Judul: Segmentasi Citra Hemispherical Menggunakan Metode Multi- Otsu untuk mengukur penutupan Kanopi Tanaman Karet (Studi  Kasus Puslit Karet Sembawa)
Abstrak: Monitoring perkebunan karet perlu dilakukan supaya penyebaran penyakit bisa dikontrol. Hemispherical photography bisa menjadi salah satu metode untuk menentukan keparahan penyakit di suatu area perkebunan tanaman karet. Tujuan dari penelitian ini adalah untuk menganalisis hubungan antara nilai canopy closure (CC) dengan tingkat keparahan penyakit dari tanaman karet. Metode penelitian ini terdiri dari pengambilan data, pembersihan dan pemilihan citra, Identifikasi keakuratan threshold, analisis hasil segmentasi, dan perbandingan CC dengan indeks lain. Identifikasi nilai threshold menghasilkan satu threshold sebagai threshold paling optimal dengan nilai F-score 0.633. Uji dengan algoritma k-nearest neighbors (k-NN) menghasilkan akurasi sebesar 0.875 untuk gambar identifikasi ahli dan 0.599 untuk semua citra. Nilai CC menunjukkan nilai tertinggi jika dibandingkan dengan nilai indeks NDRE dan LCI. Hal ini menunjukkan bahwa nilai CC bisa digunakan sebagai fitur untuk mengklasifikasikan tingkat keparahan penyakit, Monitoring rubber plantations is necessary to control the spread of diseases. Hemispherical photography can be one of the methods to determine disease severity in a rubber plantation area. The aim of this research is to analyze the relationship between canopy closure (CC) values and the level of disease severity in rubber plants. The research methodology consists of data collection, image cleaning and selection, accuracy threshold identification, segmentation result analysis, and comparison of CC with other indices. The identification of threshold values yields a single optimal threshold with an F-score value of 0.633. Testing using the k- nearest neighbors (k-NN) algorithm results in an accuracy of 0.875 for expert identification images and 0.599 for all images. CC values exhibit the highest value when compared to NDRE and LCI index values. This indicates that CC values can be used as features to classify the level of disease severity.
Keyword: segmentasi, hemispherical, remote sensing

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: 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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