Retantyo Wardoyo

Bio: Retantyo Wardoyo is an academic researcher from Gadjah Mada University. The author has contributed to research in topics: Decision support system & Pattern recognition (psychology). The author has an hindex of 12, co-authored 135 publications receiving 1069 citations. Previous affiliations of Retantyo Wardoyo include Magister.

Fuzzy multi attribute decision making (Fuzzy MADM)

Abstract: Buku ini tersusun atas 6 bab,yaitu: Bab 1,membahas tentang teori himpunan fuzzy.Pada bagian ini akan di jelaskan konsep dasar himpunan fuzzy,perbedaan antara himpunan crisp dan himpunan fuzzy,fungsi keanggotaan,operator-operator fuzzy,dan bilangan fuzzy. Bab 2,embahas tentang relasi prefensi fuzzy.Relasi preferensi fuzzy ini nantinya akan memegang peranan penting bagi proses pengambilan keputusan. Pada bagian ini akan di jelaskan beberapa format relasi preferensi dan fungsi-fungsi yang mentransformasikan suatu format preferensi tertentu ke dalam format relaso preferensi fuzzy. Bab 3,membahas tentang Multi-Attribute Decision Making (MADM). Pada bagian ini akan dijelaskan beberapa metode penyelesaian masalah MADM, seperti Simple Additive Weighting Method (SAW),Weighting Product (WP) , ELECTRE ,TOPSIS dan AHP.Disamping itu,pada bagian ini juga akan dijelaskan tentang metode pengembangan lain untuk penyelesaian masalah MADM, yaitu melalui pendekatan subyektif,objektif dan integrasi antara pendekatan subyektif & objektif. Bab 4,membahas tentang fuzzy Multi-Attribute Decision Making (MADM).pada bagian ini akan dijelaskan modelo fuzzy MMADM standar yaitu model yager dan beberapa perkembangan model fuzzy MADM, seperti pengembangn metode Baas & Kwakemaak,penggunaan interval aritmatik,dan penggunaan indeks kekuatan dan kelemahan. Bab 5,membahas fuzzy Multi-Expert Multi-Attribute Decision Making. pada bagian ini akan di jelaskan beberapa metode pengembangan dalam menyelesaikan masalah GDM pada lingkungan fuzzy.Untuk kepentingan tersebut,pada bagian ini dijelaskan pula beberapa operator agregasi preferensi seperti OWA,IOWA, I-IOWA, dan C-IOWA; aplikasi metode eksploitasi seperti Quantifer Guided non-Dominance dregree (QGDD) dan quantifer guided non-Dominance Degree (QGNDD).Format preferensi seragam,beragam,maupun format preferensi yang tidak lengkap juga akan dibahas pada bagian ini. Selain itu,pada bagian ini juga akan menjelaskan penyelesaian masalah GDM dengan format preferensi berbentuk relasi prefernsi linguistik multiplikatif. Bab 6,membahas fuzzy clustering. pada bagian ini akan di jelaskan beberapa metode pengclusteran seperti fuzzy c-means (FCM),possibilistic c-means (PCM),fuzzy possibilitis c-means (FPCM) standar, maupun fuzzy possibility c-means (FCM) yang sudah dimodifikasi.

Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM

Abstract: Support Vector Machines (SVM) is one of machine learning methods that can be used to perform classification task. Many researchers using SVM library to accelerate their research development. Using such a library will save their time and avoid to write codes from scratch. LibSVM is one of SVM library that has been widely used by researchers to solve their problems. The library also integrated to WEKA, one of popular Data Mining tools. This article contain results of our work related to complexity analysis of Support Vector Machines. Our work has focus on SVM algorithm and its implementation in LibSVM. We also using two popular programming languages i.e C++ and Java with three different dataset to test our analysis and experiment. The results of our research has proved that the complexity of SVM (LibSVM) is O(n3) and the time complexity shown that C++ faster than Java, both in training and testing, beside that the data growth will be affect and increase the time of computation.

ELECTRE Methods in Solving Group Decision Supports System Bioinformatics on Gene Mutation Detection simulation

Abstract: Analysis of genes expression can be done with the investigation of a particular microarray data for the description of a gen. This is done to identify whatgenes that were active in the human body. Detection of gene mutation is an activity that can provide contribution in the medical field. Detection of mutated gene is needed to avoid the diseases caused by themsuch as cancer. The detection of gene mutations can be performed by utilizing computer-based system. Group Decision Support System (GDSS) is a computer-based system that can be utilized in detecting human gene mutations that cause disease. The ELECTRE method, which is a Multi-Attribute DecisionMaking, is a method in modeling multi-criteria GDSS. In this paper we propose implementation of model for multi-criteria GDSS in which the simulation data is the mutated genes that can causecancer

Review of Image Compression and Encryption Techniques

Abstract: In line with a growing need for data and information transmission in a safe and quick manner, researches on image protection and security through a combination of cryptographic and compression techniques begin to take form. The combination of these two methods may include into three categories based on their process sequences. The first category, i.e. cryptographic technique followed by compression method, focuses more on image security than the reduction of a size of data. The second combination, compression technique followed by the cryptographic method, has an advantage where the compression technique can be lossy, lossless, or combination of both. The third category, i.e. compression and cryptographic technologies in a single process either partially or in the form of compressive sensing(CS) provides a good data safety assurance with such a low computational complexity that it is eligible for enhancing the efficiency and security of data/information transmission.

Batik Classification with Artificial Neural Network Based on Texture-Shape Feature of Main Ornament

Abstract: Batik is a text ile with mot ifs of Indonesian culture which has been recognized by UNESCO as world cultural heritage. Bat ik has many motifs which are classified in various classes of batik. This study aims to combine the features of texture and the feature of shapes’ ornament in batik to classify images using artificial neural networks. The value of texture features of images in batik is extracted using a gray level co-occurrence matrices (GLCM) which include Angular Second Moment (ASM) / energy), contrast, correlation, and inverse different moment (IDM). The value of shape features is extracted using a binary morphological operation which includes compactness, eccentricity, rectangularity and solidity. At this phase of the training and testing, we compare the value of a classification accuracy of neural networks in each class in batik with their texture features, their shape, and the combination of texture and shape features. From the three features used in the classification of batik image with artificial neural networks, it was obtained that shape feature has the lowest accuracy rate of 80.95% and the combination of texture and shape features produces a greater value of accuracy by 90.48%. The results obtained in this study indicate that there is an increase in accuracy of batik image classification using the artificial neural network with the combination of texture and shape features in batik image.

Statistical Pattern Recognition

Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Abstract: The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software development in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source communities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data.