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All Journal Creative Information Technology Journal SISFOTENIKA JURIKOM (Jurnal Riset Komputer) CCIT (Creative Communication and Innovative Technology) Journal IT (INFORMATIC TECHNIQUE) JOURNAL TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Social Responsibility Projects by Higher Education Forum BEES: Bulletin of Electrical and Electronics Engineering Journal of Scientech Research and Development Journal of Community Service Information System and Data Science Jurnal ICT : Information and Communication Technologies Bulletin of Informatics and Data Science Jurnal ABDIMAS MUTIARA PUBLIDIMAS CSRID
Iwan Fitrianto Rahmad
Universitas Potensi Utama
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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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.
Co-Authors Aditya, Elsa Akbar, M. Ridho Alfa Saleh Alfa Soleh Alfa Soleh Amri, Aulia Ardani, Tira Azhar, Wahyu Bob Subahan Riza Budi Triandi Budi Triandi Budi Triandi, Budi Doni, Rahmad Edy Victor Haryanto, Edy Victor Erdiana Gultom Evri Ekadiansyah Evri Ekadiansyah Fragastia, Vidi Agung Ginting, Ria Ukur Harahap, Nur Anzelina Hardianto Hardianto Harold Situmorang, Harold Helmi Kurniawan Helmi Syaiful Rahman Hestina Irfannur Diah Juanda Hakim Lubis Juanda Hakim Lubis Juanda Hakim Lubis Khairul Ummi, Khairul Lili Tanti Lili Tanti, Lili Lubis, Muhammad Luthfi Moratuah M Rizky M. Alif Firdiansyah Maulana Fajar Sidik Mesran Mesran, M.Kom Muhammad Arif Prayudi, Muhammad Arif Muhammad Yusra Rustam Nur Fadillah Pairin, Yusfrizal Bin Pasaribu, Jamil Puji Widodo Purba, Adelina Rahmad Aditya Rahmad Aditya Rahmad Doni Rahmad Doni Rahmad Doni Rahmad, Sofyan Ratih Puspasari Reza Hermawan Riah Ukur Ginting, Riah Ukur Robby Ardyanto Marsudi Samsir Samsir Samsir Samsir, Samsir Setiawan, Adil Siregar, Rahmadani Sri Mulia Mz Tanjung, Dahriyani Hakim Tumbur Munthe Wahyu Azhar Wiwi Verina Yudhi Andrian