Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Faktor Exacta
Kurniawan, Denni
Universitas Budi Luhur
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Komparasi Pengaruh Model Klasifikasi Naive Bayes dan Support Vector Machine Pada Analisis Data Sentimen Di Bidang Pendidikan Fajriah, Riri; Kurniawan, Denni
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.22342

Abstract

Optimalisasi Model Klasifikasi Naive Bayes dan Support Vector Machine Dengan Fast Text dan Chi Square Pada Analisis Sentimen Penyelenggaraan Pembelajaran Pemrograman di Fasilkom Universitas Mercu Buana Fajriah, Riri; Kurniawan, Denni
Faktor Exacta Vol 17, No 4 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i4.24751

Abstract

The implementation of effective programming learning at the Faculty of Computer Science, Universitas Mercu Buana is one of important strategy. This expectation is constrained because the results of the evaluation of the competency achievements of many graduates have not mastered programming skills well. Therefore, the research conducted is related to analyzing the sentiments of all stakeholders who have been involved with the implementation of programming learning. The data source based on the results of an online questionnaire. The sentiment data analysis process uses the Cross Industry Standard Process for Data Mining method with the Naive Bayes and Support Vector Machine classification models. The result of the research is an increase in the accuracy of sentiment analysis data processing which previously only used the Naive Bayes Algorithm only achieving an accuracy of 65.56% and by optimizing with Feature Extraction Fast Text, the accuracy achievement increased to 90.49%. While optimizing the algorithm using Feature Selection Chi Square can make the Support Vector Machine classification model optimized to achieve an accuracy value of 99.58% from the previous accuracy achievement was 90.72%. This research can prove that optimizing the application classification model algorithms can use using Fast Text and Chi Square techniques.
Co-Authors Fajriah, Riri