Article Per Year (5 Year)
p-Index From 2020 - 2025
0.659
P-Index
This Author published in this journals
All Journal OKTAL : Jurnal Ilmu Komputer dan Sains
Agus Heri Yunial
Unknown Affiliation
Astronomy Chemistry Computer Science & IT Electrical & Electronics Engineering Social Sciences

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

Found 2 Documents
Search

 1 
Implementasi Machine Learning Pada Sistem Pendeteksi URL Bermuatan Konten Negatif Menggunakan Metode Algoritma Naive Bayes Dan Support Vector Machine Rada Rasi Saputri; Agus Heri Yunial
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 2 No 11 (2023): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Systems for filtering sites that contain negative content have been carried out by many previous researchers. However, these systems focus more on only 1 type of negative content and are mostly built for sites that are in English. The system that can filter URLs using Indonesian only focuses on negative content. This study aims to create a URL detection system that contains negative content using a Machine Learning model. The system in this study filters content on URLs that use Indonesian. This study uses 2 main models, namely Naïve Bayes, Support Vector Machine. Of all the models used, the SVM model produces the highest accuracy of 96.161%.
ANALISIS SENTIMEN TERHADAP RUU KUHP PASAL 353 AYAT 1 DARI TWITTER DENGAN METODE NAÏVE BAYES CLASSIFIER Basyarulhaq Fanani; Agus Heri Yunial
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 01 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This thesis aims to analyze sentiment towards the Criminal Code Bill Article 353 Paragraph 1 from data taken from Twitter using the Naive Bayes Classifier method. This research was conducted to find out the public's view of the controversial article. The data taken is in the form of tweets containing keywords related to the Criminal Code Bill for a certain period. The Naive Bayes Classifier method is used to classify tweets into positive or negative categories based on gender, age, and level of education as well as the impact of public sentiment on this article on the sustainability of democracy in Indonesia. The data used in this study is data from the online media Twitter. This study uses a quantitative method with a descriptive approach. The results of the sentiment analysis are expected to provide an overview of the public's perception of the article.
Co-Authors A. Nurul Anwar Ahmad Lutfi Basyarulhaq Fanani Joko Suwarno Rada Rasi Saputri Wilis Puspita Sari