Judul: Penerapan Algoritme Maximum Likelihood dan k-Nearest Neighbour untuk Identifikasi Lahan Bawang Putih Menggunakan Citra Sentinel-1A
Abstrak: Komoditas bawang putih (Allium sativum) di Indonesia dikenal memiliki banyak khasiat. Seiring dengan pertumbuhan penduduk, konsumsi bawang putih juga semakin meningkat. Namun, ketersediaannya belum mencukupi seluruh permintaan yang ada. Kementerian Pertanian merencanakan swasembada bawang putih pada 2030 di salah satu sentra penanaman bawang putih Kecamatan Sembalun, Lombok Timur untuk memenuhi permintaan dengan meningkatkan lahan perkebunan. Sehingga penelitian lanjutan untuk identifikasi lahan bawang putih menjadi penting. Penelitian ini bertujuan untuk menerapkan algoritme yang dapat mengindentifikasi lahan bawang putih menggunakan citra Sentinel-1A di Kecamatan Sembalun, Lombok Timur. Model klasifikasi dibuat menggunakan algoritme k-Nearest Neighbour (kNN) dan Maximum Likelihood Classification (MLC). Model klasifikasi memiliki akurasi terbaik yaitu 78,81% pada algoritme kNN dengan koefisien kappa 0,58 dan 76,31% pada algoritme MLC dengan koefisien kappa 0,53. Berdasarkan hasil tersebut, penelitian ini menyimpulkan bahwa mode klasifikasi tersebut dapat mengidentifikasi lahan bawang putih., The commodity of garlic (Allium sativum) in Indonesia is known to have many health benefits. Along with population growth, the consumption of garlic is increasing. However, its availability is not sufficient for all existing demands. In 2030, The Ministry of Agriculture plans to achieve garlic self-sufficiency in one of the centre garlic field in Sembalun District, East Lombok by enlarging the area. Therefore, further research to identify garlic field is important. This study aims to apply classification algorithms that can identify garlic fields using Sentinel-1A imageries in Sembalun District, East Lombok. The classification model is created using the k-Nearest Neighbour (kNN) algorithm and Maximum Likelihood Classification (MLC). The classification models have the best accuracy 78,81% for the kNN algorithm with a kappa coefficient of 0,58 and 76,31% for the MLC algorithm with a kappa coefficient of 0,53. Based on these results, this study concludes that the classification model is able to identify garlic fields.
Keyword: garlic, k-nearest neighbour, classification, maximum likelihood classification, sentinel-1A

Judul: Application of Random Forest Algorithm on Sentinel-1A Imagery for Garlic Field Identification in Sembalun
Abstrak: Bawang putih (Allium sativum L.) merupakan tanaman hortikultura yang memiliki banyak manfaat dalam kehidupan manusia baik dalam bidang medis maupun non medis. Daerah Sembalun merupakan dataran tinggi (>700 meter dpl) yang memiliki luas lahan mencapai 4000 hektar sehingga cocok untuk pengembangan komoditas bawang putih. Penelitian ini bertujuan untuk menerapkan algoritme ensemble learning yang dapat meningkatkan akurasi dalam pemetaan lahan bawang putih menggunakan citra Sentinel 1-A. Data yang digunakan pada penelitian ini adalah Citra Sentinel-1A daerah Sembalun periode Juni dan November 2019. Pengklasifikasian citra Sentinel-1A menggunakan algoritme ensemble learning yaitu Random Forest. Penelitian ini menerapkan model dengan tiga skenario dengan akurasi terbaik sebesar 78,45% dan rata-rata akurasi pada semua skenario sebesar 76,55%. Rata-rata akurasi pada algoritme pembelajaran ensemble mendapatkan nilai yang lebih tinggi 0,1% dibandingkan penerapan algoritme decision tree C5.0 pada penelitian sebelumnya., Garlic (Allium sativum L.) is a horticultural plant that has many benefits in human life, both in the medical and non-medical fields. The Sembalun area is a highland (>700 meters above sea level) which has a land area of 4000 hectares so it is suitable for the development of garlic commodities. This study aims to apply an ensemble learning algorithm that can improve accuracy in mapping garlic fields using Sentinel 1-A imagery. The data used in this study is the Sentinel-1A image of the Sembalun area for the period June and November 2019. The classification of the Sentinel-1A image uses an ensemble learning algorithm, namely Random Forest. This study applies a model with three scenarios with the best accuracy of 78.45% and an average accuracy of 76.55% in all scenarios. The average accuracy of the ensemble learning algorithm is 0.1% higher than the application of the C5.0 decision tree algorithm in the previous study.
Keyword: garlic, Sembalun, ensemble learning, image classification, Sentinel-1A

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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