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Azhar, Wahyu
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Peringatan Dini Bencana Banjir Berbasis Iot Menggunakan Pendekatan Metode Prediktif Aditya, Rahmad; Samsir, Samsir; Azhar, Wahyu; Rahmad, Iwan Fitrianto
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 2 (2024): June 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.2.2024.161-173

Abstract

Floods are among the natural disasters that can cause substantial damage, particularly in tourist locations with high visitor traffic. This paper proposes the implementation of an IoT-based predictive method for early flood disaster warnings in tourist areas. The proposed system utilizes IoT sensors to monitor environmental conditions in real-time and employs machine learning-based predictive models to forecast the likelihood of flooding. By continuously collecting and analyzing data such as rainfall, river water levels, and soil moisture, the system can predict potential flood events with a relatively high degree of accuracy. The research involved developing and testing the system in a controlled environment to evaluate its performance. The results demonstrated that the system could provide timely early warnings, allowing tourist site managers to take necessary preventive measures to protect visitors and infrastructure. The implementation of such a system can significantly reduce the impact of floods by providing actionable information well in advance of potential disasters. This early warning capability is crucial in tourist areas where rapid response is necessary to ensure the safety and well-being of visitors. Overall, the study highlights the effectiveness of combining IoT technology with predictive analytics in disaster management and risk mitigation
Sistem Pakar Diagnosa Penyakit Perokok Menggunakan Metode Backward chaining Subagio, Selamat; Rahmayani, Rahmayani; Samsir, Samsir; Azhar, Wahyu
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 3 (2025): Oktober 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.3.2025.340-353

Abstract

he rapid development of information and computer technology has had a significant impact on various fields, including healthcare. One of its applications is the expert system, a computer-based system utilizing Artificial Intelligence (AI) designed to imitate the reasoning and decision-making abilities of human experts. Expert systems are widely used to assist in diagnosing diseases based on symptoms experienced by patients, providing fast, efficient, and accurate solutions without requiring direct consultation with medical professionals. This study focuses on developing an Expert System for Diagnosing Smoking-Related Diseases among Lecturers at Universitas Al Washliyah Labuhanbatu. The system aims to help users, particularly active smokers, identify potential diseases caused by smoking habits. Based on preliminary studies and interviews conducted with the Health Department of Rantauprapat City, it was found that common diseases suffered by smokers include oral disease, lung disease, respiratory disorders, throat disease, and heart disease. These illnesses often develop unnoticed in the early stages, making early diagnosis essential for prevention and health awareness. The research applies the Backward Chaining inference method, which works by reasoning backward from a possible conclusion (disease) to find supporting facts (symptoms). The relationship between symptoms and diseases is represented through IF–THEN rules derived from expert knowledge. The system was developed using Macromedia Dreamweaver 8 as a web editor and MySQL as the database management system to store information on diseases, symptoms, and diagnostic results. The implementation results show that the system can provide early diagnoses quickly and accurately based on user-input symptoms. Furthermore, the system includes a confidence level feature that presents diagnostic certainty in percentage form. Hence, the developed expert system not only serves as a medical decision-support tool but also as a digital health education medium that promotes awareness of smoking dangers and the importance of maintaining a healthy lifestyle.