Judul: Aplikasi Bagan Warna Daun untuk Optimasi Pemupukan Tanaman Padi Menggunakan k-Nearest Neighbor
Abstrak: The leaf color of rice is closely associated with the adequacy of nitrogen (N) level of the ground. N deficiency symptoms that are most obvious and commonly seen is the reduction of green color of the leaf. This research developed a mobile application to identify the color of rice leaf and to determine the appropriate fertilizer. The application was built by using the histogram feature of several color components namely Red, Green, Blue in the RGB color space, Hue, Saturation Value in the HSV color space, and grayscale. K-Nearest Neighbor was choosen as the classification method. This research showed the highest average accuracy of 90.63% on the G (green), V (Value) and grayscale color component. The k-NN classification method produced the highest average accuracy of 72.62% when the value of k is equal to 3.
Keyword: Android, grayscale, histogram, HSV, color identification, k-Nearest Neighbor

Judul: Comparative of Classification Algorithms for Detecting Nitrogen Needs of Rice Plants Based on Multi-spectral drone Image Data
Abstrak: Optimalisasi penggunaan pupuk Nitrogen (N) sangat penting untuk meningkatkan produktivitas tanaman padi. Untuk mengetahui jumlah pupuk yang diperlukan oleh tanaman padi, petani umumnya menggunakan Bagan Warna Daun (BWD) dengan cara mencocokkan warna daun padi dengan warna pada BWD secara manual. Namun, hal ini sangat memakan waktu. Salah satu strategi untuk meningkatkan efisiensi penentuan kebutuhan pupuk N adalah dengan menggunakan Multi-spectral Drone. Drone digunakan untuk mengambil citra multispectral, kemudian citra ini digunakan untuk menentukan kebutuhan pupuk N. Penelitian ini membandingkan beberapa algoritma klasifikasi untuk memodelkan kebutuhan pupuk N dari data citra multispectral, dengan menggunakan ground truth dari penskalaan BWD. Algoritma klasifikasi yang dibandingkan yaitu Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), dan K-Nearest Neighbour (KNN). Kinerja kelima algoritma klasifikasi diukur dengan metrik evaluasi accuracy, recall, precision dan F1 score. Ditemukan bahwa model klasifikasi terbaik adalah algoritma Decision Tree (DT) dengan akurasi 98,58%, recall 96,71%, precision 96,06%, dan F1 score 96,18%.
Keyword: algoritma klasifikasi, bagan warna daun, Multi-spectral Drone, padi

Judul: Katalis Eterifikasi Gliserol Berbasis Poli–(asam stirena sulfonat) dari Limbah Styrofoam.
Abstrak: Styrofoam waste can be converted to poly–(sulfonate styrene acid) (PSSA) that can be used as catalyst. The objective of this research is to get PSSA that can catalyze the etherification of glycerol and benzyl alcohol. The styrofoam waste was converted to PSSA by reacting with H2SO4 98% (as sulfonation agent) and P2O5 (as sulfonation catalyst) at 40 °C for 6 hours. Characterizations of PSSA included functional groups, sulfonation levels, water swelling, and thermal stability. The yield of PSSA was 40-55%. The success of sulfonated polystyrene was shown by –SO3H group absorption at 1157.29 cm-1. Etherification of glycerol and benzyl alcohol using PSSA catalyst was conducted at 110 °C for 12 hours. The conversion in producing mono–benzyl ether glycerol was 8.10%, 1,3–dibenzyletherglycerol 2.60%, and dibenzyl ether 9.80%. Therefore, the PSSA can be used as etherification catalyst.
Keyword: benzyl alcohol, etherification, glycerol, polystyrene, PSSA