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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science
Hamdani Hamdani
Universitas Mulawarman, Samarinda
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Klasifikasi Motif Citra Batik Menggunakan Convolutional Neural Network Berdasarkan K-means Clustering Amin Padmo Azam Masa; Hamdani Hamdani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3246

Abstract

Batik has several motifs and patterns so it is necessary to identify certain objects in an image, one of which is the recognition of the image of Yogyakarta batik using the Convolutional Neural Network (CNN) method which is already popular in the use of image data classification. The introduction of batik imagery aims to contribute to the digitization of batik image data and at the same time provide information on types of batik to the public. The batik image recognition process using CNN in this study combines the image segmentation process and the enhancement process with median filters and sharpening. The segmentation process carried out before CNN aims to help separate foreground objects from objects that are not needed in the background. The segmentation process that is commonly used is using K-means Clustering. Where K-means Clustering is used to group data in the same category. Furthermore, the enhancement process using the median filter and sharpening was carried out separately to compare the batik image classification process using CNN based on K-means Clustering from the median filter results and the sharpening results. The batik image classification process with CNN based on K-means Clustering on the median filter resulted in an accuracy value of 100%. Meanwhile, the batik image classification process with CNN based on K-means Clustering from the sharpening results resulted in an accuracy value of 80%.
Perbandingan Algoritma K-Means dan Algoritma K-Medoids Pada Kasus Covid-19 di Indonesia Novianti Puspitasari; Gidion Lempas; Hamdani Hamdani; Haviluddin Haviuddin; Anindita Septiarini
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): Maret 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2994

Abstract

Analyzing Covid-19 data has been conducted in many types of research, but research on classifying each case from Covid-19 data in all provinces in Indonesia has yet to be available. This study uses two clustering algorithms, namely K-Means and K-Medoids, to classify positive cases recovered and died in the Covid-19 data into three clusters, namely low, medium and high. The research data is Covid-19 case data in all provinces in Indonesia from 2020 to 2021. In the clustering calculations, the three distance methods used in this study are the Chebyshev Distance, Manhattan Distance, and Euclidean Distance. Based on the Silhouette Coefficient test results for the three distance calculation methods, it was found that Manhattan Distance is the best distance calculation method for K-Means and K-Medoids. Furthermore, the results of testing the Sum Squared Error (SSE), Silhouette Coefficient (SC) and Davies Index Bouldin (DBI) methods for the resulting clusters show that the value generated by the K-Means algorithm is higher in the SC and DBI methods. This result is evidenced by the SC value of 0.838; 0.838; and 0.925 in positive cases, recovered and died. While the DBI value is 0.305 for positive cases, 0.295 for recovered cases and 1.569 for dead cases. Based on these values, it proves that K-Means is superior in grouping and placing clusters compared to K-Medoids.
Co-Authors Amin Padmo Azam Masa Anindita Septiarini, Anindita Gidion Lempas Haviluddin Haviluddin Puspitasari, Novianti