Drone Spray for Base Rot Disease Detection in Oil Palm Plants aims to increase the effectiveness of disease detection and control using drone technology. Stem base rot disease, caused by the fungus Ganoderma boninense, threatens oil palm productivity in Indonesia. Drone technology with near-infrared (NIR) sensors and image processing is used to detect early signs of the disease, enabling timely preventive action. A drone equipped with a Raspberry Pi camera and NoIR V2 pro-duces high-resolution images for NIR reflectance analysis. The research included field observations, expert interviews, and sensor data collection for evaluation of the effectiveness of this technology. Results show that the use of drones can in-crease disease detection efficiency by 85%, reduce operational costs, and minimize health risks for workers. This drone technology has great potential to improve the productivity and sustainability of the palm oil industry in Indonesia. This research recommends the adoption of drone technology in the agricultural sector to address plant disease challenges more efficiently and effectively.

Keywords: Disease, Drone, NIR, Oil Palm.

ABSTRAK

Drone Semprot untuk Deteksi Penyakit Busuk Pangkal Batang pada Tanaman Kelapa Sawit bertujuan meningkatkan efektivitas deteksi dan pengendalian penyakit menggunakan teknologi drone. Penyakit busuk pangkal batang, disebabkan oleh jamur Ganoderma boninense, mengancam produktivitas kelapa sawit di Indonesia. Teknologi drone dengan sensor inframerah dekat (NIR) dan image processing digunakan untuk mendeteksi tanda-tanda awal penyakit, memungkinkan tindakan preventif yang tepat waktu. Drone yang dilengkapi kamera Raspberry Pi dan NoIR V2 menghasilkan citra yang memiliki resolusi tinggi untuk analisis reflektansi NIR. Penelitian ini mencakup observasi lapangan, wawancara dengan pakar, dan pengumpulan data sensor untuk evaluasi efektivitas teknologi ini. Hasil menunjukkan bahwa penggunaan drone dapat meningkatkan efisiensi deteksi penyakit hingga 85%, mengurangi biaya operasional, dan meminimalkan risiko kesehatan bagi pekerja. Teknologi drone ini berpotensi besar dalam meningkatkan produktivitas dan keberlanjutan industri kelapa sawit di Indonesia. Penelitian ini merekomendasikan adopsi teknologi drone dalam sektor pertanian untuk mengatasi tantangan penyakit tanaman secara lebih efisien dan efektif.

Salah satu institusi pelayanan kesehatan yang menyelenggarakan pelayanan kesehatan perorangan secara paripurna yang menyediakan pelayanan rawat inap, rawat jalan dan gawat darurat di Kota Padang. Namun, masih banyak keluhan yang dirasakan oleh pegawai RS Universitas Andalas. Penelitian ini bertujuan untuk mengetahui disiplin kerja, motivasi kerja dan kinerja pegawai Rumah Sakit Universitas Andalas. Penentuan sampel dilakukan dengan teknik purposive sampling, dengan jumlah karyawan sebanyak 132 orang. Pengumpulan data dilakukan dengan menyebarkan kuesioner kemudian dianalisis secara deskriptif dan Independent-Sample T-Test dengan program SPSS versi 21. Hasil analisis data menunjukkan bahwa disiplin kerja dan motivasi kerja berpengaruh positif terhadap kinerja pegawai RS Universitas Andalas.

By looking at current technological developments that already use automatic sensors, the application of the concept to this system can be a solution for dealing with ornamental fish farming owners. In this research, a tool is made that can perform feeding, checking temperature, water turbidity, feed level, water level and water pump control, from this information will be sent to android by firebase automatically monitored and controlled anywhere via android. This tool is designed with ease of maintenance and is an extension of the existing tools. IoT technology is used to monitor online, based on Firebase and Nodemcu Esp8266. In this study, researchers used the research method of the Borg and Gall (1987) model from 10 steps to 6 steps. The results of this study were carried out by an expert validation test to get a value of 30 which means "Very Good (Valid)". While the effectiveness test by 10 users plus by experts and employee users get a total score of 35.16 which means the effectiveness test is classified as "Very Good (Valid)" it can be said that the new system is more effective than the old system.