State Failure and The Sunni-Shia Conflict in Sampang Madura

This paper attempts to explain the process of conflict reconciliation within the Sunni-Shia conflict in Sampang, Madura. The research tries to analyze the process and progress of the Sunni-Shia conflict through the discourse of state failure. It will not only figure out the discourse through political or security perspective, but also tries to view the failure and the weakness of the state from the conflict-transformation and social perspective. This research aims to look at how religious identity has been played within the process of conflict reconciliation and how the state failed to solve the Shia-Sunni conflict in Sampang. In doing so, the paper explains the history of Shia in Sampang Madura, the chronology of the conflict and its escalation, and the absence of the state within the long process of reconciliation

Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm

This study shows a new method to estimate unsampled pH value by utilizing neighboring pH, which according to recent literature, has not been done yet. In investigating this method, three algorithms are used: Neural Network-Genetic Algorithm (MLNN-GA), MLNN with backpropagation (MLNN-BP), and averaging method. MLNNGA and MLNN-BP are inputted with four pH values from distant adjacent locations on a similar basin. MLNN-GA and MLNN-BP utilize GA and backpropagation respectively to update the weight. GA optimizer is used in MLNN-GA where the result of each learning weight will be the initial weight of the next learning process. All three methods are compared based on RMSE, MSE and MAPE. MLNN-GA yielded the lowest average RMSE =0.026265, average MSE =0.000886 and average MAPE =0.003985 compared to MLNN-BP (average RMSE =0.042644, average MSE =0.002648, average MAPE =0.006862) and averaging method (average RMSE =0.136629, average MSE = 0.026128, average MAPE =0.150400). Noticeably, estimating unsampled pH value utilizing neighboring pH by using MLNNGA shows a better performance than MLNN-BP and averaging method