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All Journal INTECOMS: Journal of Information Technology and Computer Science International Journal Software Engineering and Computer Science (IJSECS)
Eric Julianto
Universitas Esa Unggul
Computer Science & IT

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SISTEM PENDUKUNG KEPUTUSAN PENGIRIMAN BUS BERBASIS AIOT MENGGUNAKAN INTEGRASI YOLOV8 DAN GOOGLE DISTANCE MATRIX API Arinal Dzikrul Haqqy Amir; Raihan Evanza; Vitri Tundjungsari; Eric Julianto
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 1 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/ywcqtg69

Abstract

Kalkulasi penumpang dan estimasi kepadatan kerumunan yang akurat memainkan peran penting dalam sistem transportasi publik cerdas untuk meningkatkan keselamatan, kualitas layanan, dan efisiensi operasional. Pendekatan berbasis visi yang memanfaatkan model deep learning, khususnya You Only Look Once (YOLO), telah diadopsi secara luas untuk deteksi dan pelacakan penumpang secara real-time karena kecepatan deteksi dan akurasinya yang tinggi. Namun, tantangan seperti oklusi, variasi skala, dan sudut pandang kamera yang terbatas tetap menjadi kendala signifikan, terutama di halte bus yang padat dan lingkungan transportasi umum. Untuk mengatasi keterbatasan ini, studi terbaru telah mengintegrasikan arsitektur Internet of Things (IoT) dengan analitik video untuk memungkinkan pemantauan arus penumpang secara berkelanjutan. Dalam penelitian ini, deteksi kerumunan berbasis YOLO dikombinasikan dengan Google Distance Matrix API untuk mengestimasi waktu tempuh dan jarak antar halte bus, sehingga memungkinkan rekomendasi pengiriman armada otomatis berdasarkan kondisi kepadatan secara real-time. Kerangka kerja berbasis AIoT yang diusulkan mendukung pengambilan keputusan berbasis data untuk operasional bus cerdas, meningkatkan responsivitas, mengurangi penumpukan penumpang, serta mengoptimalkan penjadwalan transportasi publik. Kata Kunci: AIoT-Enabled Public Transport Surveillance, YOLOv8, Sistem Transportasi Cerdas, Estimasi Kepadatan Kerumunan Real-Time, Google Distance Matrix API.
Integration of Static Knowledge and Telemetry Data for an IoT-Based Customer Support AI Agent Jauhar Maknun Adib; Wahyu Purnama Magribi; Muhammad Fazly Qusyairy; Eric Julianto; Khusnul Fajri Rhomadon
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 3 (2026): DECEMBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i3.7875

Abstract

The operational effectiveness of artificial intelligence agents in customer support is frequently attributed to the generative sophistication of underlying language models, although answer reliability primarily depends on the structure and quality of the referenced knowledge base. In complex technical domains such as the Internet of Things (IoT), static documentation alone often fails to resolve customer inquiries that involve real-time device states, sensor readings, and connectivity logs. This study investigates how progressive tiers of knowledge integration affect the response quality of an AI agent within an IoT customer support context. Employing a mixed-method pilot design combining literature synthesis, comparative analysis, experimental testing, and conceptual exploration, thirty representative questions were classified into informational, status, and diagnostic categories. These queries were evaluated across four operational scenarios: without retrieval-augmented generation (RAG), article-based RAG, article plus device metadata records, and a comprehensive hybrid framework integrating static articles, device records, and telemetry streams. Answer quality was measured using Cosine Similarity, BERTScore (F1), and a randomized blind human evaluation assessing relevance, correctness, and usefulness. The results demonstrate that while curated static documentation adequately addresses procedural informational queries, resolving status and diagnostic issues necessitates real-time operational context. The hybrid integration delivered the highest overall performance, achieving a human-evaluation score of 4.40 compared with 2.53 for article-based RAG (p = 0.0039). These empirical findings confirm that robust IoT support agents require dynamic knowledge governance that bridges versioned documentation, synchronized asset metadata, and live telemetry data within a unified retrieval framework.
Co-Authors Arinal Dzikrul Haqqy Amir Jauhar Maknun Adib Khusnul Fajri Rhomadon Muhammad Fazly Qusyairy Raihan Evanza Vitri Tundjungsari Wahyu Purnama Magribi