Article Per Year (5 Year)
p-Index From 2019 - 2024
1.607
P-Index
This Author published in this journals
All Journal Jurnal Manajemen dan Agribisnis Semantik Jurnal Informatika dan Teknik Elektro Terapan Journal of Information Systems Engineering and Business Intelligence Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Applied Technology and Computing Science Journal Community Development Journal JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Lailatul Hidayah
Universitas Internasional Semen Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
DBscan Algorithm and Decision Tree to Automate Trip Purpose Detection Lailatul Hidayah; Catur Wulandari
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 3, No 4, November 2018
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (449.762 KB) | DOI: 10.22219/kinetik.v3i4.672

Abstract

One of transportation research topic is detecting trip purpose. Given a collection of GPS mobility records, researchers endeavored to infer useful information such as trip, travel mode, and trip purpose. Obtaining these attributes will help researcher in transportation modelling.  This work proposed an approach in defining a trip or a trip segmentation which is a part of trip purpose problem as well as inferring the trip purpose. By Utilizing Dbscan clustering algorithm, decision tree, and some useful features, we are able to detect the trips and their purposes as well as building the model to automate the trip derivation.
DETECTION OF CYBERBULLYING USING SVM, NAIVE BAYES, AND RANDOM FOREST ALGORITHM Monica Fachrita Ruziqiana; Lailatul Hidayah; Mohammad Arif Rasyidi
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.5183

Abstract

Social media usage has been steadily increasing, with Instagram emerging as one of the most prominent platforms. As of January 2023, Instagram had 1.318 billion users, predominantly aged 18-24. While teenagers report enhanced self-confidence and diminished feelings of loneliness, social media also facilitates cyberbullying, impacting 35% of adolescents with low emotional well-being. This research seeks to develop a model for detecting cyberbullying in Instagram comments, classifying them into negative, positive, and neutral categories using SVM, Naïve Bayes, and Random Forest algorithms. The methodology encompasses data collection, preprocessing, text transformation via TF-IDF, and a comparative analysis. Grid search is employed to optimize algorithm parameters. Initial results indicated that Naïve Bayes and SVM achieved an accuracy of 75.47%, while Random Forest reached 69.88%. Following parameter tuning, SVM's accuracy improved to 97.79%, whereas Random Forest's decreased to 66.51%. The findings underscore the superior performance of SVM with parameter tuning in detecting Instagram cyberbullying.
Co-Authors Ahmad Khalil, Abdul Wahab Andayani, Puji Catur Wulandari Catur Wulandari Diana Purwitasari Doni Setio Pambudi Fawa'id, Muhamad Wildan Mardhiyyah, Yunita Siti Mohammad Arif Rasyidi Mohammad Arif Rasyidi Monica Fachrita Ruziqiana Muhammad Syauqi Mubarok Ngatini Rasyidi, Mohammad Arif Yuhana, Umi Laili