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All Journal Tashdiq: Jurnal Kajian Agama dan Dakwah Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Nailah Azzahra
Unknown Affiliation
Religion Humanities Computer Science & IT Education Law, Crime, Criminology & Criminal Justice

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PERAN REMAJA MASJID AL-IQDAM DALAM KONSERVASI LINGKUNGAN BERBASIS NILAI-NILAI ISLAM Nailah Azzahra; Feby Rahayu; Salma Jauharotun; Muhammad Fiqri H.H; Abdul Fadhil
Tashdiq: Jurnal Kajian Agama dan Dakwah Vol. 9 No. 4 (2024): Tashdiq: Jurnal Kajian Agama dan Dakwah
Publisher : Cahaya Ilmu Bangsa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4236/tashdiq.v9i4.9349

Abstract

This article attempts to study the role of mosque youths in environmental conservation based on Islamic values. With the increasingly serious environmental problems, Islam directs people with morality and practice in the protection of nature. In this study, a quantitative approach is used to collect data through a survey with a questionnaire involving mosque youth from various regions. The results of the study reveal the potential role of mosque youth as agents of change in raising environmental awareness in activities involving greening moves and environmental education through mosques. Through data analysis, there has been a significant relationship between understanding of Islamic values and the level of participation among mosque youth in environmental conservation. This study recommends that the mosque programs on the environment are strengthened as a sustainable effort in supporting nature conservation.
Evaluasi Kinerja AI berbasis Recurrent Neural Network (RNN) dalam Mengidentifikasi Ancaman Phising pada URL Website Nailah Azzahra; Merry Dwi Handayani; Awwaliyah Aliyah
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 3 No. 3 (2025): Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v3i3.485

Abstract

Phishing is an evolving form of cybercrime that targets users' sensitive information through URL manipulation. Conventional detection methods such as blacklists and signature-based approaches have become increasingly inadequate in addressing the dynamic variations of modern phishing attacks. This study evaluates the effectiveness of Recurrent Neural Network (RNN) variants, such Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU), in detecting phishing threats based on URL data. The methodology involves a Systematic Literature Review (SLR) of scholarly publications from the past ten years, complemented by experimental implementation of the models using a public dataset from Kaggle. Literature findings show that Bi-LSTM consistently achieves the highest accuracy, up to 99%, while GRU stands out for its computational efficiency. Experimental results support these findings, with Bi-LSTM achieving an accuracy of 96.22%, GRU 96.29%, and LSTM 95.43%. Classification metrics indicate that RNN-based models perform very well in detecting benign and defacement URLs, although their performance on phishing URLs remains challenged, particularly in terms of recall. These results confirm that RNNs remain a promising approach for phishing detection systems, especially when integrated into hybrid models with complementary architectures. This study is expected to provide a foundation for developing precise and adaptive AI systems to combat increasingly sophisticated phishing threats.
Analisis Sentimen Twitter terhadap Tren Penyebaran Informasi Pelaku Kejahatan Menggunakan Algoritma Naives Bayes Awwaliyah Aliyah; Nailah Azzahra; Aliffia Isma Putri; Nur Aini Rakhmawati
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 2 (2024): Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i2.63

Abstract

In the rapidly developing digital era, social media such as Twitter has become part of everyday life and facilitates the rapid dissemination of information, including information about criminals. This research aims to analyze public sentiment towards information about criminals spread on Twitter using the Naive Bayes algorithm. This algorithm was chosen because of its simplicity and effectiveness in text classification. Data was collected through a crawling process from Twitter, followed by a preprocessing stage to remove noise. The research results show that public sentiment towards information about criminals on Twitter is divided into three categories: positive, neutral and negative. After classification, it was found that neutral sentiment increased significantly to 63.4%, while positive and negative sentiment decreased to 10.5% and 26.1%. These findings indicate that people tend to be more careful in reacting to sensitive information. This research provides important insights for related parties in managing information about criminals on social media and can be a reference for developing further policies and strategies.
Co-Authors Abdul Fadhil Aliffia Isma Putri Awwaliyah Aliyah Feby Rahayu Merry Dwi Handayani Muhammad Fiqri H.H Nur Aini Rakhmawati Salma Jauharotun