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All Journal Router : Jurnal Teknik Informatika dan Terapan Saturnus: Jurnal Teknologi dan Sistem Informasi
Fadhil Ahmad
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Computer Science & IT

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Implementasi Metode Task Based Language Learning untuk Meningkatkan Kompetensi Bahasa Inggris Berbasis Android Fadhil Ahmad; Tata Sutabri
Router : Jurnal Teknik Informatika dan Terapan Vol. 2 No. 4 (2024): Desember: Router: Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v2i4.269

Abstract

This study explores the implementation of Task-Based Language Learning (TBLL) in developing an Android-based application to enhance English language proficiency. TBLL was chosen for its focus on real-world language usage, enabling users to practice English skills through practical tasks. The application is developed using the Flutter framework, which supports cross-platform functionality, providing ease of access for users. The study measures the improvement in English language proficiency without relying on surveys for data collection; instead, it uses performance data from users' in-app activities. The findings are expected to demonstrate that integrating TBLL into a digital platform can effectively enhance English proficiency, especially in practical communication skills. This Android-based application implementation is envisioned as an innovative solution for flexible and efficient language learning.
Integrasi Model LLM Ollama dan OpenStreetMap API pada BI untuk Rekomendasi Lokasi Fadhil Ahmad; Hamid Rahman; Tata Sutabri
Saturnus : Jurnal Teknologi dan Sistem Informasi Vol. 3 No. 4 (2025): Oktober : Saturnus : Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v3i4.1138

Abstract

This study presents the integration of a Large Language Model (LLM) Ollama with the OpenStreetMap (OSM) API within a Business Intelligence (BI) framework to develop an intelligent, location-based recommendation system. The system is designed to assist users in finding dining, leisure, and resting places through natural language interaction and contextual understanding. The LLM interprets user input semantically, transforms it into structured spatial queries, and retrieves relevant geospatial data from OSM. The data are then analyzed, categorized, and visualized using BI methods to enhance interpretability and decision-making. The system was implemented using Next.js, Leaflet.js, ensuring interactivity and scalability for web-based deployment. Technical evaluation focused on system accuracy, response time, and output consistency. Results demonstrate an average response time of 1.74 seconds, 80% accuracy, and 80% consistency, proving the model’s efficiency in producing relevant, context-aware recommendations. This integration highlights the potential of combining open geospatial data, local LLMs, and BI analytics to create intelligent, data-driven decision support systems applicable to tourism, urban planning, and spatial information management.
Co-Authors Hamid Rahman Tata Sutabri