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: Determination of Calcium and Boron Status of Palm Plants Based on Sentinel-2A Imagery and Intelligent System Algorithms
Abstrak: Kalsium (Ca) dan Boron (B) merupakan hara makro dan mikro yang berperan penting dalam mempertahankan produktivitas sawit yang optimum, sedangkan metode untuk menentukan status hara pada tanaman sawit masih menggunakan metode destruktif yang memiliki kelemahan terutama dalam ketepatan waktu, keterwakilan wilayah dan memerlukan biaya yang relatif tinggi untuk pengujian di laboratorium. Oleh karena itu, penggunaan citra satelit Sentinel-2 dan algoritma sistem cerdas merupakan salah satu solusi untuk menentukan status Ca dan B pada tanaman sawit secara cepat, tepat, hemat dan terlihat. Sumber data pada penelitian ini berupa data sampel daun sawit (LSU) dan data citra satelit Sentinel-2A level 2A. Metode pemrosesan citra Sentinel-2 meliputi resampling resolusi citra untuk menyamakan resolusi spasial setiap band menjadi 10 x 10 meter, masking area dengan poligon batas kebun, dan ekstrasi nilai citra dengan point sampling tools. Parameter citra satelit yang digunakan adalah band multispektral dan indeks vegetasi. Parameter tersebut menjadi variabel bebas dari pemodelan. Hara Kalsium dan Boron dari hasil pengujian laboratorium merupakan variabel terikat dalam pemodelan. Data dibagi menjadi dua kelompok data, yakni data latih dan data tes, masing-masing sebanyak 90% dan 10% dari populasi sampel. Hasil penelitian menunjukkan model cerdas berbasis algoritma SVR menunjukkan kinerja hasil prediksi yang lebih baik dari model berbasis ANN, yang ditunjukkan oleh nilai correctness model berbasis SVR terhadap data tes sebesar 84.28% untuk Kalsium dan 86.47% untuk Boron. Nilai correctness model berbasis SVR terhadap data latih Kalsium sebesar 84.14% dan terhadap data latih Boron sebesar 85.69%., Calcium (Ca) and Boron (B) are macro and micro nutrients for sustaining the optimum productivity of oil palm. However, current techniques for assessing the nutritional status of oil palm rely on destructive methods that have weakness, particularly with regard to timeliness, spatial variance representation, and high cost of laboratory testing. Consequently, one solution to swiftly, precisely, and affordably ascertain the Ca and B status in oil palm trees are through the utilization of Sentinel-2 images and intelligent system algorithms. Sentinel-2A level 2 images and palm leaf sample (LSU) are the data sources used in this research. The Sentinel- 2A image processing technique consists of resampling the image to bring the resolution of each band to 10 x 10 meters, masking the image with plantation boundaries, and utilizing a point sampling tool to extract the image values. The developed machine learning-based model employes the vegetation index and multispectral bands as independent variables, while Calcium and Boron data from laboratory tests are used as dependent variables. The sample data is divided into two groups, namely training and test data, with a composition of 90% and 10%. The research results demonstrate that the intelligent model based on the SVR algorithm has superior prediction performance than the ANN-based model, as evidenced by the SVR-based model's accuracy value on test data of 84.28% for Calcium and 86.47% for Boron. The SVR-based model's accuracy value for Calcium training data is 84.14%, while for Boron training data it is 85.69%.
Keyword: remote sensing, Plant nutrients, Support Vector Regression, Artificial Neural Network, Machine learning

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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