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All Journal JOIN (Jurnal Online Informatika) Media Jurnal Informatika
Agustiani, Amelia Dewi
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Computer Science & IT

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Penggunaan MediaPipe untuk Pengenalan Gesture Tangan Real-Time dalam Pengendalian Presentasi Agustiani, Amelia Dewi; Sholahuddin, Muhammad Rizqi; Putri, Salsabila Maharani; Hidayatullah, Priyanto
Media Jurnal Informatika Vol 16, No 2 (2024): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v16i2.4788

Abstract

Penelitian ini membahas masalah pengendalian presentasi yang terbatas pada penggunaan perangkat fisik seperti mouse atau keyboard, yang sering mengurangi fleksibilitas pengguna. Untuk mengatasi hal ini, penelitian ini mengusulkan implementasi MediaPipe, sebuah framework pengolahan citra dan video, untuk pengenalan gestur tangan secara real-time. Metode ini memungkinkan pengguna mengontrol presentasi PowerPoint secara intuitif melalui gerakan tangan tanpa kontak fisik dengan perangkat. Pengendalian dilakukan dengan mendeteksi dan menginterpretasikan gestur tangan menggunakan teknologi pengenalan pola berbasis jaringan saraf tiruan. Studi ini bertujuan meningkatkan efisiensi dan kenyamanan dalam mengendalikan presentasi, khususnya dalam situasi yang membutuhkan interaksi jarak jauh. Hasil penelitian menunjukkan implementasi ini mampu memberikan respons cepat terhadap perubahan gestur dalam berbagai kondisi penggunaan. Model pengenalan gestur tangan yang diusulkan menunjukkan performa sangat baik, dengan nilai macro average precision, recall, dan F1-score masing-masing mencapai 97%, yang berkontribusi pada pengembangan antarmuka pengguna yang lebih intuitif dan efisien.
Evaluating RAG Performance on Small Language Models for Low-Resource Devices through Chunking and Retrieval Methods Agustiani, Amelia Dewi; Putri, Salsabila Maharani; Hutahaean, Jonner; Sholahuddin, Muhammad Rizqi; Alifi, Muhammad Riza; Hodijah, Ade
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1733

Abstract

Retrieval-Augmented Generation (RAG) combines generative capabilities of language models with external document retrieval to answer questions grounded in reference texts. However, deploying RAG on low-resource devices like Android smartphones is challenging because SLMs have limited computational capacity and depend heavily on efficient chunking and retrieval. Although interest in on-device processing is growing, research on RAG configurations for SLMs under strict resource constraints especially for domain-specific tasks remains limited. This study therefore investigates which combinations of chunking technique, chunk size, overlap, and retrieval strategy best balance accuracy and speed on low-resource devices. The evaluation uses 148 Indonesian questions sourced from an official Hajj guidebook. The study consists of two phases retrieval and generation. Retrieval is evaluated using BLEU, ROUGE-L, MRR, MAP, and Hit@k, while answer quality is measured with BERTScore. The experiments compare different chunking methods (fixed-size or semantic), chunk sizes (128 or 256 tokens), overlaps (25, 50 and 100 tokens), and retrieval methods (dense, sparse, or hybrid). Results show that sparse retrieval with 256-token chunks and 100-token overlap yields the best answer quality (F1 = 0.726). However, 128-token chunks with the same overlap provide the fastest generation time (69.737 seconds). The main contribution of this study is a systematic evaluation of RAG configurations for fully on-device SLMs using a domain-specific Hajj and Umrah dataset not explored in prior research. The findings provide practical guidance for designing efficient and accurate RAG-based question-answering systems on low-resource devices.
Co-Authors Ade Hodijah Jonner Hutahaean Muhammad Riza Alifi Muhammad Rizqi Sholahuddin Priyanto Hidayatullah, Priyanto Putri, Salsabila Maharani