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: Vegetation Index Analysis Using Multispectral Imagery on Drones for Measurement of Rubber Plant Health (Case Study: Sembawa Rubber Research Center)
Abstrak: Karet merupakan salah satu negara produsen karet terbesar di dunia. Pada tahun 2017 hingga 2019, nilai produksi karet di Indonesia mengalami penurunan dari 3,68 juta ton menjadi 3,32 juta ton. Salah satu penyebab menurunnya produksi karet dikarenakan adanya serangan penyakit gugur daun karet oleh Pestalotiopsis sp. Pada perkebunan karet di Sembawa, beberapa tajuk pohon sudah mengalami gugur daun sehingga menyebabkan gulma tumbuh di bawah pohon karet. Oleh karena itu, penelitian ini bertujuan untuk menganalisis indeks vegetasi yang terbaik dalam membedakan objek karet dan gulma, memverifikasi antara indeks vegetasi dan data ground truth, serta memprediksi tingkat kesehatan karet berdasarkan indeks vegetasi dengan ordinal logistic regression. Data penelitan yang digunakan adalah citra multispektral drone dan data ground truth dari pakar. Indeks vegetasi yang terbaik dalam membedakan objek karet dan gulma berdasarkan hasil segmentasi objek adalah NDRE dan LCI. Pada verifikasi hasil, NDRE dan LCI menunjukkan adanya hubungan dengan tingkat kesehatan karet dari data ground truth. Pada hasil prediksi menggunakan ordinal logistic regression, NDRE menunjukkan akurasi terbaik dalam memperkirakan tingkat kesehatan karet, yaitu sebesar 0,733 atau 73,3%., Rubber is one of the largest rubber-producing countries in the world. From 2017 to 2019, the rubber production value in Indonesia decreased from 3,68 million tons to 3,32 million tons. One of the causes of the decrease in rubber production was the attack of rubber leaf fall disease by Pestalotiopsis sp. In the rubber plantations in Sembawa, some of the tree crowns have leaf fall, causing weeds to grow under the rubber trees. Therefore, this study aims to analyze the best vegetation index in distinguishing rubber and weed objects, verify the vegetation index and ground truth data, and predict the rubber health level based on the vegetation index with ordinal logistic regression. The research data used are drone multispectral images and ground truth data from experts. The best vegetation index in distinguishing rubber and weed objects based on object segmentation results is NDRE and LCI. In the result verification, the NDRE and LCI showed a relationship with the rubber health level from the ground truth data. In the prediction results using ordinal logistic regression, NDRE showed the best accuracy in estimating the health level of rubber, which was 0.733 or 73.3%.
Keyword: drone, indeks vegetasi, karet, multispektral, multispectral, rubber, vegetation index

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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