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Journal : JOIV : International Journal on Informatics Visualization

A Framework of Mutual Information Kullback-Leibler Divergence based for Clustering Categorical Data Iwan Tri Riyadi Yanto; Ririn Setiyowati; Nur Azizah; - Rasyidah
JOIV : International Journal on Informatics Visualization Vol 5, No 1 (2021)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.1.462

Abstract

Clustering is a process of grouping a set of objects into multiple clusters, so that the collection of similar objects will be grouped into the same cluster and dissimilar objects will be grouped into other clusters. Fuzzy k-means Algorithm is one of clustering algorithm by partitioning data into k clusters employing Euclidean distance as a distance function. This research discusses clustering categorical data using Fuzzy k-Means Kullback-Leibler Divergence. In the determination of the distance between data and center of cluster uses mutual information known as Kullback-Leibler Divergence distance between the joint distribution and the product distribution from two marginal distributions. Extensive theoretical analysis was performed to show the effectiveness of the proposed method. Moreover, the proposed method's comparison results with Fuzzy Centroid and Fuzzy k-Partition approaches in terms of response time and clustering accuracy were also performed employing several datasets from UCI Machine Learning. The experiment results show that the proposed Algorithm provides good results both from clustering quality and accuracy for clustering categorical data as compared to Fuzzy Centroid and Fuzzy k-Partition.
The Comprehensive Mamdani Inference to Support Scholarship Grantee Decision - Humaira; - Rasyidah; - Junaldi; Indri Rahmayuni
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.2.449

Abstract

Fuzzy Mamdani has been mostly used in various disciplines of science. Its ability to map the input-output in the form of a surface becomes an interesting thing. This research took DSS case of a scholarship grantee. Many criteria in taking a decision need to be simplified so that the result obtained remains intuitive. The model completion by conducting two stages consisted of two phases. The first phase consists of four FIS blocks. The second phase consists of one FIS block. The FIS design in the first phase was designed in such a way so that the output obtained has a big score interval. FIS output at the first phase will become FIS input at the second phase. This big value range becomes good input at FIS in the second phase. Each FIS block has different total input. Until the surface formed must be seen from various dimensions to assure trend surface increasing or decreasing softly. This kind of thing is conducted by observing the movement of output dots kept for its soft surface form. The output dots change influenced by the membership function, the regulations used, total fuzzy set, and parameter value of membership function. This research used the Gaussian membership function. The Gaussian membership function is highly suitable for this DSS case. This article also explains the usage of a fuzzy set in each input, the parameter from the membership function, and the input value range. After observing the surface form with an intuitive approach, then this model needs to be evaluated. The evaluation was done to measure the model performance using Confusion Matrix. The result of model performance obtained accuracy in the amount of 85%.
Laying Chicken Algorithm (LCA) Based For Clustering Iwan Tri Riyadi Yanto; Ririn Setiyowati; Nursyiva Irsalinda; - Rasyidah; Tri Lestari
JOIV : International Journal on Informatics Visualization Vol 4, No 4 (2020)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.4.467

Abstract

Numerous research and related applications of fuzzy clustering are still interesting and important. In this paper, Fuzzy C-Means (FCM) and Laying Chicken Algorithm (LCA) were modified to improve local optimum of Fuzzy Clustering presented by using UCI dataset. In this study, the proposed FCMLCA performance was also compared to baseline technique based on CSO methods. The simulation results indicate that the FCMLCA method have better performance than the compared methods.
Implementing FAST Method on the Development of Object-Oriented Cooperative Information Systems Meri Azmi; Yance Sonatha; Ervan Asri; - Rasyidah; Dwi Sudarno Putra
JOIV : International Journal on Informatics Visualization Vol 2, No 4-2 (2018): Cyber Security and Information Assurance
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1337.912 KB) | DOI: 10.30630/joiv.2.4-2.189

Abstract

Cooperative is one form of businesses that is widely known as people concern and has a legal entity. In helping its members, the cooperative embraces familial value principle and mutual cooperation for the common social welfare. In carrying out its duties and functions, the cooperative requires an accurate and accountable recording. However, currently there are still many cooperatives performing their recording manually. Therefore, an information systems is needed in assisting the cooperative management in term of this recording. This research developed an object-oriented cooperative information systems using FAST method. Its purpose is to develop a cooperative information systems that can facilitate its  administrators in order to record the data through information systems-based, so the inaccuracy in recording, and loss of important data and archives can be avoided. Its result is a system that has been implemented into a cooperative. Hence, the information systems is developed using Java Programming language and MySQL database. From the system testing results shows that this information systems is capable in processing the accounting data associated with savings and loan transactions automatically, and produce information in the form of managerial and financial reports.