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Nurlaelah Nurlaelah
Institut Pertanian Bogor
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Artificial Intelligence-Based Irrigation Monitoring Information System For Enhancing Irrigation Rehabilitation Work Supervision Nurlaelah Nurlaelah; Satyanto Krido Saptomo; Moh Yanuar Jarwadi Purwanto
Jurnal Indonesia Sosial Sains Vol. 7 No. 1 (2026): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jiss.v7i1.2182

Abstract

The rehabilitation of irrigation networks plays a critical role in enhancing agricultural productivity and supporting national food self-sufficiency targets as mandated by Presidential Instruction No. 2 of 2025. Traditional monitoring practices still rely on manual reporting, inconsistent documentation, and non–real-time inspections, leading to data inaccuracies and limited transparency. These challenges highlight the need for a more reliable, timely, and objective monitoring system. SIMORI (Irrigation Monitoring Information System) is introduced as an AI-powered digital solution capable of providing real-time data, detecting anomalies, and generating accountable automated reports. This paper explores the relevance and urgency of SIMORI as a strategic instrument to enhance transparency, accountability, and monitoring quality in irrigation rehabilitation projects, thereby accelerating national food security objectives.
Co-Authors Moh Yanuar Jarwadi Purwanto Satyanto Krido Saptomo