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All Journal dCartesian: Jurnal Matematika dan Aplikasi AdMathEdu : Jurnal Ilmiah Pendidikan Matematika, Ilmu Matematika dan Matematika Terapan JOIV : International Journal on Informatics Visualization Indonesian Journal of Applied Statistics BAREKENG: Jurnal Ilmu Matematika dan Terapan Limits: Journal of Mathematics and Its Applications Budimas : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Kepada Masyarakat Aptekmas : Jurnal Pengabdian Kepada Masyarakat Equiva
Setiyowati, Ririn
Universitas Sebelas Maret
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Journal : JOIV : International Journal on Informatics Visualization

 1 
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.
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.
Co-Authors Amelia, Cynthia Rizky Arrazaq, Khamid Muhammad Dewi Retno Sari Saputro, Dewi Retno Diah Nur Hidayati Elnatan Dimas Aditya Endang Sulastri Handajani, Sri Sulistijowati Haning Puspita, Aulia Salsabila Hidayati, Diah Nur Iwan Tri Riyadi Yanto Iwan Tri Riyadi Yanto Khurdi, Nughthoh Arfawi Martini, Titin Sri Mulyani, Sabrina Sri Nughthoh Arfawi Kurdhi, Nughthoh Arfawi Nugroho Arif Sudibyo Nur Azizah Nursyiva Irsalinda Pangadi Pangadi Pangadi, Pangadi Purnami Widyaningsih Rachmawati, Zahra Febrilia Ramadhani, Shinta Nur Pratiwi Rasyidah, - S Siswanto Slamet Krismanto Supriyadi Wibowo sutanto sutanto Sutanto Sutanto Sutanto Sutanto Sutrima Sutrima Tri Lestari Usdianto, Dwitama Alphin Venna Ade Riestiana Vika Yugi Kurniawan Winaro, Bowo