Kinetika Pelepasan Nitrogen Dari Pupuk Urea Lepas Lambat ( Urea Slow Release, Sru) Matriks Zeolit Teraktivasi

Pupuk urea lepas lambat (SRU) dibuat dengan mendispersikan urea di matriks zeolit. Proses granulasi dilangsungkan menggunakan granulator piringan miring dengan perekat larutan pati. Penelitian pelepasan nitrogen dilakukan untuk mengatahui karakteristik lepas lambat. Penelitian ini bermaksud mengetahui model kinetika pelepasan nitrogen dari pupuk lepas lambat, serta pengaruh temperatur dan pH terhadap laju pelepasan nutrien dari pupuk SRF.Telah dikaji kinetika release menggunakan pendekatan model orde nol, model Higuchi, dan Korsmeyer-peppas. Analisis kecocokan didasarkan pada nilai koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa model kinetika yang memiliki nilai R2 tertinggi adalah model kinetika orde Kosmeyer-Peppas

Adaptive Neural Fuzzy Inference System and Automatic Clustering for Earthquake Prediction in Indonesia

Earthquake is a type of natural disaster. The Indonesian archipelago located in the world's three mega plates; they are Australian plate, Eurasian plate, and Pacific plate. Therefore, it is possible for applied of earthquake risk of mitigation. One of them is to provide information about earthquake occurrences. This information used for spatiotemporal analysis of earthquakes. This paper presented Spatial Analysis of Magnitude Distribution for Earthquake Prediction using adaptive neural fuzzy inference system (ANFIS) based on automatic clustering in Indonesia. This system has three main sections: (1) Data preprocessing, (2) Automatic Clustering, (3) Adaptive Neural Fuzzy Inference System. For experimental study, earthquake data obtained Indonesian Agency for Meteorological, Climatological, and Geophysics (BMKG) and the United States Geological Survey’s (USGS), the year 2010-2017 in the location of Indonesia. Automatic clustering process produces The optimal number of cluster, that is 7 clusters. Each cluster will be analyzed based on earthquake distribution. Its calculate the b value of earthquake to get the seven seismicity indicators. Then, implementation for ANFIS uses 100 training epochs, Number of membership function (MFs) is 2, MFs type input is gaussian membership function (gaussmf). The ANFIS result showed that the system can predict the non-occurrence of aftershocks with the average performance of 70%

Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia

A model of artificial neural networks (ANNs) is presented in this paper to predict aftershock during the next five days after an earthquake occurrence in selected cluster of Indonesia with magnitude equal or larger than given threshold. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey’s (USGS). Six clusters was an optimal number of cluster base-on cluster analysis implementing Valley Tracing and Hill Climbing algorithm, while Hierarchical K-means was applied for datasets clustering. A quality evaluation was then conducted to measure the proposed model performance for two different thresholds. The experimental result shows that the model gave better performance for predicting an aftershock occurrence that equal or larger than 6 Richter’s scale magnitude