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All Journal Bulletin of Computer Science Research AMMA : Jurnal Pengabdian Masyarakat
Sri Rahayu, Eka
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Humanities Chemistry Computer Science & IT Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Sosialisasi Teknologi Keamanan Digital: Strategi Menghindari Ancaman Siber bagi Pelajar Di SMPN 2 Tangerang Selatan Sri Rahayu, Eka; Masayu Lintang, Ananda; Rayhan Sanjaya, Bayu; Stiady Syah, Farhan; Ashari, Idpan; Sofian, Kurnain; Arfiola Suci, Meta; Abdul Sahid, Rahmat; Dwi Irawan, Ryan; Aziz, Tanzilal; Salsabila Putri, Firda
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 11 : Desember (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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An This community service activity in the field of digital security technology aims to raise awareness and understanding among middle school students about the importance of protecting themselves from cyber threats. In an increasingly digital world, cyberattacks have become a real threat that can damage personal data, steal important information, or even cause financial losses. Unfortunately, many students lack sufficient knowledge about preventive measures against cyberattacks, making them more vulnerable to such risks. Therefore, this activity aims to provide an understanding of how to avoid cyberattacks through socialization, training, and simulations that can be applied in daily life. The steps taught include using strong passwords, managing privacy settings on social media, being cautious of suspicious emails and links, as well as the importance of security tools like antivirus software and firewalls. It is expected that through this activity, middle school students will be able to reduce the risk of falling victim to cyberattacks and become more cautious in using digital technology. The expected outcome of this community service activity is to enhance students' knowledge and skills in safeguarding personal data, as well as positively impacting safer digital behaviors.
Klasifikasi Penyakit Jamur Pada Tanaman Tomat dengan Algoritma SVM Sri Rahayu, Eka; Anugrah Ade Purnama, Oktaviana; Zakaria, Hadi; Rosyani, Perani
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.515

Abstract

Diseases in tomato plants, such as mosaic virus and yellow leaf curl virus, can significantly reduce crop yields. Therefore, early detection based on artificial intelligence (AI) presents a strategic solution to improve the efficiency of plant disease identification. This study aims to develop and evaluate a classification model using Support Vector Machine (SVM) for the automatic and accurate detection of tomato leaf diseases. SVM is selected as the primary classification method due to its ability to handle high-dimensional data with better computational efficiency compared to Convolutional Neural Network (CNN) and Random Forest. The dataset used is the PlantVillage Tomato Leaf Dataset from Kaggle, consisting of 600 images categorized into three classes: healthy tomato leaves, leaves affected by mosaic virus, and leaves affected by yellow leaf curl virus. The research stages include data preprocessing such as image normalization, dataset splitting (80% training, 20% testing), and undersampling to address class imbalance. The SVM model is trained using various kernels and evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the SVM model achieves an accuracy of 98.33%, demonstrating its effectiveness in detecting tomato plant diseases. Therefore, this model can be implemented in smart agriculture systems to enhance early disease detection and assist farmers in optimizing crop yields.
Co-Authors Abdul Sahid, Rahmat Anugrah Ade Purnama, Oktaviana Arfiola Suci, Meta Ashari, Idpan Aziz, Tanzilal Dwi Irawan, Ryan Masayu Lintang, Ananda Perani Rosyani Rayhan Sanjaya, Bayu Salsabila Putri, Firda Sofian, Kurnain Stiady Syah, Farhan Zakaria, Hadi