Judul: Development of Potassium Detection System on Peat Soil Oli Palm Plantation Using Sentinel-1 Satellite Imagery
Abstrak: Palm oil play an important role in the Indonesian economy because it is the largest Indonesian export commodities. Sufficient soil nutrient will result in maximum production and better oil yield quality. One of the nutrients that is very influential on productivity is potassium. Soil samples and location coordinates taken from peat soil oil palm plantations were then adjusted to the Sentinel-1 satellite image. Sentinel-1 image obtained processed through preprocessing steps. Sentinel-1's image preprocessing steps are apply file orbit, thermal noise removal, border noise removal, calibration, speckle filtering, terrain correction, and convert to dB. The satellite image parameters used are, sigma naught, gamma naught, beta naught, local angle of incidence, projected local incidence angle, elevation, and incidence angle from ellipsoid. These parameters used as model’s independent variables. The actual soil nutrition from the lab results used as model’s dependent variable. The data was divided into two groups of training data with 90% data and test data with 10% data. Prediction model is made using Neural Network Regressor (NN). All models that were built were assessed for the quality of the model using MAPE. NN modeling is built using hyperparameter tuning so that the model quality is better. The NN model has a MAPE of 25,85%, meaning that the correctness is 74,15%.
Keyword: Neural network regressor, Palm oil, Sentinel-1, Soil potassium level, Synthetic aperature radar

Judul: Phosphorus Levels Estimation System of Peat Soil for Oil Palm Based on Image of Synthetic Apperture Radar (SAR) Sentinel-1
Abstrak: Industri kelapa sawit di Indonesia sangat potensial. Salah satu kendala industri kelapa sawit yaitu tingkat produktivitas yang masih rendah disebabkan oleh pemupukan yang kurang optimal. Penyediaan unsur hara yang proporsional diperlukan untuk meningkatkan produktivitas kelapa sawit. Salah satu unsur hara yang dibutuhkan oleh tanaman adalah fosfor. Penelitian ini menggunakan metode analisis citra satelit berbasis radar untuk menduga kadar nutrisi fosfor tanah. Pengambilan sampel tanah dilakukan dengan menyertakan titik koordinat pada setiap sampel di masing-masing wilayah agar dapat dideteksi menggunakan sistem radar pada satelit. Proses pengambilan citra disesuaikan dengan waktu pengambilan sampel. Citra yang telah sesuai dan diunduh kemudian dilakukan praproses untuk mengurangi derau. Nilai ekstraksi gelombang yang didapat kemudian dijadikan sebagai variabel independen yang berjumlah sebanyak sepuluh variabel serta hasil analisis nutrisi fosfor dari laboratorium ditetapkan sebagai variabel dependen. Data yang didapat kemudian dilakukan praproses pengolahan data yang terdiri dari seleksi pencilan, standarisasi data dan data balancing. Data hasil praproses dibagi menjadi data latih sebanyak 90% dan data uji sebanyak 10%. Model algoritma yang digunakan untuk analisis regresi adalah Random Forest Regressor. Evaluasi model dilakukan dengan metode Mean Absolute Percentage Error (MAPE). Hasil MAPE terbaik yang dihasilkan model adalah 27.94% setelah dilakukan transformasi data dan penyesuaian hyperparameter pada model., The palm oil industry in Indonesia has a huge potential. The low rate of productivity brought on by inadequate fertilization is one of the issues affecting the palm oil industry. Increased oil palm productivity requires the provision of proportionate nutrients. Phosphorus is one of the nutrients that plants require. In this study, the amount of phosphorus in the soil is estimated using a radar-based satellite image processing technique. In order for the radar system on the satellite to detect each sample, the coordinates of each sample in each area are included in the soil sampling process. The image retrieval procedure is adjusted according to the sample period. The image that has been matched and downloaded is then preprocessed to reduce noise. The acquired wave extraction value was then applied as independent variables which contained ten variables, and the results of the laboratory analysis of phosphorus nutrition were determined as the dependent variable. The collected data is subsequently preprocessed for data processing, which involves outlier selection, data standardization, and data balancing. Data that has been preprocessed is split into 90% training data and 10% test data. After data transformation and hyperparameter adjustments, the best MAPE result the model produced was 27.94%.
Keyword: palm oil, phosphorus, peat soil, sentinel-1, random forest regressor

Judul: Konstruksi Hukum Grup Kurva Eliptik Atas F3m
Abstrak: Elliptic curves upon F3m can be divided in two cases, i.e. supersingular and nonsupersingular case. Based on group laws on elliptic curves, a new arithmetic upon F3m can be formulated as an addition process of two random points on the curve. This addition process can then be applied to the ElGamal algorithm.
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