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All Journal Portal Riset dan Inovasi Sistem Perangkat Lunak Inventa: Journal of Science, Technology, and Innovation
Zahra Apriyani Hakim Nasution
Universitas Muhammadiyah Sumatera Utara
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 Industrial & Manufacturing Engineering Mechanical Engineering

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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.
Analisis Dan Implementasi Algoritma Apriori Dalam Memprediksi Banjir Di Kota Medan Pada Bendungan Lau Simeme Zahra Apriyani Hakim Nasution; Mulkan Azhari
Portal Riset dan Inovasi Sistem Perangkat Lunak Vol. 4 No. 3 (2026): Artikel Penelitian
Publisher : SoraTekno Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59696/prinsip.v4i3.239

Abstract

Banjir merupakan salah satu bencana alam yang sering terjadi di Kota Medan dan sekitarnya, khususnya pada daerah yang berdekatan dengan Bendungan Lau Simeme. Faktor-faktor seperti curah hujan tinggi, kapasitas tampungan air yang terbatas, serta kondisi lingkungan yang tidak stabil menjadi penyebab utama terjadinya banjir. Oleh karena itu, diperlukan suatu sistem yang mampu menganalisis data historis dan memprediksi potensi banjir secara lebih akurat. Data yang digunakan berasal dari catatan klimatologi dan hidrologi di wilayah Bendungan Lau Simeme. Proses analisis dilakukan melalui tahap pra-pemrosesan data, penerapan algoritma Apriori, serta evaluasi hasil menggunakan support dan confidence untuk menentukan tingkat keakuratan pola. Hasil penelitian menunjukkan bahwa algoritma Apriori mampu mengidentifikasi kombinasi faktor yang berpotensi menyebabkan banjir dengan tingkat akurasi yang baik. Dengan demikian, implementasi algoritma Apriori dapat dijadikan sebagai alat bantu dalam sistem peringatan dini banjir di Kota Medan, khususnya di sekitar Bendungan Lau Simeme, guna mendukung upaya mitigasi dan pengambilan keputusan yang lebih efektif.
Co-Authors Mulkan Azhari