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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) JSI (Jurnal sistem Informasi) Universitas Suryadarma Indonesian Journal of Education and Mathematical Science IT (INFORMATIC TECHNIQUE) JOURNAL Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Blend Sains Jurnal Teknik Jutech: Jurnal Teknologi Informasi Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer DEVOTE: Jurnal Pengabdian Masyarakat Global Jurnal Komprehenshif Hanif Journal of Information Systems Portal Riset dan Inovasi Sistem Perangkat Lunak Al'Adzkiya International of Computer Science and Information Technology Journal Tsabit Inventa: Journal of Science, Technology, and Innovation
Mulkan Azhari
Universitas Muhammadiyah Sumatera Utara
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Journal : Inventa: Journal of Science, Technology, and Innovation

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Analisis dan Implementasi Algoritma Apriori dalam Memprediksi Banjir di Kota Medan pada Bendungan Lau Simeme Zahra Apriyani Hakim Nasution; Mulkan Azhari
Journal of Science, Technology, and Innovation Vol 1 No 3 (2026): : April: Inventa: Journal of Science, Technology, and Innovation
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/4cn1ym72

Abstract

This study aims to analyze and implement the Apriori algorithm to predict flood potential at the Lau Simeme Dam in Medan City by identifying association patterns among rainfall, water discharge, and water level parameters based on daily hydrological data. The main challenge of this study lies in the limitations of conventional mitigation systems, which are not yet capable of systematically and adaptively interpreting multivariate environmental relationships. The research method employs an empirical data mining-based approach, involving data preprocessing, numerical transformation into transactional data, frequent item set formation, and association rule derivation using minimum support and confidence parameters. The system was developed using Python and MySQL to support the operational analysis and visualization of prediction results. The results show that the Apriori algorithm is capable of generating consistent association patterns between heavy rainfall, increased water discharge, and flood alert status with an accuracy of 97.81%, precision of 100%, and recall of 97.81%. These findings indicate that association rule-based models possess interpretive and predictive capabilities relevant to supporting flood mitigation based on hydrological data.
Co-Authors Al Khowarizmi Andi Zulherry Dalimunthe, Ahmad Al Qodri Azizi Efendi, Syahril Febrina, Nadia Ferdy Riza Halim Maulana Husaini, Abdillah Ichsan, Aulia ILHAM Indah Purnama Sari Jannah, Aulia Khairani, Ade Irma Kipa Jundapri Manurung, Asrar Aspia Maulana, Eka Syarif Maulia Rahman Nasution, Perdinal Oris Krianto Sulaiman Prastia, Ilham Purwanto Purwanto Putra, Aldryan Bhara Rifqi Yafik Rika Rosnelly, Rika Siti Khairun Nisa Situmorang, Zakaria Syafik, Hafizan Wulandari, Anggy Zahra Apriyani Hakim Nasution