Article Per Year (5 Year)
p-Index From 2019 - 2024
2.147
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Semantik JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Inspiration Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IJISTECH (International Journal Of Information System & Technology) Indonesian Journal of Electrical Engineering and Computer Science Randwick International of Social Science Journal IJISTECH
Elviawaty Muisa Zamzami
Universitas Sumatera Utara
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Social Sciences Other

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Sinkron : Jurnal dan Penelitian Teknik Informatika

 1 
MODEL PREDIKSI PROFIL PELANGGAN BERDASARKAN KLASIFIKASI MELALUI PENDEKATAN SUPPORT VECTOR MACHINE Ogiana Kiro; Herman Mawengkang; Elviawaty Muisa Zamzami
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2022): Article Research Volume 7 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11608

Abstract

Nowadays the market is characterized globally, products and services are almost identical and there are many suppliers. The most important aspect in classifying data in data mining is classification. Classification techniques have been widely used in many problems in research. The purpose of this research is to build a model that can predict behavior based on the information of each customer. This research was conducted by making a Prediction Model of Customer Profile Based on Classification Through the Support Vector Machine Approach which aims to obtain a package prediction accuracy value that is suitable for WO (Wedding Organizer) customers in classifying based on the profile of prospective customers. In the optimization results on the SVM model kernel function, the linear and polynomial kernels get the same accuracy value on the training data of 99.29% and the testing data of 94.92%. The lowest accuracy value was obtained in the RBF kernel function of 97.16% on training data and 96.61% on testing data. the best precision class value in the data testing was obtained in the basic package at 100%. The total value of the appropriate prediction on the training data was obtained by 56 samples from a total of 59 samples, and 3 samples that did not match the prediction with an accuracy of 94.92% on the data testing
Co-Authors Anirma Kandida Br Ginting Anirma Kandida Br Ginting Arif Ridho Lubis Boy Yako Siahaan Eko Kuswardono Budiardjo Eko Kuswardono Budiardjo Erna Budhiarti Nababan Hayanti, Septi Herman Mawengkang Indah Mulyani Mahyuddin K. M. Nasution, Mahyuddin K. M. Maya Silvi Lydia Muhammad Rizal Muhammad Zarlis Niskarto Zendrato Ogiana Kiro Opim Salim Sitompul Suherman Suherman