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Automated Drip Irrigation System Based on IoT for Chili Plants Using Solar Panel Energy Lamasigi, Zulfrianto Yusrin; Haba, Abd. Rahmat Karim; Jafar, Muh. Iqbal; Syamsir, Syamsir; Hulukati, Stephan A.
Jurnal Pengabdian Masyarakat Vol. 5 No. 1 (2024): Jurnal Pengabdian Masyarakat
Publisher : Institut Teknologi dan Bisnis Asia Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jpm.v5i1.2006

Abstract

Purpose: This research paper aims to address the challenges faced by horticultural farmers in Lauwonu Village, particularly regarding water scarcity exacerbated by hot weather conditions. The study emphasizes the significance of utilizing technology, specifically a drip irrigation system based on IoT and solar panel energy, to mitigate these challenges effectively. Method: The research employed a qualitative approach to investigate the impact of implementing IoT-based drip irrigation systems on chili farming productivity. Data collection methods included surveys and interviews with 15 members of the Mekar Green farmer group. Thematic analysis was utilized to interpret the gathered data. Practical Applications: The findings demonstrate the practical benefits of adopting IoT-driven irrigation technology, enhancing water efficiency and agricultural productivity. This research offers valuable insights for farmers, policymakers, and agricultural practitioners, facilitating informed decision-making and sustainable agricultural practices.Conclusion: Implementing IoT-enabled drip irrigation systems powered by solar panels presents a viable solution to address water scarcity challenges in chili farming. The study underscores the importance of leveraging technology to improve agricultural resilience and productivity, thereby contributing to sustainable food production and livelihoods in rural communities.
Grouping of Areas Based on Flood Disaster Level Using K-Means Clustering Algorithm Hasan, Maryam; Panna, Sudirman S.; Haba, Abd. Rahmat Karim; Alhamad, Apriyanto
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.33145

Abstract

The Province of Gorontalo is highly vulnerable to flood disasters due to its geographical conditions, high rainfall, and uncontrolled land-use changes. This study aims to apply the K-Means Clustering algorithm to classify regions based on flood impact levels to support disaster mitigation and decision-making processes by the National Search and Rescue Agency (BNPP) Gorontalo. The dataset comprises 405 disaster incident records obtained from related institutions, including the number of affected, injured, deceased, and missing individuals. The analysis process involves data collection, preprocessing, distance calculation using the Euclidean Distance method, and the formation of two clusters based on impact levels. The iteration process stopped at the second iteration, indicating that a stable (convergent) condition had been achieved. The results revealed that Cluster 1 (C1) includes areas significantly affected by floods such as Imana, Iloheluma, and Tudi villages, while Cluster 2 (C2) represents unaffected areas like Wapalo, Ilomata, Motihelumo, and others. The implementation of the K-Means algorithm proved effective in identifying disaster-prone regions objectively and data-driven, thus supporting more efficient disaster response planning.