shafer theory enhances the reliability of expert systems in generating accurate and measurable nutritional diagnoses despite data uncertainty, and shows strong potential as a decision-support tool for nutritionists in providing faster, more objective, and evidence-based nutritional interventions.
Co-Authors Andi Sofyan Anas Gede Yogi Pratama Husain Husain Muhammad Azwar Muhammad Haris Nasri Tanwir Tanwir