Hasanuddin University

About: Hasanuddin University is a education organization based out in Makassar, Indonesia. It is known for research contribution in the topics: Population & Medicine. The organization has 5839 authors who have published 5267 publications receiving 25893 citations. The organization is also known as: Universitas Hasanuddin.

Technical data analysis for movement prediction of Euro to USD using Genetic Algorithm-Neural Network

Abstract: In the foreign currency exchange (FOREX), a technical data analysis system for predicting currency movements is needed to help traders in decision making. Thus, this study proposes a system of technical data analysis to movement prediction of Euro to USD using Genetic Algorithm-Neural Network (GANN). To generate a predicted value, Genetic Algorithm searching for the best value of Feed Forward Neural Network (FFNN) trained with the Neural Network method that produced a net to predict. The Validation of predicted results with GANN method based on the degree of accuracy as follows. RMSE values of open is 0.00043; The RMSE values of high is 0.00068; The RMSE value of low is 0.00075; and RMSE values of close is 0.00070.

Urgency of Political Decentralization and Regional Autonomy in Indonesia: Local Perspectives

Abstract: Indonesia is a country that is inhabited by hundreds of ethnic groups with different geographical condition, natural resources and social culture. Regional decentralization and autonomy that is carried out in Indonesia is an acknowledgment of ethnic diversity (pluralism) to create unity and harmony (BhinekaTunggal Ika), to form social harmony and equal development with the framework of protecting national stability. This article argues on the urgency of political decentralization and regional autonomy in Indonesia through local perspectives by using political decentralization/local democracy model approach with the centrifugal style of authority and government decentralization and structural efficiency model approach with centripetal style.

Genetic Parameters and Multivariate Analysis to Determine Secondary Traits in Selecting Wheat Mutant Adaptive on Tropical Lowlands

Abstract: One of approaches to maintain the yield stability of the lowland tropical wheat is the use of secondary traits in the selection process. The identification of these characters requires a statistical approach in the form of genetic parameter analysis and multivariate analysis. The objective of this study was to determine the secondary traits of adaptive wheat mutants in the lowlands through the use of genetic parameters and multivariate analysis on the parameters. The study consisted of three field trials conducted in three different regencies, namely Jeneponto (135 m above sea level (asl)), Maros (100 m asl) and Bantaeng (125 m asl). The study used a nested design, where replications were nested in the environments. The genotype factors consisted of 20 genotypes repeated three times. 11 characters were observed including vegetative and reproductive characters. The analysis used consisted of repeatability, correlation, cluster analysis, principal component analysis, factor analysis, and cross print analysis. The overall results of the analysis indicate that the number of productive tillers is the main secondary trait for the selection of adaptive wheat in the lowlands. The character can be recommended for selection criteria in testing wheat lines in the lowlands to make an effective selection.