37 research outputs found

    Multiple Sequence Alignment Menggunakan Hidden Markov Model

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    Mudah dan murahnya proses pengumpulan data biologi molekuler saat ini menyebabkan ukuran basis data genetika meningkat dengan pesat. Hal ini meningkatkan kebutuhan akan alat bantu komputasi untuk menganalisa data tersebut. Salah satu task dasar dalam menganalisa data biologi molekuler adalah Multiple Sequence Alignment. Program Multiple Sequence Alignment yang sering digunakan oleh praktisi biomolekuler adalah ClustalX yang menggunakan metode komputasi progressive pairwise alignment.Salah satu metode yang saat ini banyak dikaji untuk menghasilkan Multiple Sequence Alignment adalah Hidden Markov Model. Hidden Markov Model cocok digunakan dalam Multiple Sequence Alignment karena Multiple Sequence Alignment dapat dipandang sebagai masalah pengenalan pola. Hidden Markov Model menggunakan algoritma pembelajaran Baum-Welch untuk mengestimasi parameter-parameter dalam HMM dan algoritma Viterbi untuk melakukan alignment dari unaligned sequence. Pada penelitian ini dilakukan eksperimen untuk menerapkan Hidden Markov Model dalam menghasilkan Multiple Sequence Alignment dari sequence protein yang belum ter-align dan dilakukan pengujian menggunakan data sequence protein BaliBASE 3.0 dengan membandingkan hasil alignment yang menerapkan Hidden Markov Model dengan hasil alignment program ClustalX. Hasil eksperimen menunjukkan bahwa implementasi Hidden Markov Model pada Multiple Sequence Alignment memiliki performa lebih baik pada data sequence yang memiliki identity tinggi dan mengalami penurunan perfoma pada data sequence yang panjang dan data sequence yang memiliki banyak noise seperti N/C terminal extension atau insertion

    MULTIPLE SEQUENCE ALIGNMENT MENGGUNAKAN HIDDEN MARKOV MODEL

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    Mudah dan murahnya proses pengumpulan data biologi molekuler saat ini menyebabkan ukuran basis data genetika meningkat dengan pesat. Hal ini meningkatkan kebutuhan akan alat bantu komputasi untuk menganalisa data tersebut. Salah satu task dasar dalam menganalisa data biologi molekuler adalah Multiple Sequence Alignment. Program Multiple Sequence Alignment yang sering digunakan oleh praktisi biomolekuler adalah ClustalX yang menggunakan metode komputasi progressive pairwise alignment. Salah satu metode yang saat ini banyak dikaji untuk menghasilkan Multiple Sequence Alignment adalah Hidden Markov Model. Hidden Markov Model cocok digunakan dalam Multiple Sequence Alignment karena Multiple Sequence Alignment dapat dipandang sebagai masalah pengenalan pola. Hidden Markov Model menggunakan algoritma pembelajaran Baum-Welch untuk mengestimasi parameter-parameter dalam HMM dan algoritma Viterbi untuk melakukan alignment dari unaligned sequence. Pada penelitian ini dilakukan eksperimen untuk menerapkan Hidden Markov Model dalam menghasilkan Multiple Sequence Alignment dari sequence protein yang belum ter-align dan dilakukan pengujian menggunakan data sequence protein BaliBASE 3.0 dengan membandingkan hasil alignment yang menerapkan Hidden Markov Model dengan hasil alignment program ClustalX. Hasil eksperimen menunjukkan bahwa implementasi Hidden Markov Model pada Multiple Sequence Alignment memiliki performa lebih baik pada data sequence yang memiliki identity tinggi dan mengalami penurunan perfoma pada data sequence yang panjang dan data sequence yang memiliki banyak noise seperti N/C terminal extension atau insertion. Keyword: BaliBASE 3.0, Baum-Welch, Hidden Markov Model, Multiple Sequence Alignment, Viterbi

    1 Multiple SequeMULTIPLE SEQUENCE ALIGNMENT MENGGUNAKAN HIDDEN MARKOV MODELnce Alignment Menggunakan Hidden Markov Model

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    Mudah dan murahnya proses pengumpulan data biologi molekuler saat ini menyebabkan ukuran basis data genetika meningkat dengan pesat. Hal ini meningkatkan kebutuhan akan alat bantu komputasi untuk menganalisa data tersebut. Salah satu task dasar dalam menganalisa data biologi molekuler adalah Multiple Sequence Alignment. Program Multiple Sequence Alignment yang sering digunakan oleh praktisi biomolekuler adalah ClustalX yang menggunakan metode komputasi progressive pairwise alignment. Salah satu metode yang saat ini banyak dikaji untuk menghasilkan Multiple Sequence Alignment adalah Hidden Markov Model. Hidden Markov Model cocok digunakan dalam Multiple Sequence Alignment karena Multiple Sequence Alignment dapat dipandang sebagai masalah pengenalan pola. Hidden Markov Model menggunakan algoritma pembelajaran Baum-Welch untuk mengestimasi parameter-parameter dalam HMM dan algoritma Viterbi untuk melakukan alignment dari unaligned sequence. Pada penelitian ini dilakukan eksperimen untuk menerapkan Hidden Markov Model dalam menghasilkan Multiple Sequence Alignment dari sequence protein yang belum ter-align dan dilakukan pengujian menggunakan data sequence protein BaliBASE 3.0 dengan membandingkan hasil alignment yang menerapkan Hidden Markov Model dengan hasil alignment program ClustalX. Hasil eksperimen menunjukkan bahwa implementasi Hidden Markov Model pada Multiple Sequence Alignment memiliki performa lebih baik pada data sequence yang memiliki identity tinggi dan mengalami penurunan perfoma pada data sequence yang panjang dan data sequence yang memiliki banyak noise seperti N/C terminal extension atau insertion. Keyword: BaliBASE 3.0, Baum-Welch, Hidden Markov Model, Multiple Sequence Alignment, Viterbi

    Evaluating Library Services Quality Using GDSS-AHP, LibQual and IPA

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    Library services quality is one of the most vital parts in library management. Evaluation of the library services based on the perspective of users is important. In this paper, we propose a collaboration of GDSS-AHP (Group Decision Support System-Analytical Hierarchy Process), LibQual, and IPA (Importance-Performance Analysis) methods to evaluate library services quality. The collaboration of GDSS-AHP and LibQual is used to calculate the weight of each evaluation statement and the level of library services quality based on users’ perception and expectation. IPA is then used to determine the position of the value of each evaluation statement in IPA’s four quadrants to obtain the recommended level for the library services improvement. This study is conducted at the Library of the Ministry of Trade of Indonesia, involving four decision makers: a head librarian, a library academic expert, and two library practitioners. Fifty library visitors become respondents to assess the quality services questionnaires. Based on their responses, we obtain that users’ satisfaction level is at least satisfied by 76.49 %. Meanwhile, usability testing is also conducted on the developed system by using three observation elements: effectiveness, efficiency and satisfaction. The usability testing is performed on five respondents, one admin, and two decision makers, and results in an average usability level of 90.03%

    Flower Pollination Inspired Algorithm on Exchange Rates Prediction Case

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    Flower pollination algorithm is a bio-inspired system that adapts a similar process to genetic algorithm, that aims for optimization problems. In this research, we examine the utilization of the flower pollination algorithm in linear regression for currency exchange cases. The solutions are represented as a set that contains regression coefficients. Population size for the candidate solutions and the switch probability between global pollination and local pollination have been experimented with in this research. Our result shows that the final solution is better when a higher size population and higher switch probability are employed. Furthermore, our result shows the higher size of the population leads to considerable running time, where the leaning probability of global pollination slightly increases the running time

    Convolutional Neural Networks for Handwritten Javanese Character Recognition

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    Convolutional neural network (CNN) is state-of-the-art method in object recognition task. Specialized for spatial input data type, CNN has special convolutional and pooling layers which enable hierarchical feature learning from the input space. For offline handwritten character recognition problem such as classifying character in MNIST database, CNN shows better classification result than any other methods. By leveraging the advantages of CNN over character recognition task, in this paper we developed a software which utilizes digital image processing methods and CNN module for offline handwritten Javanese character recognition. The software performs image segmentation process using contour and Canny edge detection with OpenCV library over captured handwritten Javanese character image. CNN will classify the segmented image into 20 classes of Javanese letters. For evaluation purposes, we compared CNN to multilayer perceptron (MLP) on classification accuracy and training time. Experiment results show that CNN model testing accuracy outperforms MLP accuracy although CNN needs more training time than MLP

    Multiple sequence aligment menggunakan hidden Markov model dengan augemented set dan pengaruhnya terhadap akurasi pohon filogenetik

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    The basic tasks in molecular biology data analysis are multiple sequence alignment (MSA) and phylogenetic tree inference. The quality of the phylogenetic tree depends on the quality of the MSA. Hidden Markov model (HMM) is one of the good methods to generate the MSA, but having sequences with low similarity, this method will produce less optimal MSA. This research works on performing multiple alignments of protein sequences with low similarity using the HMM, which can be used as input and it produces more accurate phylogenetic tree. The research is carried out by building augmented set. The parameters are the number of child sequences and the percentage of mutation applied in child sequence. There are two kind of m utation process, first based on substitution matrix BLOSUM 80 and second, random mutation. Augmented set used as input into the HMM to obtain the MSA. Baum welch learning algorithm is used to estimate the parameters in HMM. While Viterbi algorithm is used to arrange the alignment from unaligned sequences. The prototype tool is built using Java programming language and utilizing Biojava library. In this research, the accuracy of phylogenetic trees using MSA with augmented set is compared with the MSA without augmented set. There are two phylogenetic tree inference methods used in here. First, neighbour joining is conducted using ClustalX tool. Second, parsimony methods is conducted using Phylip Protpars tool. The data are the amino acid sequences of ribosomes 16S from mitochondria. The accuracy of phylogenetic tree using augmented set based on matrix BLOSUM 80 and the neighbour joining method increases when the datasets with criteria : the number of sequences and HDS (highly diverge sequence) are small enough, and the difference between maximum length and average length of sequences is small enough. While the accuracy of phylogenetic trees using the augmented set and the parsimony method can increase or decrease arbitrarily

    Identifying Hate Speech in Bahasa Indonesia with Lexicon-Based Features and Synonym-Based Query Expansion

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    Freedom of social media users who are not controlled in giving opinions can make it easier for users to attack certain people, objects, or environments with hateful language or commonly called hate speech. According to the Indonesia Criminal Investigation Police, 80% of cybercrimes reported were expressions of hatred. Preventive actions taken by Facebook & Twitter are deemed ineffective because checking hate speech is still manually through user reports. In this study, we used a machine learning algorithm, which is Support Vector Machine (SVM), to identify whether a speech is considered as hate speech or not. We combined the SVM with the Lexicon-based Features and Synonym-based Query Expansion method. The models were trained and evaluated by calculating Accuracy, Precision, Recall, and F-measure. This study shows that the use of the Synonym-based Query Expansion method can improve the performance of the SVM model with Lexicon-based as its feature

    Improving Detection Performance of Helmetless Motorcyclists Using the Combination of HOG, HOP, and LDB Descriptor

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    A significant number of motorcyclists that do not wear helmets lose their lives during a traffic accident, one of the major causes of death globally. This led to the design and development of a system capable of detecting helmetless motorcyclists in real-time to reduce the number of deaths. Generally, this system consists of 3 subsystems, namely moving object segmentation, motorcycle classification, and helmetless head detection. The Histograms of Oriented Gradients (HOG) descriptor has been used in preliminary studies, which resulted in fast computation time and high accuracy. However, this descriptor was less effective when applied to images with varying lighting and was unable to distinguish local pattern features. Therefore, this study proposed a new descriptor algorithm, namely Histogram of Oriented Phase and Gradient- Local Difference Binary (HOPG-LDB), which combined the HOG, Histogram of Oriented Phase (HOP), and Local Difference Binary (LDB) descriptors. The HOP was used to enhance the inability of the HOG to be effectively used in detecting images with varying lighting, and the LDB was used to extract local pattern features. The results showed that the proposed method can improve the accuracy of motorcycle classification and helmetless head detection compared to HOG, HOP, LDB, HOG-HOP, HOG-LDB, and HOP-LDB descriptors. Furthermore, the motorcycle classification accuracies of the proposed method were 97.05%, 97.25%, and 99.35% for the JSC1, JSC2, and database1 datasets. Meanwhile, the helmetless head detection results of the proposed method were 71.21%, 66.63%, and 91.73 for the JSC1, JSC2, and database2 datasets
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