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All Journal Informasi Interaktif Journal of Applied Engineering and Technological Science (JAETS)
Titi Silfia
STIKOM Cipta Karya Informatika
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Analisis Perbandingan Algoritma Svm Dan Knn Untuk Klasifikasi Anime Bergenre Drama Vika Vitaloka Pramansah; Dadang Iskandar Mulyana; Titi Silfia
Informasi Interaktif Vol 7, No 2 (2022): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

There are many genres of anime such as drama, action, romance, comedy, and so on. However, because there are so many anime genres, it is quite difficult for viewers to find anime whose genre they like, such as the drama genre which tells about everyday human life which is quite light in nature. From these problems, a classification method is needed to classify anime that belongs to the drama genre. Classification has several algorithms including Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). SVM and KNN algorithms have been widely used and have a good level of accuracy. In this study, a comparative analysis will be carried out between the two algorithms, the dataset used is 12,294 data and 2 genre classes, namely drama and non-drama, the attribute of the anime dataset is 7. The results obtained in this study indicate that the K-Nearest Neighbors Algorithm (KNN) ) get a training accuracy value of 100% and a test accuracy value of 84%. And also the Support Vector Machine (SVM) algorithm gets a training accuracy value of 83% and a test accuracy value of 82%. The results of the accuracy values of the two algorithms indicate that the K-Nearest Neighbors (KNN) algorithm has a better testing accuracy than the Support Vector Machine (SVM) with a fairly thin difference between the two algorithms.
Implementation of Data Mining Using K-Means Clustering Method to Determine Sales Strategy In S&R Baby Store Tri Wahyudi; Titi Silfia
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 1 (2022): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (827.472 KB) | DOI: 10.37385/jaets.v4i1.913

Abstract

The S&R Baby Store store is a Small and Medium Enterprise (SME) that is engaged in baby equipment, but there is a lot of competition between small and medium enterprises (SMEs) who are engaged in the same field, so that many products sold are of course not all sold out, some are lacking. in demand. Therefore the S&R Baby Store store needs a good sales strategy in order to increase sales profit. This study discusses the application of data mining, using the K-Means Clustering algorithm with the CRISP-DM method. Implementation using RapidMiner 9.10 which is done by entering sales transaction data with a total of 4 attributes and forming 4 clusters consisting of very in demand, in demand, moderate in demand and less in demand. the second cluster with 944 products, the third cluster with 2 products, and the fourth cluster with 43 products. The results of the cluster above are the products sold are the best-selling product categories, then the results of the cluster are validated using the Davies-Bouldin Index with a DBI value generated from clustering of 0.560.
Co-Authors Dadang Iskandar Mulyana` Tri Wahyudi Vika Vitaloka Pramansah