Judul: Estimation of Calcium, Magnesium, and Boron Levels in Oil Palm on Peatlands Using Sentinel-2A Satellite Imagery Analysis
Abstrak: Penelitian dalam mendukung perwujudan pertanian presisi terus dikembangankan. Penelitian ini bertujuan untuk memprediksi nutrisi kalsium, magnesium dan boron pada kelapa sawit di lahan gambut dengan menggunakan analisis citra sentinel 2A. Dataset nutrisi diperoleh dari leaf sampling unit pada perkebunan yang berbeda di lahan gambut Pulau Sumatera dan Kalimantan. Proses analisis meliputi pra-pemrosesan citra, pra-proses data, pelatihan model random forest, validasi dan evaluasi model, serta penyusunan interpretasi nutrisi. Hasil penelitian menunjukkan bahwa model mampu menghasilkan akurasi yang layak pada kalsium dan boron dengan correctness 57.13% dan 61.60%, dan akurasi yang baik pada magnesium sebesar 82.27%. Hasil tersebut diperoleh dari data yang terkonsentrasi pada rentang tingkat kandungan tertentu pada setiap nutrisi. Untuk dapat meningkatkan performa model, penelitian ini perlu dilanjutkan dengan menambahkan data sampel agas dapat mewakili setiap rentang nutrisi, dan mengintegrasikannya dengan variabel lain seperti data iklim, tanah, usia pohon, dan selainnya. Penggunaan citra satelit dengan resolusi yang lebih baik dapat menjadi pilihan yang layak dipertimbangkan., Research in supporting the realization of precision agriculture continues to be developed. This study aims to predict calcium, magnesium, and boron nutrients on peatland’s oil palm using sentinel-2A imagery analysis. The nutrient dataset was obtained from leaf sampling units in different plantations on peatlands in Sumatera and Kalimantan. The analysis model include imagery preprocessing, data pre-processing, random forest model training, validation and evaluation model, and nutrient interpretation. The study found that the model achieved a responsible level of accuracy for calcium and boron, with correctness rates of 57.13% and 61.60%, respectively. The model also performed well for magnesium, with an accuracy rate of 82.27%. However, it is important to note that these results were obtained from data that was concentrated within a certain range of nutrient levels. To improve the model's performance, future research should focus on adding sample data that represents a wider range of nutrient levels, as well as integrating other variables such as climate data, soil type, and tree age. The use of satellite imagery with higher resolution may also be worth considering.
Keyword: oil palm, precision agriculture, random forest, sentinel, machine learning

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: Grup dan Homomorfisma Grup pada Rubik Revenge
Abstrak: Rubik cube is a kind of puzzle game. A player must arrange rubik sides, which have been disordered, back to the position where every side has the same colour. This paper does not focus on the settlement solution, but instead on proving the existence of a group homomorphism between rubik movement group and S96 symmetric permutation group in rubik revenge. Firstly, it has been proved that rubik movement set is a group. Then, it also has been proved that the M set, which contains numeric labels of 1 until 96 in cubinos sides, is S96 symmetric permutation group.
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