Optimisasi Penempatan Sensor Automatic Weather Station di Perkebunan Kopi Menggunakan Pendekatan Metric Dimension Graf Join

Nurul Maulida Surbakti, Dinda Kartika, Zul Amry, Muhammad Ashari

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Penelitian ini bertujuan untuk menganalisis metric dimension dari graf join sebagai model matematis untuk optimasi penempatan sensor pada sistem Automatic Weather Station (AWS) berbasis Internet of Things (IoT) di perkebunan kopi. Penelitian ini dilatarbelakangi oleh kebutuhan sistem pemantauan iklim mikro yang efisien, dengan jumlah sensor minimum namun tetap mampu memberikan informasi lingkungan secara akurat dan berkelanjutan. Metode yang digunakan meliputi analisis matematis berbasis konsep resolving set dalam teori graf untuk menentukan nilai metric dimension, simulasi komputasi menggunakan algoritma Breadth-First Search (BFS) berbasis Python untuk memverifikasi hasil teoretis, serta implementasi prototipe AWS untuk pengukuran parameter lingkungan. Hasil penelitian menunjukkan bahwa metric dimension graf  adalah   untuk setiap , yang merepresentasikan jumlah minimum sensor yang diperlukan untuk membedakan seluruh titik pemantauan secara unik. Hasil simulasi komputasi konsisten dengan analisis teoretis, sehingga mengonfirmasi validitas model yang diusulkan. Implementasi prototipe AWS di lapangan menunjukkan bahwa pendekatan ini mampu menghasilkan distribusi sensor yang efisien sekaligus menyediakan data iklim mikro yang akurat. Kontribusi utama penelitian ini adalah pengembangan model optimasi penempatan sensor berbasis metric dimension pada graf join  yang diintegrasikan dengan sistem AWS berbasis IoT. Secara praktis, temuan ini berimplikasi pada peningkatan efisiensi biaya, pengurangan konsumsi energi, serta peningkatan keandalan sistem pemantauan lingkungan. Pendekatan ini juga menyediakan kerangka yang skalabel untuk optimasi penempatan sensor pada berbagai aplikasi jaringan sensor dalam pertanian presisi dan pemantauan lingkungan.

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DOI: http://dx.doi.org/10.25157/teorema.v11i1.20971

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