PENERAPAN SISTEM MONITORING PERTUMBUHAN JAGUNG BERBASIS IOT DAN MACHINE LEARNING UNTUK MENDUKUNG PERTANIAN CERDAS

Dinial Utami Nurul Qomariah; Ade Irma Elvira; Ratna Yuniati

doi:10.31764/jmm.v10i3.39143

PENERAPAN SISTEM MONITORING PERTUMBUHAN JAGUNG BERBASIS IOT DAN MACHINE LEARNING UNTUK MENDUKUNG PERTANIAN CERDAS

Dinial Utami Nurul Qomariah, Ade Irma Elvira, Ratna Yuniati

Abstract

Abstrak: Kegiatan pengabdian kepada masyarakat ini dilaksanakan pada kelompok tani di Desa Jembatan, Kecamatan Kesamben, Kabupaten Jombang, yang didominasi oleh komoditas padi dan jagung. Permasalahan utama mitra meliputi keterbatasan pemantauan kondisi lahan secara real-time serta pengelolaan irigasi yang masih konvensional. Kegiatan ini bertujuan meningkatkan pemahaman dan kapasitas petani dalam pemanfaatan teknologi pertanian cerdas melalui implementasi sistem monitoring pertumbuhan jagung berbasis Internet of Things (IoT) dan Machine Learning. Pelaksanaan kegiatan menggunakan pendekatan kolaborasi multipihak antara kelompok tani, akademisi, dan media dengan metode Participatory Rural Appraisal (PRA). Evaluasi dilakukan melalui pretest–posttest untuk mengukur peningkatan hard skill dan soft skill. Hasil evaluasi menunjukkan adanya peningkatan rata-rata nilai dari 50,25 menjadi 78,75 atau sebesar 58,8%. Kegiatan ini berkontribusi dalam mendorong penerapan pertanian cerdas secara berkelanjutan.

Abstract: This community service activity was conducted with a farmer group in Desa Jembatan, Kecamatan Kesamben, Kabupaten Jombang, where agricultural activities are predominantly focused on rice and corn. The main problems faced by the partners include limitations in real-time land condition monitoring and conventional irrigation management practices. This activity aims to enhance farmers’ understanding and capacity in utilizing smart agriculture technologies through the implementation of a corn growth monitoring system based on the Internet of Things (IoT) and Machine Learning. The implementation employed a multi-stakeholder collaboration approach involving farmer groups, academics, and media using the Participatory Rural Appraisal (PRA) method. Evaluation was carried out using a pretest–posttest approach to measure improvements in both hard skills and soft skills. The evaluation results indicate an increase in the average score from 50.25 to 78.75, representing an improvement of 58.8%. This activity contributes to promoting the sustainable adoption of smart agriculture.

Keywords

IoT; Machine Learning; Corn; Smart Agriculture; PRA.

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DOI: https://doi.org/10.31764/jmm.v10i3.39143