Rd Rohmat Saedudin

An Application of Rough Set Theory for Clustering Performance Expectancy of Indonesian e-Government Dataset

Performance expectancy has been studied as an important factor which influences e-government. Therefore, grouping of e-government users involving performance expectancy factor is still challenging. Computational model can be explored as an efficient clustering technique for grouping e-government users. This paper presents an application of rough set theory for clustering performance expectancy of e-government user. The propose technique base on the selection of the best clustering attribute where the maximum dependency of attribute in e-government data is used. The datasets are taken from a survey aimed to understand of the adoption issue in e-government service usage at Bandung city in Indonesia. At this stage of the research, we point how a soft set approach for data clustering can be used to select the best clustering attribute. The result of this study will present useful information for decision maker in order to make policy concerning theirs people and may potentially give a recommendation how to design and develop e-government system in improving public service.

A Relative Tolerance Relation of Rough Set for Incomplete Information Systems

Rough set theory is an effective approach to imprecision, vagueness, and uncertainty. This theory overlaps with many other theories such that fuzzy sets, evidence theory, and statistics. From a practical point of view, it is a good tool for data analysis. However, classical rough set theory cannot cope with the incomplete information systems where some attribute values are missing. There have been efforts in studying incomplete information systems for data classification which are based on the extensions of rough set theory. Moreover, the existing approaches have their weaknesses in terms of inflexible and imprecise in data classifications. To overcome these issues, we propose a relative tolerance relation of rough set (RTRS) to handling incomplete information systems, which it has flexibility and precisely for data classification. We compared RTRS with the existing approaches, the results show that our proposed method relatively achieves higher flexibility and precisely in data classification in incomplete information systems.

A Numerical Classification Technique Based on Fuzzy Soft Set Using Hamming Distance

In recent decades, fuzzy soft set techniques and approaches have received a great deal of attention from practitioners and soft computing researchers. This article attempts to introduce a classifier for numerical data using similarity measure fuzzy soft set (FSS) based on Hamming distance, named HDFSSC. Dataset have been taken from UCI Machine Learning Repository and MIAS (Mammographic Image Analysis Society). The proposed modeling consists of four phases: data acquisition, feature fuzzification, training phase and testing phase. Later, head to head comparison between state of the art fuzzy soft set classifiers is provided. Experiment results showed that the proposed classifier provides better accuracy when compared to the baseline fuzzy soft set classifiers.

An enhanced topologically significant directed random walk in cancer classification using gene expression datasets

Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.

Analisis Dan Perancangan Power Management Data Center Berdasarkan Tiering Level Di Pemerintahan Kabupaten Bandung Menggunakan Standar TIA-942 Dengan Metode PPDIOO Life-Cycle Approach

Dinas Komunikasi, Informatika dan Statistik Kabupaten Bandung bertanggung jawab terhadap layanan dibidang komunikasi dan informatika. Saat ini dinas tersebut telah memiliki data center yang berfungsi sebagai penyedia layanan di bidang komunikasi dan informatika bertempat pada gedung kantor DISKOMINFO. Berdasarkan rencana jangka panjang kedepannya data center tersebut akan dikembangkan baik secara infrastruktur, hardware, dan layanan yang diberikan. Oleh karena itu dibutuhkan sebuah perencanaan yang baik terhadap seberapa besar daya yang akan digunakan kedepannya dan sistem kelistrikan yang sesuai. Dibutuhkan perancangan power management data center yang sesuai dengan kebutuhan daya pada data center tersebut. Rancangan ini menggunakan metode PPDIOO Life-Cycle Approach pada tiga tahapan awal yaitu prepare, plan, design dan sesuai dengan standar TIA-942. Penggunaan metode ini cocok dengan pengembangan data center Pemerintah Kabupaten Bandung yang berkelanjutan dengan adanya tahap optimize untuk pengembangan jangka panjang data center tersebut. Hasil akhir dari penelitian ini berupa guideline pengembangan data center sesuai standar TIA-942 yang terbagi kedalama tiering level, diketahui bahwa penggunaan daya pada tier 1 sebesar 82954 watt membutuhkan generator yang berukuran 110 kVA, pada tier 2 penggunaan daya sebesar 111079 watt membutuhkan generator yang berukuran 145 kVA, dan pada tier 3 penggunaan daya sebesar 136309 watt membutuhkan generator yang berukuran 175 kVA.

Soft Set Approach for Clustering Graduated Dataset

Every university has objectives to make sure their students graduate on time. This objective can be achieved by using early warning system (EWS). Through EWS, students who will graduate late can be recognized in advance. Thus, appropriate interventions can be given to the student so that they can graduate on time. The predictive model is the core of an EWS, that built based on the graduated student data. The problem that often arises in a predictive model is the degree of accuracy. In order to increase the accuracy of the prediction, the clustering of attribute selection need to be conducted first. One of approach that can be used to cluster attribute selection is by using Maximum Degree of Domination in Soft Set Theory (MDDS) algorithm. This article implements the MDDS algorithm to cluster the attributes from student datasets. The results obtained from this research is the dominant attributes that can be used as a foundation to develop a predictive model of student graduation time.

Clustering Based on Classification Quality (CCQ)

Clustering a set of objects into homogeneous classes is a fundamental operation in data mining. Categorical data clustering based on rough set theory has been an active research area in the field of machine learning. However, pure rough set theory is not well suited for analyzing noisy information systems. In this paper, an alternative technique for categorical data clustering using Variable Precision Rough Set model is proposed. It is based on the classification quality of Variable Precision Rough theory. The technique is implemented in MATLAB. Experimental results on three benchmark UCI datasets indicate that the technique can be successfully used to analyze grouped categorical data because it produces better clustering results.