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All Journal JURIKOM (Jurnal Riset Komputer)
Nuranissa D. Paemo
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Edge AI Berbasis Computer Vision Untuk Meningkatkan Efektivitas Sistem Deteksi Pemilahan Sampah Real-Time Integrasi YOLOv8, Raspberry Pi 5 dan SEE Syaifuddin; Ifriandi Labolo; Nuranissa D. Paemo; Abdul Malik I Buna
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9298

Abstract

Waste management in Indonesia is still characterized by a high volume of improperly managed waste and low source-level segregation, causing recyclable materials to mix with other waste streams and reducing their recovery value. This situation calls for a sorting system that is effective, fast, and affordable, while also providing real-time operational information to support on site decision-making. This study presents an integrated computer vision approach using YOLOv8 deployed on a Raspberry Pi 5 with a Camera Module 3, connected to a real-time information system via Server-Sent Events (SSE) for monitoring and analytics. The methodology includes constructing a labeled dataset in YOLO TXT format, training a YOLOv8n model, deploying edge inference, and developing a backend API to receive detection outputs and stream them to a dashboard in real time. The system is evaluated using mean Average Precision (mAP), precision–recall, frames per second (FPS), and end-to-end latency from the camera to the dashboard. The prototype achieves an mAP@0.5 of 98.5% with precision–recall above 97%, an average throughput of 8.3 FPS at 640×640 resolution, and a median SSE communication latency of 0.5–0.6 ms, demonstrating the feasibility of a cost-effective solution for automated waste sorting. The system also provides logging, operational statistics, an offline queue, and an idempotency mechanism to support reliable operation in real-world deployments.
Algoritma Apriori dan Visualisasi Heatmap GIS untuk Evaluasi Ketimpangan Distribusi Bantuan Sosial Senung, Bachtiar; Satriadi D. Ali; Abdul Malik I. Buna; Nuranissa D. Paemo
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9154

Abstract

This study integrates the Apriori algorithm and local spatial analytics to assess inequality in social assistance distribution in Gorontalo Province, Indonesia, covering six regencies/cities. The administrative Beneficiary Master List (BNBA) dataset was standardized for association rule mining to identify co-beneficiary patterns across major social assistance schemes, namely PKH, BPNT, BST, and BPUM. In parallel, the data were aggregated at the sub-district level to construct an inequality score based on Principal Component Analysis (PCA) of beneficiary proportions, which was then analyzed using Local Moran’s I (LISA) and Getis–Ord Gi*. The Apriori analysis of the province-wide dataset produced 64 association rules, 74 frequent itemsets, and 38 unique items. The results indicate strong co-beneficiary relationships among BPNT, BST, BPUM, and PKH, with confidence values ranging from approximately 0.60 to 0.95 and lift values exceeding 10. Spatial analysis shows that five of the six regencies/cities exhibit significant positive spatial autocorrelation (p < 0.10), with particularly strong clustering in Pohuwato (I = 0.9681) and North Gorontalo (I = 0.8331), while Gorontalo Regency shows no statistically significant pattern. LISA cluster maps further identify high-high (HH) clusters in parts of Boalemo and North Gorontalo, as well as low-low (LL) and high-low (HL) areas relevant for policy refinement. These findings suggest that integrating Apriori and local spatial analytics provides an effective operational approach for improving targeting accuracy, reducing overlap in assistance allocation, and identifying areas at risk of under-coverage.
Co-Authors Abdul Malik I Buna Abdul Malik I. Buna Ifriandi Labolo Satriadi D. Ali Senung, Bachtiar Syaifuddin