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All Journal Jurnal Teknologi Informasi dan Multimedia Reputasi: Jurnal Rekayasa Perangkat Lunak
Tanwir, Tanwir
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Computer Science & IT

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Implementasi Konsultasi Stunting Balita Menggunakan Large Language Models (LLMs) Tanwir, Tanwir; Hidjah, Khasnur; Susilowati, Dyah
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Mei 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v6i1.8961

Abstract

Stunting pada balita merupakan masalah kesehatan kritis di Indonesia yang memerlukan intervensi berbasis teknologi untuk meningkatkan akses informasi nutrisi. Penelitian ini bertujuan mengembangkan chatbot konsultasi stunting berbasis Large Language Models (LLMs) guna menyediakan rekomendasi kesehatan yang akurat dan mudah diakses. Metode yang digunakan berupa Model LLaMA 3 di-fine-tuning menggunakan dataset Q&A spesifik stunting berisi 7.642 entri, kemudian dievaluasi dengan matrik ROUGE untuk mengukur kesesuaian semantik respons. Hasil menunjukkan model Stunting mencapai skor ROUGE-1 (72,24%), ROUGE-2 (64,54%), ROUGE-L (70,42%), dan ROUGE-Lsum (70,96%), secara signifikan melampaui model baseline seperti LLaMA3, Deepseek-R1, dan Mistral. Chatbot diimplementasikan dalam aplikasi web berbasis cloud dengan arsitektur terdistribusi, dilengkapi enkripsi SSL dan HTTPS untuk menjamin keamanan data. Sistem ini memungkinkan interaksi real-time antara pengguna dan model LLMs melalui antarmuka berbasis Gradio. Temuan penelitian mengonfirmasi potensi LLMs dalam menyederhanakan layanan kesehatan preventif, khususnya di daerah dengan sumber daya terbatas
Pengenalan Bahasa Isyarat Hijaiyah: Augmentasi Data dengan EfficientnetB7 Tanwir, Tanwir; Husain, Husain; Hammad, Rifqi; Anas, Andi Sofyan; Azwar, Muhammad
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 4 (2025): November
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

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.
Co-Authors Andi Sofyan Anas Dyah Susilowati Hammad, Rifqi Husain Husain Khasnur Hidjah Muhammad Azwar