Judul: Determination of Oil Palm&#8217;s Age Using Artificial Neural Network Method and Sentinel-2 Based Vegetation Indices
Abstrak: Kelapa sawit menjadi varietas unggul dalam total produksi dan distribusi minyak nabati global pada tahun 2019/2020, yaitu mencapai volume sebesar 81,54 juta metrik ton (40%). Pengaruh positif kelapa sawit memberikan pertumbuhan yang sangat besar pada sektor sosial dan ekonomi. Penentuan umur tanaman sawit berbasis data penginderaan jauh sangat penting dalam manajemen perkebunan diantaranya perhitungan dosis pupuk dan penentuan waktu untuk peremajaan. Tujuan penelitian ini mengembangkan model yang dapat menentukan umur tanaman kelapa sawit pada lahan mineral dengan metode Artificial neural network MLPClassifier (ANN-MLPC) berbasis indeks vegetasi dari citra satelit Sentinel-2. Penelitian dilakukan pada Kebun Pendidikan dan Penelitian Kelapa Sawit IPB-Cargill Jonggol. Pengambilan data dilakukan dengan mengunduh citra satelit Sentinel-2 yang diolah menjadi nilai reflektansi tingkat grid. Nilai reflektansi tingkat grid digunakan sebagai variabel independen, sedangkan informasi umur aktual digunakan sebagai variabel dependen. Pembagian proporsi dataset dengan jumlah sampel 1500 data, dimana 80% digunakan untuk data training dan 20% untuk data testing. Berdasarkan hasil evaluasi, model yang dibangun dengan algoritma ANN-MLPC diperoleh nilai akurasi sebesar 97,47%, nilai presisi sebesar 93,87%, recall sebesar 93,81%, serta F1-score sebesar 93,84%. Hasil tersebut menunjukkan model machine learning yang digunakan mampu menentukan umur tanaman sawit pada lahan mineral secara akurat dan presisi., Palm oil is the leading variety in total global vegetable oil production and distribution in 2019/2020, reaching a volume of 81.54 million metric tons (40%). The positive influence of oil palm provides enormous growth in the social and economic sectors. Determining the age of oil palm plants based on remote sensing data is very important in plantation management including calculating fertilizer doses and determining the time for replanting. The purpose of this research is to develop a model that can determine the age of oil palm plants on mineral land with the Artificial neural network MLPC Classifier (ANN-MLPC) method based on vegetation indices from Sentinel-2 satellite images. The research was conducted at the IPB-Cargill Jonggol Oil Palm Education and Research Farm. Data collection was carried out by downloading Sentinel-2 satellite images which were processed into grid-level reflectance values. The grid-level reflectance value was used as the independent variable, while the actual age information was used as the dependent variable. The proportion of datasets with a total sample size of 1500 data, where 80% and 20% of the total data samples are used traing and testing respectively. Based on the evaluation results, the model built with the MLPClassifier Artificial neural network algorithm has accuracy, precision, recall and F1-score values of of 97,47%, 93,87%, 93,81%, and 93,84% respectively. These results show that the machine learning model used is able to determine the age of oil palm plants on mineral land accurately and precisely.
Keyword: Data citra, kecerdasan buatan, satelit, usia tanaman sawit

Judul: Pemanfaatan Citra Landsat 7 untuk Estimasi Umur Tanaman Kelapa Sawit (Studi Kasus Di PTPN VIII Cisalak Baru, Banten)
Abstrak: Oil palm (Elaeis guineensis Jacq.) is a commodity increasingly in demand due to its increasing utilization. Indonesia together with Malaysia have 85% market share of crude palm oil (CPO) of the world. It is estimated the world CPO demand will continue to increase every year. This is due to the wide utilization in industry, increasing demand of population growth, and as comodity for production of biofuels as alternative energy sources instead of fossil fuels in vehicles. Oil palm growth is closely related to physical changes in canopy density and biomass. These changes can be monitored by remote sensing. The Landsat 7 used was data of 2010 year. This study tried to take advantage of Landsat 7 to estimate the age of oil palm . The study was conducted on land owned by PTPN VIII Cisalak Baru, Banten. The scope of the study was limited on the analysis of visual characteristics of the Landsat 7 gap-filled and make a model of an estimated age of the plant oil palm. The analysis showed that there is a gap-filled image that can be used for visual identification of objects in the composite images. Models are made in the form of a functional relationship between plant age and the combination of the selected channel in the form of linear regression. Variables used in the modeling of spectral lines and spectral values are extracted from each plant age. The model test results indicate that the model is good enough to predict the age of oil palm plantations in the range of 6-16 years.
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Judul: Analisis Bifurkasi Fluxon pada Persambungan Josephson Bertipe Superkonduktor-Feromagnetik-Superkonduktor (S/F/S)
Abstrak: Dynamics for the Josephson junction of type S/F/S is different from ordinary Josephon junction, where on the junction is contained the addition of tribal sin 2φ which is the result of the second harmonic supercurrent on the Josephson junction of type S/F/S that is related to Shapiro Steps symptoms. Research has been done is analyze the fluxon bifurcation to determine the stability of the fluxon in a Josephson junction of type S/F/S. The analysis was done by using the approach of analytically dynamics system and assisted by numerical solution that uses the application ODE 45 on MATLAB. The result of the critical point and the bifurcation types which is obtained to show the normalized damping (β and ϳₑ) does not affect the change of fluxon stability at the limit of less than one. Parameter β only affects the type of critical point and bifurcation that occurs, if β = 0, for ϳₑ = 0 and ϳₑ = 0.4 the bifurcation occurs is a saddle-center, and if β = 0.5 the bifurcation is a saddle-focus. Parameter ϳₑ affects the amplitude on the generated fluxon oscillation, if ϳₑ = 0 the fluxon amplitude is very small, whereas if ϳₑ = 0.4 the amplitude is larger. In each case, the fluxon stability is affected by the first harmonic supercurrents which is different sign, those are when ϳ₁ < 0 the fluxon is always stable, whereas when ϳ₁ < 0 fluxon is unstable at first but it tends towards to the point of its stability.
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