Implementasi Algoritma Random Forest untuk Klasifikasi Keberhasilan Pembibitan Bougenville Berbasis Internet of Things

Authors

  • Ilham Firmansyah Universitas Nusantara PGRI Kediri Indonesia
  • Julian Sahertian Universitas Nusantara PGRI Kediri Indonesia
  • Ratih Kumalasari Niswatin Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/cj6gk313

Abstract

Efisiensi pembibitan vegetatif bougenville sangat dipengaruhi oleh stabilitas mikroklimat. Pemantauan konvensional pada skala usaha mikro sering memicu kegagalan akibat keterlambatan penanganan fluktuasi lingkungan. Penelitian ini mengintegrasikan sistem pemantauan berbasis Internet of Things dengan algoritma Random Forest untuk mengklasifikasikan keberhasilan pembibitan selama siklus empat puluh hari. Sistem mengakuisisi data suhu, kelembaban udara, intensitas cahaya, kelembaban tanah, dan pH secara kontinu. Eksperimen menggunakan basis data berisi 6.659 rekaman menunjukkan model klasifikasi mencapai akurasi sebesar 82,21 persen. Melalui analisis tingkat kepentingan fitur, ditemukan bahwa suhu udara, kelembaban tanah, dan pH merupakan parameter paling deterministik dalam kelangsungan hidup stek batang. Sistem ini berfungsi sebagai instrumen pendukung keputusan untuk optimalisasi produksi hortikultura cerdas

Keywords:

bougenville, Internet of Things, klasifikasi, monitoring lingkungan, Random Forest.

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Published

2026-07-20

Issue

Vol. 10 No. 3 (2026): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2026

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How to Cite

Implementasi Algoritma Random Forest untuk Klasifikasi Keberhasilan Pembibitan Bougenville Berbasis Internet of Things. (2026). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 10(3), 2144-2151. https://doi.org/10.29407/cj6gk313