Judul: Aplikasi Teknologi Near Infrared untuk Pendugaan Viabilitas Benih Padi (Oryza sativa) Varietas Ciherang
Abstrak: Near infrared (NIR) spectroscopy in the range of 1000-2500 nm was studied for its ability to predict the seed viability such as germability, vigor index, maximum growth potential and the biochemistry composition such as water content, soluble protein, and free faty acid of the ciherang paddy seed following 0, 2, 4, 6 and 8 days acclerated aging (45oC, RH >90%). A total of 60 sample groups consist of 40 g paddy seed per sample ware used. Fourty samples were used for calibration and 20 samples were used for validation. Artificial neural network (ANN) and partial least squares (PLS) regression methods were used to build the prediction model. The good model should have a low standard error of calibration (SEC), a low standard error of performance (SEP), a high correlation coefficient, a small difference between SEC and SEP and have ratio performance deviation (RPD) higher than 2.5. The result showed that kind of original and pre processing spectra influenced the value of evaluation ANN and PLS model. The structure of ANN also influence the value of evaluation model. The best model to predict germability was 10-5-3 ANN using standard normal variate of reflectan spectra with RPD = 2.2359 and r validation = 0.8947, to predict vigor index was 10-5-1 ANN using 2nd derivative of reflectan spectra with RPD=3.6842 and r validation 0.9645, to predict maximum growth potential was 10-5-1 ANN using 2nd derivative of reflectan spectra with RPD=2.5572 and r validation 0.9204, to predict water content was PLS using absorbant spectra with RPD=9.6028 and r validation 0.9946, to predict soluble protein was 5-5-1 ANN using 2nd derivative of reflectan spectra with RPD=3.9615 and r validation 0.9686, and to predict free faty acid was 10-20-1 ANN model using 2nd derivative of absorbant spectra with RPD=4.0290 and r validation 0.9688.
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Judul: Estimating Rice Productivity Using Sentinel-2 Satellite Imagery with a Machine Learning Approach Based on Vegetation Index
Abstrak: Informasi ketersediaan beras di masa mendatang mendukung kebijakan pemerintah guna mewujudkan ketahanan pangan yang berkelanjutan. Salah satu metode yang digunakan untuk mendukung ketersediaan beras di masa mendatang yaitu penggunaan teknologi penginderaan jauh. Penelitian ini bertujuan untuk menduga produktivitas padi berdasarkan nilai normalized difference vegetation index (NDVI) citra satelit Sentinel-2 melalui pendekatan machine learning decision tree. Data produksi padi, varietas, tanggal tanam, tanggal panen, dan koordinat sawah didapat dengan melakukan wawancara kepada petani serta survei lapang. Koordinat sawah kemudian dikonversi menjadi luas area dan dibuat produktivitas padi per petakan sawah berdasarkan data yang sudah didapat. Nilai NDVI per petakan sawah selama satu musim tanam kemudian dihubungkan dengan produktivitas padi melalui machine learning decision tree. Model terbaik pada pendugaan gabungan varietas Inpari 32 dan MR 219 memiliki nilai MAPE 5,077% dengan akurasi 94,923%; RMSE ± 0,391 ton/ha; dan nilai R2 0,776. Model pendugaan terbaik pada varietas Inpari 32 memiliki nilai MAPE 4,16% dengan akurasi 95,84%; RMSE ± 0,262 ton/ha; dan R2 0,894. Model pendugaan terbaik pada varietas MR 219 pada varietas MR 219 memiliki nilai MAPE 1,339% dengan akurasi 98,661%; RMSE sebesar ± 0,103 ton/ha; dan R2 0,982., Information on future rice availability supports government policies for sustainable food security. One method used to support rice availability in the future is the use of remote sensing technology. This study aims to predict the productivity of rice based on normalized difference vegetation index (NDVI) of the Sentinel-2 satellite image through a machine learning decision tree approach. Rice production, plants variety, planting date, harvest date, and field coordinates were obtained by conducting interviews with farmers as well as field surveys. Paddy field coordinates then converted into area, and productivity per square area is generated based on the obtained data. The NDVI value per pitch for a single growing season is linked to the productivity of paddy through the machine learning decision tree. The best model on the combined varieties of Inpari 32 and MR 219 has a MAPE value of 5,077% with an accuracy 94,923%; RMSE ± 0,391 tons/ha; and R² 0,776. The best model on Inpari 32 varieties has a MAPE value of 4,16% with an accuracy 95,84%; RMSE ± 0,262 tons/ha; and R2 0,894. The best model on MR 219 varieties has a MAPE value of 1,339% with an accuracy 98,661%; RMSE ± 0,103 tons/ha; and R2 0,982.
Keyword: decision tree, estimation, NDVI, paddy, Sentinel-2

Judul: Formulasi Hamiltonian untuk Menggambarkan Gerak Gelombang Soliter Dimensi Tiga di Permukaan Laut
Abstrak: Gelombang permukaan adalah gelombang yang terjadi perbedaan rapat massa antara air dan udara. Salah satu gelombang permukaan yang ditinjau adalah gelombang soliter. Formulasi gelombang soliter dilakukan dengan menggunakan formulasi Hamiltonian.
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