Garuda - Garba Rujukan Digital

p-Index From 2020 - 2025

1.99

P-Index

This Author published in this journals

All Journal Techno.Com: Jurnal Teknologi Informasi JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Pengabdian UntukMu NegeRI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Pendidikan dan Konseling ABDIKOM

Helena Nurramdhani Irmanda

Universitas Pembangunan Nasional Veteran Jakarta

Author-ID : 2400898

Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

Published : 11 Documents Claim Missing Document

Claim Missing Document

Articles

Title

Klasifikasi Jenis Pantun dengan Metode Support Vector Machines (SVM) Helena Nurramdhani Irmanda; Ria Astriratma
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 5 (2020): Oktober 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

This study aims to create a model for categorizing pantun types and analyze the accuracy of support vector machines (SVM). The first stage is collecting pantun that have been labeled with pantun category. The pantun categories consist of pantun for children, pantun for young people, and pantun for elder. After collecting data, the next stage is pre-processing. This pre-processing stage makes data ready to be processed on the extraction stage. The pre-processing stage consists of text segmentation, case folding, tokenization, stop word removal, and stemming. The feature extraction stage is intended to analyze potential information and represent terms as a vector. Separating training data and testing data is necessary to be conducted before the classification process. Then the classification process is done by using multiclass SVM. The results of the classification are evaluated to obtain accuracy and will be analyzed whether the classification model is proper to be used. The results showed that SVM classified the types of pantun with accuracy of 81,91%.

Sentiment Analysis of Beauty Product E-Commerce Using Support Vector Machine Method Muhammad Rio Pratama; Faza Abdillah Gunawan Soerawinata; Rafdi Reyhan Zhafari; Rendy; Helena Nurramdhani Irmanda
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Customers who buy goods will provide an assessment in the form of a review. If negative reviews dominate an item, other customers will be reluctant to buy at that store, so customers look for other stores, affecting the store's revenue. Therefore, this study aims to classify e-commerce beauty product reviews using the Support Vector Machine to create a model to categorize beauty product reviews and analyze accuracy. The research phase begins by collecting 50,000 datasets consisting of 35,000 training data and 15,000 test data. After the data is collected, the data labeling stage is carried out, labeled positive and negative. Then the preprocessing step is carried out so that the data is ready to be processed in the feature extraction step. The feature extraction step aims to explore potential information that represents words. Furthermore, the resulting data is evaluated to obtain an accuracy value and determine whether the model made is feasible to use. The results showed that the Support Vector Machine could classify beauty product reviews well with an accuracy of 80.06%.

Co-Authors Anita Muliawati Erly Krisnanik Faza Abdillah Gunawan Soerawinata Hardiana Said Intan Hesti Indriana Kraugusteeliana Kraugusteeliana Muhammad Rio Pratama Nur Hafifah Matondang Nurul Chamidah Rafdi Reyhan Zhafari Rendy Ria Astriratma Santoni, Mayanda Mega Sarika Afrizal Yulnelly Yulnelly

Title Search

Found 2 Documents Search Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

Abstract

Abstract

Title

Found 2 Documents
Search
Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)