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All Journal Building of Informatics, Technology and Science International Journal of Basic and Applied Science
Nurafni Damanik
National Taipei University of Technology
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Physics

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Performance Analysis Of Support Vector Machine In Identifying Comments And Ratings On E-Commerce Mutiara S. Simanjuntak; Nurafni Damanik; Allwine
International Journal of Basic and Applied Science Vol. 11 No. 1 (2022): June: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v11i1.79

Abstract

Consumers who have shopped at E-Commerce will provide reviews/comments on products that have been purchased. Customer confidence in the rating is hampered due to inconsistency of answers such as reviews that have negative text with a positive rating value. For this reason, a technique is needed to adjust the rating with comments or reviews of purchased goods to make it easier for consumers when shopping to see the rating directly without reading the reviews/comments of previous buyers. purpose of this study is to classify comments and ratings and then obtain the results of the accuracy of the classification system so that the above problems can be answered.This study uses Support Vector Machine classification technique because this algorithm is better in classification’s terms. Data used are 1044 comment data and 1044 rating. Data are grouped into Good, Neutral, Less good categories using Python by Google Colab and divided into training and test data. To test capability of system, data that has been classified then analyzed using Confusion matrix. Results showed that SVM Algorithm was able to classify with an accuracy rate of 71.14%, 88% precision, and 79% recall.SVM algorithm is able to formulate training data with an accuracy of 91.3%.
K-Means and AHC Methods for Classifying Crime Victims by Indonesian Provinces: A Comparative Analysis Ridha Maya Faza Lubis; Jen-Peng Huang; Pai-Chou Wang; Nurafni Damanik; Ade Clinton Sitepu; Ceria D Simanullang
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3630

Abstract

Crime is a common phenomenon that often occurs in society and has a negative impact both individually and collectively. Gaining a deeper understanding of crime can help us tackle the problem more efficiently. In an era that is increasingly complex and globally connected as it is now, crime has undergone significant developments and changes. Crime remains a serious threat to our security, integrity, and well-being. Some common types of crime include theft, robbery, fraud, physical abuse, and murder. Crime can happen anytime and anywhere. To tackle crime, data mining techniques can be used to analyze the surrounding situation and gain new knowledge. One approach is to classify provinces based on crime data from previous years so that crime-prone areas can be identified and security measures can be increased. In this study, two grouping methods were used, namely K-Means and AHC using the complete linkage mode. There are 34 provinces in Indonesia which are grouped based on the number of victims of crime from 2019 to 2021. The grouping results using the K-Means method yield two groups with 17 provinces each. However, using the AHC complete linkage method, there is a difference in the number of provinces between cluster 0 and cluster 1 compared to the K-Means results. In addition, there are differences in the location of the province in the cluster between the two methods. In the K-Means method, provincial data is located in cluster 0, while in the AHC method, the province's data is in cluster 1. Thus, this study provides insight into crime in Indonesia and provides information about the grouping of provinces based on crime rates using the K-Means method. Means and AHC
Co-Authors Ade Clinton Sitepu Allwine Ceria D Simanullang Jen-Peng Huang Mutiara S. Simanjuntak Pai-Chou Wang Ridha Maya Faza Lubis