TRANSPARANSI PELAYANAN SANTUNAN DI KANTOR JASA RAHARJA SIDOARJO

ABSTRAKSI VAILA SHUFAH NINDIYATI NINGRUM, TRANSPARANSI PELAYANAN SANTUNAN DI KANTOR JASA RAHARJA SIDOARJO. Berdasarkan fenomena bahwa penerima dana santunan dari kantor Jasa Raharja Sidoarjo sebanyak 602 orang sangat kecil jumlahnya (41%), jika dibanding dengan pemohon pengajuan asuransi sebanyak 1.458 pemohon. Seharusnya semua masayarakat yang berhak dapat memperoleh santunan. Di sisi lain PT. Jasa Raharja merupakan salah satu BUMN yang menjadi ikon pelayanan publik di Jawa Timur yang dalam melaksanakan pelayanan yang berpedoman pada Keputusan Men.PAN No.63/KEP/M.PAN/7/2003 tentang Penyelenggaraan Pelayanan Publik. Khususnya transparansi dan akuntabilitas berpedoman pada Keputusan Menteri Pendayaan Aparatur Negara Nomor : KEP/26/M.PAN/2/2004. Tujuan Penelitian ini adalah : (1)Untuk Mengetahui Kategori Tingkat Transparansi Pelayanan Santunan di Kantor Jasa Raharja Sidoarjo.(2)Untuk Menguji Perbedaan Antara Frekuensi Tingkat Transparansi Pelayanan Santunan Yang Diamati (fo) Dengan Frekuensi Tingkat Transparansi Yang Diharapkan (fh). Penelitian ini merupakan penelitian kuantitatif dengan variabel mandiri dengan 10 indikator sesuai dengan Keputusan Menteri Pendayaan Aparatur Negara Nomor : KEP/26/M.PAN/2/2004 yaitu manajemen penyelenggaran pelayanan publik, prosedur pelayanan, persyaratan teknis dan persyratan administratif pelayanan, rincian biaya pelayanan, wkatu penyelesaian pelayanan, pejabat yang berwewenang dan bertanggung jawab, lokasi pelayanan, janji pelayanan, standart pelayanan dan informasi pelayanan.. Metode yang digunakan adalah deskritif kuantitatif dengan teknik pengumpulan data kuesioner dari 240 responden sebagai sampel yang mewakili populasi dalam penelitian ini adalah penerima dana santunan sejumlah 602 orang dari bulan Oktober 2010 sampai dengan bulan September 2011. Dari hasil penelitian dan pembahasan menghasilkan kesimpulan : 1) Transparansi Pelayanan Santunan di Kantor Jasa Raharja Sidoarjo berada dalam kategori transparan dengan sebesar 43% jawaban responden, tetapi 38% menyatakan cukup transparan. 2) Indikator Manajemen dan Penyelenggaraan Pelayanan Publik merupakan Indikator yang mendapatkan penilaian paling tidak transparan (19%). 3) Indikator Standar Layanan Publik medapatan penilaian merupakan indikator yang mendapatkan penilaian sangat transparansi (20%). Berdasarkan analisis data dengan Perhitungan harga Chi Kuadrat hitung = 184,5832. dengan derajat keabsahan (dk) = k – 1 = 5 – 1 = 4 taraf kesalahan 5% , harga Chi Kuadrat tabel sebesar 9,488. Ternyata harga Chi Kuadrat hitung 184,5832 lebih besar dari pada harga Chi Kuadrat tabel 9,488. Maka uji statistik menyatakan hipotesis nol ditolak dan hipotesis alternatif diterima. Maka dapat dinyatakan bahwa Terdapat Perbedaan Antara Frekuensi Tingkat Transparansi Pelayanan Santunan Yang Diamati (fo) Dengan Frekuensi Tingkat Transparansi Yang Diharapkan (fh

A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for the EMNIST Classification

End user AI is trained on large server farms with data collected from the users. With ever increasing demand for IoT devices, there is a need for deep learning approaches that can be implemented at the Edge in an energy efficient manner. In this work we approach this using spiking neural networks. The unsupervised learning technique of spike timing dependent plasticity (STDP) and binary activations are used to extract features from spiking input data. Gradient descent (backpropagation) is used only on the output layer to perform training for classification. The accuracies obtained for the balanced EMNIST data set compare favorably with other approaches. The effect of the stochastic gradient descent (SGD) approximations on learning capabilities of our network are also explored

Deep Spiking Neural Networks: Study on the MNIST and N-MNIST Data Sets

Deep learning, i.e., the use of deep convolutional neural networks (DCNN), is a powerful tool for pattern recognition (image classification) and natural language (speech) processing. Deep convolutional networks use multiple convoltuion layers to learn the input data. They have been used to classify the large dataset Imagenet with an accuracy of 96.6%. In this work deep spiking networks are considered. This is new paradigm for implementing artificial neural networks using mechanisms that incorporate spike-timing dependent plasticity which is a learning algorithm discovered by neuroscientists. Advances in deep learning has opened up multitude of new avenues that once were limited to science fiction. The promise of spiking networks is that they are less computationally intensive and much more energy efficient as the spiking algorithms can be implemented on a neuromorphic chip such as Intel’s LOIHI chip (operates at low power because it runs asynchronously using spikes). Our work is based on the work of Masquelier and Thorpe, and Kheradpisheh et al. In particular a study is done of how such networks classify MNIST image data and N-MNIST spiking data. The networks used in consist of multiple convolution/pooling layers of spiking neurons trained using spike timing dependent plasticity (STDP) and a final classification layer done using a support vector machine (SVM)

Learning Behavior of Memristor-Based Neuromorphic Circuits in the Presence of Radiation

In this paper, a feed-forward spiking neural network with memristive synapses is designed to learn a spatio-temporal pattern representing the 25-pixel character ‘B’ by separating correlated and uncorrelated afferents. The network uses spike-timing-dependent plasticity (STDP) learning behavior, which is implemented using biphasic neuron spikes. A TiO2 memristor non-linear drift model is used to simulate synaptic behavior in the neuromorphic circuit. The network uses a many-to-one topology with 25 pre-synaptic neurons (afferent) each connected to a memristive synapse and one post-synaptic neuron. The memristor model is modified to include the experimentally observed effect of state-altering radiation. During the learning process, irradiation of the memristors alters their conductance state, and the effect on circuit learning behavior is determined. Radiation is observed to generally increase the synaptic weight of the memristive devices, making the network connections more conductive and less stable. However, the network appears to relearn the pattern when radiation ceases but does take longer to resolve the correlation and pattern. Network recovery time is proportional to flux, intensity, and duration of the radiation. Further, at lower but continuous radiation exposure, (flux 1x1010 cm−2 s−1 and below), the circuit resolves the pattern successfully for up to 100 s

Deep Convolutional Spiking Neural Networks for Image Classification

Spiking neural networks are biologically plausible counterparts of artificial neural networks. Artificial neural networks are usually trained with stochastic gradient descent (SGD) and spiking neural networks are trained with bioinspired spike timing dependent plasticity (STDP). Spiking networks could potentially help in reducing power usage owing to their binary activations. In this work, we use unsupervised STDP in the feature extraction layers of a neural network with instantaneous neurons to extract meaningful features. The extracted binary feature vectors are then classified using classification layers containing neurons with binary activations. Gradient descent (backpropagation) is used only on the output layer to perform training for classification. Surrogate gradients are proposed to perform backpropagation with binary gradients. The accuracies obtained for MNIST and the balanced EMNIST data set compare favorably with other approaches. The effect of the stochastic gradient descent (SGD) approximations on learning capabilities of our network are also explored. We also studied catastrophic forgetting and its effect on spiking neural networks (SNNs). For the experiments regarding catastrophic forgetting, in the classification sections of the network we use a modified synaptic intelligence that we refer to as cost per synapse metric as a regularizer to immunize the network against catastrophic forgetting in a Single-Incremental-Task scenario (SIT). In catastrophic forgetting experiments, we use MNIST and EMNIST handwritten digits datasets that were divided into five and ten incremental subtasks respectively. We also examine behavior of the spiking neural network and empirically study the effect of various hyperparameters on its learning capabilities using the software tool SPYKEFLOW that we developed. We employ MNIST, EMNIST and NMNIST data sets to produce our results

Surveying the thoughts of Japanese people on its LGBT people

Japan is known for being one of the most urbanized countries in the world, with bustling urbancities, with high rates of education and economic development, as stated in reports showcased by international organizations such as the OECD. Despite these indicators for high development, Japan has been reported to be lacking in the legal rights and treatment of sexual and gender minorities, this may be suprising for some as urban environments are often thought of as safe spaces for these groups of people. In this study the attitudes of Japanese people towards LGBT people are explored through the use of a survey, which inquires Japanese people about their social circles, and how these circles and the respondents themselves think that LGBT people are viewed and treated and if there are any specific differences in Japan's urban and rural areas. The findings of this study seem to agree with previous studies and reports published by organizations and individual scholars, the consensus suggests, that while Japanese people seem to be aware of some of the inequalities LGBT people face such as discrimination and in marriage and legal protection, the overall attitude towards LGBT issues is one of apathy

Surveying the thoughts of Japanese people on its LGBT people

Japan is known for being one of the most urbanized countries in the world, with bustling urbancities, with high rates of education and economic development, as stated in reports showcased by international organizations such as the OECD. Despite these indicators for high development, Japan has been reported to be lacking in the legal rights and treatment of sexual and gender minorities, this may be suprising for some as urban environments are often thought of as safe spaces for these groups of people. In this study the attitudes of Japanese people towards LGBT people are explored through the use of a survey, which inquires Japanese people about their social circles, and how these circles and the respondents themselves think that LGBT people are viewed and treated and if there are any specific differences in Japan's urban and rural areas. The findings of this study seem to agree with previous studies and reports published by organizations and individual scholars, the consensus suggests, that while Japanese people seem to be aware of some of the inequalities LGBT people face such as discrimination and in marriage and legal protection, the overall attitude towards LGBT issues is one of apathy