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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) AMMA : Jurnal Pengabdian Masyarakat
Indah Ratna Ningsih
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Edukasi Keamanan Digital dan Etika Bermedia Sosial bagi Remaja Sekolah Saeful Anwar; Tati Supra; Indah Ratna Ningsih; Kevin Salsabil Arlandy
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 04 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

In today's digital age, social media use has become an integral part of teenagers' lives. However, increased access to information and online communication also brings new challenges, especially in terms of digital safety and media etiquette. School adolescents, as active users of social media, often lack an adequate understanding of the risks that can arise from their digital activities. This study aims to provide a comprehensive understanding of the importance of digital safety and the application of ethics in social media among teenagers. The method used is a literature study combined with an educational approach based on learning and training modules. The results of the study show that most teenagers are not aware of the importance of personal data protection, password security, and the risk of spreading false information or hoaxes. In addition, the lack of ethics in communicating on social media can lead to social conflicts and privacy violations. Therefore, a systematic educational approach is needed to equip teenagers with the knowledge and skills to maintain digital security and act ethically in the digital space. This educational effort needs to involve the role of teachers, parents, and school policies that support digital literacy as a whole.
House Price Prediction Analysis Using a Comparison of Machine Learning Algorithms in the Jabodetabek Area Indah Ratna Ningsih; Ahmad Faqih; Ade Rizki Rinaldi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.733

Abstract

Jabodetabek, as the largest metropolitan area in Indonesia, has complex property price dynamics, making it difficult for developers and buyers to determine house prices. This study aims to analyze and compare the performance of the Multiple Linear Regression and Random Forest Regression algorithms in predicting house prices in the region. The data was obtained through scraping techniques from the rumah123.com website in October 2024, covering 999 data points with variables such as price, location, building area, land area, number of bedrooms, bathrooms, and garages. A comparative approach with cross-validation was applied to evaluate the performance of both algorithms using the metrics MAE, MSE, RMSE, MAPE, and R². The research results show that Random Forest Regression using GridsearchCV has better predictive performance, with an MAE value of Rp.645,764,815, MAPE of 28.12%, and R² of 0.864. The main factors influencing house prices in Jabodetabek include building size, land size, number of bedrooms, bathrooms, garages, and location. This finding emphasizes the superiority of Random Forest Regression in capturing complex data patterns and the significant role of these variables in determining house prices.
Co-Authors Ade Rizki Rinaldi Ahmad Faqih Kevin Salsabil Arlandy Saeful Anwar Tati Supra