Pendidikan Akhlak Hasan Al Banna dan Said Nursi dalam pengembangan model pendidikan agama Islam

Sepanjang sejarah umat manusia masalah akhlak selalu menjadi pokok persoalan. Karena pada dasarnya, pembicaraan tentang akhlak selalu berhubungan dengan persoalan perilaku manusia dan menjadi permasalahan utama manusia terutama dalam rangka pembentukan peradaban. Banyak tokoh Islam yang telah memberikan pemikirannya dalam pendidikan akhlak. Pada akhir masa usia Khilafah Usmaniyah yang berpusat di Konstantinopel atau pada masa ini terkenal dengan nama resmi Istanbul, lahirlah dua tokoh besar yang hidup satu zaman dan aktif dalam pembinaan akhlak masyarakat yaitu Hasan al-Banna Said Nursi. Sehubungan dengan hal di atas maka penulis ingin mengetahui konsep pendidikan akhlak kedua tokoh tersebut dalam pengembangan model Pendidikan Agama Islam di sekolah. Adapun rumusan masalahnya sebagai berikut, 1. Bagaimana konsep pendidikan akhlak menurut Hasan al Banna dan Said Nursi?; 2. Bagaimana keistimewaan pemikiran Hasan al Banna dan Said Nursi tentang pendidikan akhlak? ; 3.Bagaimana pemikiran Hasan al Banna dan Said Nursi tersebut dalam pengembangan model Pendidikan Agama Islam saat ini? Penelitian ini dilakukan dengan menggunakan metode kualitatif Bentuk penelitian ini adalah berupa kajian pustaka (library research). Kajian ini berusaha mengungkapkan pendidikan akhlak menurut Hasan al-Banna dan Said Nursi melalui sumber data yang relevan dengan kebutuhan, baik buku-buku teks, jurnal, atau majalah-majalah ilmiah dan hasil-hasil penelitian. Salah satu kesimpulan dari penelitian ini adalah keistimewaan konsep pendidikan akhlak Hasan al Banna yang berorientasi pada pengembangan seluruh potensi yang ada pada diri manusia, sebab Islam sangat menaruh perhatian penciptaan manusia yang utuh, baik dari segi jasmani dan rohani. Di antara hal yang paling diutamakan oleh Hasan al Banna dari akhlak untuk ditanamkan dalam jiwa pengikutnya adalah sabar, tabah, cita-cita, dan pengorbanan. Sedangkan keistimewaan konsep pendidikan akhlak yang ditawarkan oleh Said Nursi terlihat lebih condong pada aspek kesempurnaan jiwa manusia. Sehingga jika dilihat dari aspek-aspek akhlak kepada Allah, maka hal utama yang bisa dilihat dari seseorang yang berakhlak baik menurut Said Nursi adalah dengan selalu menjaga keimanan, ibadah, bersyukur, berdzikir, dan berdoa

Advance video analysis system and its applications

This research aims at developing an Advance Video Analysis System (AVAS) which can be used in wide range of video surveillance applications as well as to detect moving objects and human beings. The AVAS is able to detect and track interested objects along with human. It recognizes activities in an application environment, such as in a room, supermarket, car, or security checkpoint. Designing a real-time video analysis system is a complex task, as many factors including processing speed, system cost, accuracy, and robustness, need to be carefully balanced. This research has focused these factors at two levels, algorithm level and software level. Background elimination algorithm is proposed in this paper to enhance the performance of Smart Camera systems in changing background and varying lighting condition environment. Among the main features of this research some are, Event Id, Video Id, and Human Id which give detail information about the events, videos and other tracked objects. Finally, the software implementation of AVAS is applied to detect motion and then to trigger alarm for the security purposes. The system will trigger alarm once the motion is detected and when it exceeds the desire threshold value it will give warning to prevent any loss or mass destruction. Finally, we have given a number of recommendations that need to be addressed for the future growth of surveillance technologies and meeting the end-users' diversified and dynamic requirements. © EuroJournals Publishing, Inc. 2010

Face recognition using illumination-invariant local patches

Illumination variation that span globally and locally across the facial surface is one of the most important aspect in designing a robust face recognition system. The illumination variations due to changes in lighting conditions could produce different shape of shading on the face thus deforming the facial features. The effect of these variations is simply more severe in the presence of single-sample constraint since there would be many variables with very limited observations. Illumination variations have been modelled in literature as a series of undetermined multiplicative and additive noise, hence it is more convenient to eliminate or reduce the effect rather than computing them. In this paper, we present an illumination-invariant method where we use local features as basis for face classification which is obtained from partitioning histogram-equalized faces into smaller overlapping local patches (LPs). We can achieve illumination-invariance for these LPs by subtracting the vectors with local average illumination and then these vectors are logarithmically normalized to enhance the local contrast. The degree of invariance is controlled by a weight connected to the average intensity component. We have tested this method in single sample face recognition setting on AR Database and Extended YALE B Database. Recognition results show that the proposed method is suitable for robust face recognition since it achieve good performance in both even illumination and uneven illumination cases

A Comparative Study of Optimization Methods for 33kV Distribution Network Feeder Reconfiguration

Distribution Network Reconfiguration (DNR) has been a part of importance strategies in order to reduce the power losses in the electrical network system. Due to the increase of demand for the electricity and high cost maintenance, feeder reconfiguration has become more popular issue to discuss. In this paper, a comparative study has been made by using several optimization methods which are Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The objectives of this study are to compare the performance in terms of Power Losses Reduction (PLR), percentage of Voltage Profile Improvement (VPI), and Convergence Time (CT) while select the best method as a suggestion for future research. The programming has been simulated in MATLAB environment and IEEE 33-bus system is used for real testing. ABC method has shown the superior results in the analysis of two objectives function. The suggestion has been concluded and it is hoped to help the power system engineer in deciding a better feeder arrangement in the future

A Comparative Study of Optimization Methods for 33kV Distribution Network Feeder Reconfiguration

Distribution Network Reconfiguration (DNR) has been a part of importance strategies in order to reduce the power losses in the electrical network system. Due to the increase of demand for the electricity and high cost maintenance, feeder reconfiguration has become more popular issue to discuss. In this paper, a comparative study has been made by using several optimization methods which are Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The objectives of this study are to compare the performance in terms of Power Losses Reduction (PLR), percentage of Voltage Profile Improvement (VPI), and Convergence Time (CT) while select the best method as a suggestion for future research. The programming has been simulated in MATLAB environment and IEEE 33-bus system is used for real testing. ABC method has shown the superior results in the analysis of two objectives function. The suggestion has been concluded and it is hoped to help the power system engineer in deciding a better feeder arrangement in the future

Encoding of facial images into illumination-invariant spike trains

Some previous work of several researchers have mathematically proven the advantage of Spiking Neural Network (SNN) in term of computational power and one of the neuron model that shows promising result is Spike response Model (SRM). Facial recognition is one of the tasks that can benefit from the advantages of SNN. Therefore in this work we try to unravel the elementary of facial recognition using SNN –the encoding of analog-valued images of the subject face into spike trains as inputs to the neural network using Leaky Integrate and Fire (LIF) model. Implementation of an adaptive LIF model is investigated and a spike adjustment method is proposed to improve the robustness of the generated spikes from a normalized image against different level of illuminations

Face Recognition From Single Sample Per Person by Learning of Generic Discriminant Vectors

AbstractThe conventional ways of recognizing faces always assume the possession and heavily relies on extensive and representative datasets, but that is not the case in most real-world situations where more often than not, a very limited or even only single sample per person (SSPP) is available which ultimately rendering most face recognition systems to fail severely. This paper proposes a development of face recognition based on a combination of traditional eigenface with artificial neural network (ANN), having the face recognition performance boosted by the classification of discriminant vectors learned from a set of generic samples. The discriminant vectors representing intra-subject and inter-subject variations are learned based on similarities of pairs of generic samples which then used to classify novel intra-subject pairs and inter-subject pairs from probe set and corresponding gallery set. After that, the resulting classification is used to recognize faces by combining it with the expressive ability of eigenface via a voting procedure. The proposed method when tested with FERET and YALE datasets suggests that in face recognition within the SSPP constraints, the performance of the proposed method is better than some state-of-the-art methods

Motion detection techniques using optical flow

The main objective of this project is to build an autonomous microcontroller-based mobile robot for a local robot soccer competition. The black competition field is equipped with white lines to serve as the guidance path for competing robots. Two prototypes of soccer robot embedded with the Basic Stamp II microcontroller have been developed. Two servo motors are used as the drive train for the first prototype whereas the second prototype uses two DC motors as its drive train. To sense the lines, lightdependent resistors (LDRs) supply the analog inputs for the microcontroller. The performances of both prototypes are evaluated. The DC motor-driven robot has produced better trajectory control over the one using servo motors and has brought the team into the final round