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"[ABSTRAKPesatnya perkembangan Deep Learning akhir-akhir ini juga menyentuh ASRberbasis HMM, sehingga memunculkan teknik hibrid HMM-ANN. Salah satuteknik Deep Learning yang cukup menjanjikan adalah penggunaan arsitekturCNN. CNN yang memiliki kemampuan mendeteksi local correlation sesuaiuntuk digunakan pada data spectrum suara. Spectrogram memiliki karakteristiklocal correlation yang nampak secara visual. Penelitian ini adalah eksperimenpenggunaan spectrogram sebagai fitur untuk HMM-CNN untuk meningkatkankinerja ASR berbasis HMM. Penelitian menyimpulkan spectogram dapatdigunakan sebagai fitur untuk HMM-CNN untuk meningkatkan kinerja ASRberbasis HMM.

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ABSTRACTThe latest surge in Deep Learning affecting HMM based ASR, which give birth tohybrid HMM-ANN technique. One of the promising Deep Learning technique isthe implementation of CNN architecture. The ability of CNN to detect localcorrelation make it suitable to be used for speech spectral data. Spectrogram as aspeech spectral data has local correlation characteristic which is visuallyobservable. This research is an experiment to use spectrogram as a feature forHMM-CNN to add to the performance of HMM based ASR. This research foundthat spectrogram is indeed can be used as a feature for CNN to add to theperformance of HMM based ASR., The latest surge in Deep Learning affecting HMM based ASR, which give birth tohybrid HMM-ANN technique. One of the promising Deep Learning technique isthe implementation of CNN architecture. The ability of CNN to detect localcorrelation make it suitable to be used for speech spectral data. Spectrogram as aspeech spectral data has local correlation characteristic which is visuallyobservable. This research is an experiment to use spectrogram as a feature forHMM-CNN to add to the performance of HMM based ASR. This research foundthat spectrogram is indeed can be used as a feature for CNN to add to theperformance of HMM based ASR.]"

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Berbagai metode pengembangan rekognisi citra wajah telah banyak dilakukan, berbagai metode seperti Deep Learning, Multilayer Perceptron sudah dilakukan. Metode Convolutional Neural Network juga sudah banyak dikembangkan untuk melakukan klasifikasi citra seperti rekognisi jenis bunga, hewan, hingga pendeteksian kecacatan sel. Convolutional Neural Network diharapkan mampu melakukan rekognisi citra wajah secara tiga dimensi. Operasi konvolusi sebagai bagian ekstraksi fitur pada Convolutional Neural Network, diharapkan dapat membantu bagian klasifikasi untuk melakukan tugasnya dengan lebih baik. Rekognisi citra wajah secara tiga dimensi ini sangat dibutuhkan, karena ketika kita ingin mendeteksi seseorang tanpa diketahui orang tersebut, maka dengan berbagai macam sudut hadap wajahnya sistem harus dapat mengidentifikasi orang tersebut. Untuk penelitian kali ini saya akan menggunakan dataset gambar wajah tiga dimensi yang akan digunakan sebagai klasifikasi parameter biometrik seseorang. Pada penelitian ini akan menganalisa tiap-tiap lapisan pada Convolutional Neural Network, serta melakukan perbandingan dengan Backpropagation Neural Network. Dan juga akan melakukan analisa dengan menggunakan citra wajah berderau.

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Various methods of developing facial image recognition have been carried out, various methods such as Deep Learning and Radial Basis Function Neural Network have been carried out. Convolutional Neural Network methods have also been developed to carry out image classifications such as recognition of types of flowers, animals, and detection of cell defects. Convolutional Neural Network is expected to be able to recognize facial images in three dimensions. Convolution operations as a feature extraction part of the Convolutional Neural Network are expected to help the classification section to do their job better. Three-dimensional face image recognition is needed, because when we want to detect someone without knowing by the person, then with a variety of face angles, the system must be able to identify that person. For this research I will use a three-dimensional face image dataset that will be used as a classification of a persons biometric parameters. In this study, we will analyze each layer in the Convolutional Neural Network, do a comparison with Backpropagation Neural Network. And also will do the analysis by using a noisy face image.

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"Skripsi ini membahas mengenai rancangan untuk pengembangan sistem penilaian esai otomatis (SIMPLE-O) menggunakan Convolutional Neural Network dan Manhattan Distance sebagai penilaian pada ujian esai Bahasa Jepang yang sedang dikembangkan oleh Departemen Teknik Elektro Universitas Indonesia. Sistem ini menggunakan Convolutional Neural Network (CNN) untuk memberikan nilai pada esai Bahasa Jepang. Dari beberapa variasi yang diuji, model yang paling stabil adalam model yang memiliki layer CNN, Manhattan Distance, dan dropout dengan dropout rate sebesar 0.1, di-train selama 32 epochs dengan loss function cross-categorical entropy dan optimizer RMSprop dengan input model ditokenisasi per karakter dengan rata-rata akurasi sebesar 59.48%.......This thesis discusses the design for the development of an automatic essay scoring system (SIMPLE-O) using the Convolutional Neural Network and Manhattan Distance as an assessment of the Japanese essay exam which is being developed by the Department of Electrical Engineering, University of Indonesia. This system uses Convolutional Neural Network (CNN) to score Japanese essays. Of the several variations tested, the most stable model is a model that has CNN, Manhattan Distance, and dropout layers with a dropout rate of 0.1, trained for 32 epochs with a loss function cross-categorical entropy and an RMSprop optimizer with model input tokenized per character on average. the average accuracy is 59.48%."

"ABSTRACTThis research trained Deep Convolutional Networks(ConvNets) to classify hand-written Pallava alphabet. The Deep ConvNets architecture consists of two convolutional layers, each followed by maxpooling layer, two Fully-Connected layers. It had 442.602 parameters. This model classified 660 images of hand-written Pallava alphabet into 33 diferent classes. To make training faster, this research used GPU implementation with 384 CUDA cores. Two different techniques were implemented, Stochastic Gradient Descent (SGD) and Adaptive Gradient, each trained with 10, 20, 30 and 40 epoch. The best accuracy was 67,5%, achieved by the model with SGD technique trained at 30 epoch."