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: Oil Palm Productivity Estimation Based on Sentinel 2 Image Vegetation Index and Open Area Index, Rainfall, and Fertilization
Abstrak: Taksasi adalah proses perkiraan hasil tandan buah segar yang dipanen pada periode tertentu. Taksasi secara umum dilakukan secara langsung turun ke lapangan saat proses pengambilan data, namun taksasi dapat dilakukan dengan teknik remote sensing sehingga proses pengumpulan data lebih praktis dan menghemat biaya. Tujuan dari penelitian ini adalah menghitung nilai indeks vegetasi (NDVI) dan indeks keterbukaan lahan (OAI) citra Sentinel-2, menentukan persamaan regresi linear berganda untuk estimasi produktivitas kelapa sawit di PT. Socfin Indonesia, menguji persamaan dengan uji statistik yaitu uji signifikasi F (Sig. F), uji koefisien determinasi (R2 ), dan uji akurasi, dan menentukan model terbaik dalam menduga produktivitas kelapa sawit. Pendugaan produktivitas kelapa sawit dilakukan dengan menggunakan 3 parameter (indeks vegetasi/indeks keterbukaan lahan, curah hujan, dan dosis pupuk), 2 parameter (indeks vegetasi/indeks keterbukaan lahan dan curah hujan) dan 1 parameter (indeks vegetasi/indeks keterbukaan lahan) dengan masingmasing parameter dilakukan pergeseran beberapa bulan ke belakang (M-2, M-4, dan M-8 untuk 4 bulanan serta M-3, M-6, dan M-12 untuk 6 bulanan). Hasil dari persamaan yang didapat diuji lalu diberi skor masing-masing kelompoknya lalu di beri bobot yang sesuai. Hasil penelitian menunjukkan kelompok 4 bulanan SR pergeseran M-8 dengan 2 parameter (NDVI maksimum dan curah hujan lapang) memiliki hasil terbaik dalam menduga produktivitas kelapa sawit., Oil palm productivity forecasting is the process of estimating the yield of fresh fruit bunches harvested in a certain period. Oil palm productivity forecasting is generally carried out directly to the field during the data collection process but can be done using remote sensing techniques so that the data collection process is more practical and saves costs. The purpose of this study is to calculate the value of the vegetation index (NDVI) and open area index (OAI) of the Sentinel-2 image, determine the multiple linear regression equation for the estimation of oil palm productivity at PT. Socfin Indonesia, tested the equation with statistical tests, namely the F significance test (Sig. F), coefisien of determination test (R2 ), and accuracy test, and determine the best model in estimating oil palm productivity. Estimation of oil palm productivity was carried out using 3 parameters (vegetation index/open area index, rainfall, and fertilizer dose), 2 parameters (vegetation index/open area index and rainfall), and 1 parameter (vegetation index/open area index) with each parameter was shifted several months back (M-2, M-4, and M-8 for 4 months and M-3, M-6, and M-12 for 6 months). The results of the equations obtained are tested and then given a score for each group and then given the appropriate weight. The results showed that the 4-months SR shift M-8 with 2 parameters (maximum NDVI and field rainfall) had the best results in predicting oil palm productivity.
Keyword: Google Earth Engine, open area index, palm oil, remote sensing, sentinel-2, vegetation index

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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