Article Per Year (5 Year)
p-Index From 2021 - 2026
2.133
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA Sinkron : Jurnal dan Penelitian Teknik Informatika IJIES (International Journal of Innovation in Enterprise System) Jurnal Teknologi Dan Sistem Informasi Bisnis Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Data and Science Jurnal Teknik Informatika (JUTIF) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Ilmiah Teknik Informatika dan Komunikasi International Journal of Artificial Intelligence in Medical Issues KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Maie Istighosah
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health

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

Found 12 Documents
Search

 1   2 
Sentiment Analysis of Cyberbullying Against Children on Social Media: A Naïve Bayes Approach Fatin, Alfindian Kurnia; Adhitama, Rifki; Istighosah, Maie
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2858

Abstract

Cyberbullying involving children has become a major concern on social media, generating diverse public responses. This study aims to analyze public sentiment toward child cyberbullying cases on social media and to evaluate the effectiveness of data-balancing techniques in improving the performance of a Naïve Bayes (NB) classifier. The motivation for using NB lies in its computational efficiency, strong performance in text classification, and suitability for high-dimensional Term Frequency–Inverse Document Frequency (TF-IDF) features. A dataset consisting of 3,137 comments was collected from X/Twitter and processed through text preparation, lexicon-based sentiment labeling using Indonesian Sentiment Lexicon (InSet), and TF-IDF feature representation. Three balancing scenarios, namely Random Oversampling, Random Undersampling, and Non-Balancing, were compared by varying the proportion of training and testing samples across four split ratios: 90:10, 80:20, 70:30, and 60:40. The experimental results show that balancing techniques contribute to classification effectiveness, with Random Oversampling producing the best outcome. The highest accuracy of 86.36% was achieved with Random Oversampling and a 90:10 training-test split, outperforming Random Undersampling and Non-Balancing. Furthermore, the sentiment distribution indicates that negative sentiment dominates public responses to child cyberbullying incidents. 
Machine Learning Approaches with Random Forest and XGBoost for Sustainable Tourism Forecasting in Bali Destinations Nadia Nabila; Yohani Setiya Rafika Nur; Maie Istighosah
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5612

Abstract

Bali has experienced rapid tourism growth, reaching more than 6.3 million international visitors in 2024, which has increased the risk of overtourism and created challenges for sustainable destination management. Despite tourism being a major contributor to Bali’s economy, planning practices have not fully adopted data-driven prediction approaches, resulting in uncertainty in infrastructure development, service capacity, and resource allocation. This study aims to compare the performance of Random Forest and XGBoost algorithms in predicting the popularity of tourist destinations in Bali to support evidence-based decision-making. The research utilizes historical tourist visitation data from 2018 to 2023, obtained from the Bali Provincial Tourism Office. Data preprocessing includes data cleaning, normalization, feature encoding, and dimensionality reduction using Principal Component Analysis. Three data split schemes (80:20, 75:25, and 90:10) are evaluated. Model performance is assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that XGBoost outperforms Random Forest, achieving the best performance with a MAPE of 5.32% using the 90:10 data split. The selected model is then applied to project tourism demand for 2024–2026, indicating that nature-based and cultural tourism destinations remain dominant, particularly in Badung, Jembrana, and Gianyar Regencies. This study contributes to informatics by providing a machine-learning-based prediction model to support sustainable tourism management.
Co-Authors Abednego Dwi Septiadi Agatha Dinarah Sri Rumestri Ahmad Uffi Lestrasi Ma’ruf Amrullah, Arif Andi Sunyoto Angger Taufiqur Rohman Winarno Ariel Yonatan Alin Arif Riyandi Ariq Cahya Wardhana Cahyo Prihantoro Dany Candra Febrianto Diovianto Putra Rakhmadani Fatin, Alfindian Kurnia Liana Trihardianingsih Maryona Septiara Muhammad Ryandy Ghonim Asgar Nadia Nabila Rifki Adhitama, Rifki TONNY HIDAYAT Yohani Setiya Rafika Nur Yudha Islami Sulistya Yudha Islami Sulistya