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Khairunnisya, Aqilla
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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SENSOR SOIL MOISTURE UNTUK PENYIRAMAN TANAMAN DALAM MENGHADAPI VARIABILITAS CUACA Khairunnisya, Aqilla; Khairunnisa’; Merinda, Siska
JURNAL TELISKA Vol 17 No I: TELISKA Maret 2024
Publisher : Teknik Elektro Polsri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.10886526

Abstract

The instability of weather conditions marked by extreme temperature fluctuations and irregular rainfall patterns significantly impacts agriculture, causing issues of drought or excessive soil moisture. An automated system capable of continuously measuring soil moisture and providing precise plant irrigation is essential. However, energy efficiency remains a primary challenge. Innovations in automatic irrigation technology are crucial, focusing on developing systems efficient in soil moisture measurement and energy usage. Integrating advanced, energy-efficient sensors presents an intriguing solution to maintain precise performance while reducing power consumption. This research explores the design of soil moisture measurement tools, optimizing plant irrigation systems, and the functionalities of sensors. Sensor reliability is a primary concern due to its operation under diverse environmental conditions. The tool's development needs to consider energy efficiency limitations and its scalability with existing irrigation systems on a larger scale. The development of this tool aims to enhance agricultural responsiveness to weather changes by accurate soil moisture monitoring and automated irrigation. It seeks to improve crop quality while reducing water and energy wastage in agricultural management.
Sistem Deteksi URL Phishing Menggunakan Random Forest dan Gradient Boosting untuk Pencegahan Kejahatan Dunia Maya Khairunnisya, Aqilla; Lindawati, Lindawati; Zefi, Suzan
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 3 (2025): Oktober 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.3.2025.296-310

Abstract

Phishing attacks through malicious URLs have become a critical cybersecurity threat, resulting in substantial financial losses and data exposures on a global scale. Conventional approaches like blacklisting and rule-based detection often fall behind as phishing methods become more advanced, including zero-day phishing URLs. In this research, machine learning models based on Random Forest and Gradient Boosting are designed and tested to accurately identify phishing URLs. The dataset, obtained from Kaggle, consists of 11,430 URLs with extracted features representing URL characteristics such as length, subdomain count, HTTPS status, and domain age. The two models underwent training and validation with the help of stratified train-test splits and cross-validation techniques. To evaluate the models, several performance indicators—such as accuracy, precision, recall, F1-score, and ROC AUC—were applied. Results from the experiments reveal that Gradient Boosting slightly exceeds the performance of Random Forest, achieving an accuracy of 98.0%, precision of 98.1%, and an F1-score of 98.0%. The best-performing model was integrated into a web application built with Streamlit, providing real-time phishing detection for end-users. This research contributes to developing adaptive and efficient phishing URL detection systems, enhancing cybersecurity defenses against evolving phishing threats. The implementation demonstrates practical applicability and ease of use for non-expert users.
Co-Authors Khairunnisa’ Lindawati Lindawati Merinda, Siska suzan zefi