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All Journal JURIKOM (Jurnal Riset Komputer) KLIK: Kajian Ilmiah Informatika dan Komputer
Ifriandi Labolo
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisis Penerapan Logika Fuzzy Pada Sistem Diagnosis Infeksi Saluran Pernapasan Akut Berbasis Android Ifriandi Labolo; Citra Yustitya Gobel; Satriadi Ali; Muhammad Isla; Ridwan Y Kulu
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.2014

Abstract

Acute respiratory tract infection is a health problem that often occurs and requires a fast and accurate diagnosis to prevent further complications. The high incidence of ARI raises the need for a more accurate and faster diagnosis system. This research aims to analyze the application of fuzzy logic in an expert system so that it can diagnose acute respiratory infections (ARI) in Android-based applications. This expert system uses a fuzzy logic method to handle uncertainty and variability in the symptoms experienced by patients. The method used in this research involves collecting symptom data from patients, which is then processed using fuzzy rules to produce a diagnosis. This system is designed to provide easy access for users via Android devices by facilitating users in entering the symptoms experienced by the patient and providing an initial diagnosis along with the level of confidence in each diagnosis given so that the patient can carry out an initial examination independently before consulting with professional medical personnel. The research results show that the application of fuzzy logic to this expert system is able to provide fairly accurate diagnosis results, seen from the Deviation results. Patients suffering from mild acute respiratory infections with a final score of 4,804. Apart from that, this system also received a positive response from users because of its ease and speed in use. This research concludes that fuzzy logic is effectively applied to expert systems for the diagnosis of respiratory tract infections and has the potential to be further developed to improve health services in the community.
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.
Co-Authors Abdul Malik I Buna Citra Yustitya Gobel Muhammad Isla Nuranissa D. Paemo Ridwan Y Kulu Satriadi Ali Syaifuddin