Judul: Pemodelan Keparahan Penyakit Tanaman Karet Berbasis Citra Multispektral Drone Menggunakan Algoritme Random Forest
Abstrak: Karet menjadi salah satu komoditas ekspor yang menyumbang devisa nonmigas bagi negara. Indonesia merupakan negara dengan perkebunan karet terbesar di dunia. Pada tahun 2018 hingga 2020, produksi karet mengalami penurunan setiap tahunnya dari 3,63 juta ton menjadi 3,04 juta ton. Salah satu penyebab, yaitu penyakit Gugur Daun Karet (GDK) yang menyerang perkebunan karet pada berbagai wilayah di Indonesia. Penyakit GDK disebabkan oleh cendawan Pestalotiopsis sp. yang menyebar secara cepat melalui udara dan menyebabkan rontoknya daun karet hingga turunnya produksi getah. Oleh karena itu, penelitian ini bertujuan memodelkan keparahan penyakit tanaman karet menggunakan metode random forest melibatkan indeks NDRE dan LCI. Data penelitian yang digunakan terdiri dari citra multispektral drone dan data ground truth yang dilabeli berdasarkan citra hemispherical. Pada data ground truth, terdapat data yang overlap dengan jumlah relatif tinggi sehingga jumlahnya perlu dikurangi menggunakan prinsip densitas. Pada hasil pemodelan menggunakan random forest dengan optimasi, diperoleh rataan skor training sebesar 0.8225 atau 82.25% dengan akurasi skor testing sebesar 0.8246 atau 82.46% dan f1-score sebesar 0.8176 atau 81.76%. Kata kunci: drone, indeks vegetasi, multispektral, karet, severity, Rubber is one of the export commodities that contributes non-oil and gas foreign exchange to Indonesia. Indonesia is the country with the largest rubber plantation in the world. From 2018 to 2020, rubber production has decreased every year from 3.63 million tons to 3.04 million tons. One of the factors is the Gugur Daun Karet (GDK) disease that attacks rubber plantations in various regions in Indonesia. GDK disease is caused by the fungus Pestalotiopsis sp. which spreads rapidly through the air and causes the fall of rubber leaves to decrease latex production. Therefore, this study aims for modeling the disease severity of rubber plants using the random forest method by involving the NDRE and LCI indices. The research data used consists of multispectral drone images and ground truth data labeled based on hemispherical images. There is a relatively high amount of overlapping data in ground truth data. Therefore, the amount needs to be reduced using the density principle. The modeling results using random forest with optimization, the average training score is 0.8225 or 82.25% with a testing score accuracy of 0.8246 or 82.46% and f1-score of 0.8176 or 81.76%.
Keyword: drone, indeks vegetasi, multispektral, karet, severity

Judul: Segmentasi dan Pengukuran Tingkat Kesehatan Tanaman Karet Berbasis Citra Satelit Sentinel-2A Menggunakan Algoritma Random Forest (Studi Kasus Perkebunan Karet Sembawa)
Abstrak: Karet merupakan salah satu komoditi perkebunan penting di Indonesia. Sayangnya produksi karet tahun 2017 sampai2019 mengalami penurunan. Salah satu penyebabnya yaitu gangguan penyakit gugur daun oleh patogen Pestalotiopsis sp. Maka dari itu perlu adanya analisis nilai indeks kesehatan tanaman karet, yang bisa dilakukan menggunakan teknologi remote sensing. Salah satunya yaitu menggunakan citra satelit Sentinel-2A dengan bantuan software Google Earth Engine yang dapat memantau suatu area dalam lingkup yang luas dan dalam periode yang panjang sehingga akan menghemat tenaga dan waktu jika dibandingkan dengan cara manual. Pengklasifikasian untuk memisahkan area perkebunan karet dengan objek lain di sekitarnya terlebih dahulu perlu dilakukan yaitu menggunakan metode klasifikasi berbasis objek dengan algoritma Simple Non-Iterative Clustering dan random forest. Akurasi terbaik dari model klasifikasi yaitu 78,04%. Di antara indeks-indeks yang dianalisis nilainya, indeks Normalized Difference Red Edge dan Leaf Chlorophyll Index dinilai paling cocok untuk dilakukan analisis. Hasil analisis memperlihatkan, penyakit gugur daun menyebabkan kondisi kesehatan tanaman karet menurun yang diperburuk oleh La Nina., Rubber is one of the important plantation commodities in Indonesia. Rubber production from 2017 to 2019 has decreased. One of the causes is the disruption of a diseases caused by the pathogen Pestalotiopsis sp. Therefore it is necessary to monitor the health of rubber plants which can be done by using remote sensing technology. One of them is using satellite imagery with the help of Google Earth Engine software. Classification to separate the rubber area from various objects around it needs to be done first. One of the methods is object-based classification method using Simple Non-Iterative Clustering and Random Forest algorithm. The best model has 78,04% accuracy. Among all the indices, Normalized Difference Red Edge dan Leaf Chlorophyll Index are considered the most suitable for analysis. The results show that Pestalotiopsis sp leaf fall disease causes the health condition of rubber plants to decline, which is exacerbated by La Nina.
Keyword: Google Earth Engine, Klasifikasi Berbasis Objek, Karet, Remote Sensing

Judul: Hamiltonian formulation for linear waves motion in two layers fluid
Abstrak: The surface wave could be considered as a wave that separates two fluids, namely water and air. Based on this assumption, it is introduced the interfacial wave, a wave between two layers of fluid with different density. The formulation of interfacial waves motion begins with deriving the base equation of irrotational ideal fluid. Furthermore, according to irrotational fluid assumption, the base equation can be stated in velocity potential. In this derivation, the fluids domain is assumed to be restricted by rigid lid boundary conditions, both at the upper and lower limit. Therefore, the interfacial waves motion can be explained in a hamiltonian formulation. In the hamiltonian formulation, total energy is defined as the sum of kinetic and potential energy. The hamiltonian system is obtained from reduction of kinetic energy by using the Dirichlet Neumann Operator. The resulted kinetic energy equation is nonlinear. Therefore, this form is linearized by first part of the Taylor expansion. This linearization gives a dispersion relation of linear wave. Based on this dispersion relation, the phase speed of the linear wave depends on the density ratio of the two layers fluid.
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