Judul: Penerapan Algoritme Support Vector Machine pada Citra Sentinel-2A untuk Klasifikasi Fase Pertumbuhan Bawang Putih
Abstrak: Peningkatan kebutuhan konsumsi bawang putih (Allium sativum L.) di Indonesia tidak diimbangi dengan peningkatan jumlah produksinya. Pemerintah melakukan program ekspansi lahan penanaman bawang putih di Sembalun, Lombok Timur untuk mencapai swasembada bawang putih 2030-2045. Penelitian ini bertujuan untuk membuat model klasifikasi lahan bawang putih berdasarkan fase pertumbuhan bawang putih di Sembalun. Data yang digunakan adalah citra Sentinel-2A yang diakuisisi tanggal 1 Juli 2021 dan periode 31 Juli 2021. Algoritme yang digunakan adalah support vector machine (SVM) untuk klasifikasi data banyak kelas. Penelitian ini menerapkan model dengan dua skenario data yang memiliki perbedaan atribut. Model klasifikasi terbaik terdapat pada skenario kedua. Model ini memiliki akurasi terbaik sebesar 72,9% dengan nilai precision dan recall masing-masing sebesar 70,8% dan 71,4%. Berdasarkan evaluasi hasil klasifikasi, model SVM ini mampu mengidentifikasi fase pertumbuhan bawang putih di Sembalun., The increasing of need for consumption of garlic (Allium sativum L.) in Indonesia is not matched by an increase in the amount of production. The government carried out a program to expand the garlic planting area in Sembalun, East Lombok to achieve garlic self-sufficiency in 2030-2045. This study aims to create a model to classify the growth phase of garlic in Sembalun. The data used are Sentinel-2A imagery which was acquired on July 1st, 2021 and July 31st, 2021. The algorithm used is support vector machine (SVM) for multiclass classification data. This study applies a model with two data scenarios that have different attributes. The best classification model is found in the second scenario. This model has the best accuracy of 72.9% with precision and recall values of 70.8% and 71.4%, respectively. Based on the evaluation of the classification results, this SVM model able to identify the growth phase of garlic in Sembalun.
Keyword: bawang putih/garlic, fase pertumbuhan/growing phase, klasifikasi citra/image classification, Sentinel-2A, support vector machine

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: Approximation of the Median of Poisson Distribution Using the Relation of Poisson-Gamma Distribution
Abstrak: A major drawback of median is that median cannot usually be defined in closed form, even in the cases where their distribution functions are known. This study gives an approximation of the closed form for the median of the Poisson distribution by using the relation of Poisson-Gamma distribution. The approximation uses elementary techniques based on the monotonicity of certain sequences involving tail probabilities of the Poisson distribution and the Central Limit Theorem. Furthermore, a closed form expression for the mean absolute deviation 􀜧|􀜰􀰒 􀵆 􀜯􀝁􀝀􁈺􀜰􀰒􁈻| is also studied. The median is relevant because of the fact that it minimizes the mean absolute deviation of a Poisson random variable (􀜰􀰒􁈻 by considering median as central tendency.
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