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Journal : CSRID

Peringatan Dini Bencana Banjir Berbasis Iot Menggunakan Pendekatan Metode Prediktif Rahmad Aditya; Samsir Samsir; Wahyu Azhar; Iwan Fitrianto Rahmad
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
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
Plagiarism Detection System Level of Similiarity of Title Thesis Using the Winnowing Algorithm (Case Study: Information Systems Study Program) Ginting, Riah Ukur; Purba, Adelina; Situmorang, Harold; Rahmad, Iwan Fitrianto
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 3 (2024): October 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.3.2024.316-329

Abstract

The Winnowing Algorithm is a system built to determine the similarity of the title of a proposed thesis with previously existing thesis titles, so it is necessary to apply an algorithm to determine the similarity of titles. One of the algorithms for determining the similarity of titles is the Winnowing algorithm. The Winnowing algorithm is a method used to detect similar words/sentences (common subsequence) in two thesis titles being compared. Two thesis titles are known to have the same words / sentences if a fingerprint is found in the document, this fingerprint will be used as a basis for comparison between thesis titles, this algorithm will look for fingerprints (similarities in the two thesis titles) by changing the n-gram of the title into the form The number value is called a hash value, the technique for finding this value is hashing. The research results show that the winnowing algorithm is proven to be effective in detecting similarities in thesis titles in information systems study programs with a threshold of 10%. The system built is able to provide accurate and fast results in identifying similarities in thesis titles, and this system really helps students in finding topics that are original and have never been researched before. Apart from that, the supervisor can easily verify the similarity of the thesis title to avoid plagiarism.