PENGENALAN CITRA EKSPRESI WAJAH MENGGUNAKAN ALGORITMA PRINCIPAL COMPONENT ANALYSIS (PCA) DAN EXTREME LEARNING MACHINE (ELM)

Dewasa ini pengembangan ilmu teknologi semakin berkembang pesat,terutama dalam aspek keamanan, ilmu teknologi digunakan untuk pencarian identitas individu pada database kepolisian yang melibatkan data berupa citra digital ekspresi wajah manusia. Ekspresi wajah dapat dibedakan menjadi 7, yaitu senang, sedih, marah, jijik, takut, terkejut dan netral. Untuk mengenali citra ekspresi wajah pada penelitian ini digunakan algoritma Principle Component Analyst (PCA) dan Extreme Learning Machine (ELM). Algoritma PCA digunakan untuk mengekstraksi fitur, sedangkan ELM untuk melakukan pengenalan ekspresi. Dataset yang digunakan diambil dari JAFFE Dataset, berjumlah 210 citra ekspresi wajah, yang terdiri dari 10 orang masing-masing dengan 7 citra ekspresi berbeda, pengambilan foto dilakukan setiap 3 kali. Ratio datatrain : datates yang digunakan adalah 4:1. Penelitian dilakukan dengan menggunakan jumlah fitur yang berbeda mulai dari 50, 60, 70, 80 dan 90 serta 100. Pengenalan ekspresi menggunakan fungsi sigmoid dengan epoch hingga 100 kali tiap fitur, serta menggunakan hidden neuron sebanyak 65. Hasil terbaik menggunakan 90 fitur dengan epoch 65 kali diperoleh testing akurasi sebesar 0.715 (71.5%) dan training akurasi sebesar 0.931 (93.1%) dengan training time selama 0.062 detik dan testing time selama 0.015 detik. Kata kunci : ekspresi wajah, PCA, EL

KLASIFIKASI BEAT ARITMIA PADA SINYAL EKG MENGGUNAKAN FUZZY WAVELET LEARNING VECTOR QUANTIZATION

Pengenalan pola beat dalam analisa rekaman elektrokardiogram (EKG) menjadi bagian yang penting dalam deteksi penyakit jantung terutama aritmia. Banyak metode yang dikembangkan terkait dengan pengenalan pola beat, namun sebagian besar masih mengunakan algoritma klasifikasi klasik di mana masih belum mampu mengenali outlier klasifikasi. Fuzzy Learning Vector Quantization (FLVQ) merupakan salah satu algoritma yang mampu untuk mengenali outlier klasifikasi tetapi juga memiliki kelemahan untuk sistem uji yang bukan data berkelompok. Dalam tulisan ini peneliti mengusulkan Fuzzy Wavelet LearningVector Quantization (FWLVQ), yaitu modifikasi FLVQ sehingga mampu mengatasi data crisp maupun data fuzzy dan juga memodifikasi inferensi sistemnya sebagai perpaduan model fuzzy Takagi Sugeno Kang dengan wavelet. Sinyal EKG diperoleh dari database MIT-BIH. Sistem pengenalan pola beat secara keseluruhan terbagi atas dua bagian yaitu data pra proses dan klasifikasi. Hasil percobaan diperoleh bahwa FWLVQ memiliki akurasi sebesar 90.20% untuk data yang tidak mengandung outlier klasifikasi dan 87.19% untuk data yang melibatkan outlier klasifikasi dengan rasio data uji outlier klasifikasi dengan data non-outlier sebesar 1:1. The recognition of beat pattern in analysis of recording an electrocardiogram (ECG) becomes an important detection of heart disease, especially arrhythmias. Many methods are developed related to the recognition of beat patterns, but most still use the classical classification algorithms which are still not able to identify outlier classification. Fuzzy Learning Vector Quantization (FLVQ) is one of the algorithms that can identify outlier classification but also has a weakness for test systems that are not grouped data. In this paper we propose a Fuzzy Wavelet Quantization Learning Vector (FWLVQ), which is modified so as to overcome FLVQ crisp data and fuzzy data and also modify the inference system as a combination of Takagi Sugeno Kang fuzzy model with the wavelet. ECG signal obtained from the MIT-BIH database. Beat pattern recognition system as a whole is divided into two parts: data pre-processing and classification. The experimental results obtained that FWLVQ has an accuracy 90.20% for data that does not contain outlier classification and 87.19% for the classification of data involving outlier ratio outlier test data classification with non-outlier data of 1:1

Studi Steganografi Pada Citra Digital Menggunakan Shuffled Singular Value Decomposition (SSVD)

Stegangraphy is a technique for embed secret message in original image. It has an important role in the field of information hiding for secret communication. Many research about steganography tecniques have been developed, one of them is singular value decomposition (SVD). SVD method is popular discused in many tecnique such us steganography and watermarking. In addition to SVD there is a method which can give better result than SVD on watermaring technique that is Shuffled SVD. The differences between SSVD and SVD is in shuffle process which applied before applying SVD. The popularity of SSVD in the watermarking technique made the writer intererest to propose an image steganography tecnique using shuffled singular value decomposition (SSVD). The data used are two original RGB imaage and a message RGB image. Quality measured by PSNR and Correlation Coefficient. The experimental result show that the shuffling process on the secret message caused embedded message can’t  read easyly so the secret message is more secure

STUDY COMPARISON OF SVM-, K-NN- AND BACKPROPAGATION-BASED CLASSIFIER FOR IMAGE RETRIEVAL

Classification is a method for compiling data systematically according to the rules that have been set previously. In recent years classification method has been proven to help many people’s work, such as image classification, medical biology, traffic light, text classification etc. There are many methods to solve classification problem. This variation method makes the researchers find it difficult to determine which method is best for a problem. This framework is aimed to compare the ability of classification methods, such as Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Backpropagation, especially in study cases of image retrieval with five category of image dataset. The result shows that K-NN has the best average result in accuracy with 82%. It is also the fastest in average computation time with 17,99 second during retrieve session for all categories class. The Backpropagation, however, is the slowest among three of them. In average it needed 883 second for training session and 41,7 second for retrieve session

STUDY COMPARISON BACKPROPOGATION, SUPPORT VECTOR MACHINE, AND EXTREME LEARNING MACHINE FOR BIOINFORMATICS DATA

A successful understanding on how to make computers learn would open up many new uses of computers and new levels of competence and customization. A detailed understanding on information- processing algorithms for machine learning might lead to a better understanding of human learning abilities and disabilities. There are many type of machine learning that we know, which includes Backpropagation (BP), Extreme Learning Machine (ELM), and Support Vector Machine (SVM). This research uses five data that have several characteristics. The result of this research is all the three investigated models offer comparable classification accuracies. This research has three type conclusions, the best performance in accuracy is BP, the best performance in stability is SVM and the best performance in CPU time is ELM for bioinformatics data

EARLY DETECTION AND MONITORING SYSTEM OF HEART DISEASE BASED ON ELECTROCARDIOGRAM SIGNAL

Abstract Heart disease is the number one deadly disease in Indonesia. One of the main causes of fatality is the late detection of the disease. To avoid escalation of mortality caused by heart disease, we need early detection and monitoring system of heart disease. Therefore, in this research we propose an early detection and monitoring system of heart disease based on ECG signal. The proposed system has three main components: ECG hardware, smartphone, and server. Since the proposed system is designed to classify heartbeat signal, heart disease symptom can be detected as early as possible. We use FLVQ-PSO algorithm to classify heartbeat signal. Experiment result shows that classification accuracy of the system can reach 91.63%. Moreover, the proposed system can be used to verify patients heartbeat by cardiologists from distant area (telehealth). Experiment result shows that responsiveness of the system for the telehealth system is less than 0.6 seconds

KLASIFIKASI SCHIZOPHRENIA BERDASARKAN SINYAL EEG MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE

Schizophrenia adalah sindrom yang disertai dengan manifestasi psikologis spesifik yang disebabkan ketidakseimbangan dopamin dalam otak. Salah satu metode yang dapat digunakan untuk mendeteksi schizophrenia adalah elektroensephalogram (EEG). Kemampuan EEG untuk merekam perubahan aktifitas listrik otak membuatnya dapat digunakan sebagai alat identifikasi dengan luaran berupa rekaman sinyal. Pada penelitian ini digunakan algoritma Support Vector Mechine sebagai metode pengolahan data sinyal EEG untuk pengklasifikasian schizophrenia. Dataset yang digunakan adalah kaggle dataset yang terdiri dari 3072 atribut dan 2 kelas yaitu kelas normal dan kelas schizophrenia yang diambil dari 81 subyek, dengan 32 subyek normal dan 49 subyek penderita schizophrenia. Sebelum proses pelatihan, data latih dan data uji dipisah secara acak dengan metode hold-out validation dengan rasio data latih dan data uji 9 : 1, 4 : 1, 7 : 3 dan 3 : 2. Hasil pengujian menghasilkan akurasi terbaik saat rasio 9 : 1 yaitu 70%. Kata kunci : Kaggle dataset, klasifikasi, schizophrenia , sinyal EEG, SV

Detecting violent scenes in movies using Gated Recurrent Units and Discrete Wavelet Transform

The easiness of accessing video on various platforms can negatively impact if not done wisely, especially for children. Parental supervision is needed so that movies platforms avoid inappropriate displays such as violence. Violent scenes in movies can trigger children to commit acts of violence, which is not desired. Unfortunately, it is not easy to supervise them fully. This study proposed a method for automatic detection of violent scenes in movies. Automatic violence detection assists the parents and censorship institutions in detecting violence easily. This study uses Gated Recurrent Units (GRU) algorithm and wavelet as feature extraction to detect violent scenes. This paper shows comparative studies on the variation of the mother wavelet. The experimental results show that GRU is robust and deliver the best performance accuracy of 0.96 while combining with mother wavelet Symlet and Coiflets8. The combination of GRU with wavelet Coiflets8 shows better results than the predecessor

Face Recognition Using Complex Valued Backpropagation

Face recognition is one of biometrical research area that is still interesting. This study discusses the Complex-Valued Backpropagation algorithm for face recognition. Complex-Valued Backpropagation is an algorithm modified from Real-Valued Backpropagation algorithm where the weights and activation functions used are complex. The dataset used in this study consist of 250 images that is classified in 5 classes. The performance of face recognition using Complex-Valued Backpropagation is also compared with Real-Valued Backpropagation algorithm. Experimental results have shown that Complex-Valued Backpropagation performance is better than Real-Valued Backpropagation