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All Journal Jurnal Teliska Building of Informatics, Technology and Science
Merinda, Siska
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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.
Perbandingan Algoritma Support Vector Machine dan Bi-Directional Long Short Term Memory Dalam Mengklasifikasi Berita Hoaks Merinda, Siska; Ciksadan, Ciksadan; Fadhli, Mohammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7391

Abstract

The rapid advancement of digital technology has made it easier to spread information widely and quickly. However, this ease of access has also contributed to the rise of false or misleading news, commonly known as hoaxes, which can confuse the public. This study aims to evaluate and compare the performance of two machine learning algorithms, namely Support Vector Machine (SVM) and Bi-Directional Long Short Term Memory (BiLSTM), in classifying hoax news written in Indonesian. The research adopts a supervised learning approach, where models are trained using pre-labeled data categorized as either hoax or non-hoax. The process begins with collecting data from trusted sources, followed by several preprocessing steps, including text cleaning, tokenization, stopword removal, and stemming. After preprocessing, the dataset is split into training and testing sets in an 80:20 ratio. The results show that the SVM model achieved an accuracy of 98.46%, with 98% precision and 99% recall for the non-hoax category. In comparison, the BiLSTM model performed better, reaching 99% accuracy, with both precision and recall at 99% for both categories. These findings indicate that BiLSTM is more effective at capturing linguistic context and identifying patterns in hoax-related content. Additionally, the models were implemented into a web-based system to assess their real-world detection capabilities.
Co-Authors Ciksadan, Ciksadan Fadhli, Mohammad Khairunnisa’ Khairunnisya, Aqilla