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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Meteorologi Klimatologi dan Geofisika
Muttaqin, Rofif Zainul
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Education Environmental Science Materials Science & Nanotechnology Mathematics Physics

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RANCANG BANGUN SISTEM PERINGATAN DINI BANJIR BERBASIS SENSOR ULTRASONIK DAN MIKROKONTROLER SEBAGAI UPAYA PENANGGULANGAN BANJIR Umari, Citra; Anggraini, Eci; Muttaqin, Rofif Zainul
Jurnal Meteorologi Klimatologi dan Geofisika Vol 4 No 2 (2017): Jurnal Meteorologi Klimatologi dan Geofisika
Publisher : Unit Penelitian dan Pengabdian Masyarakat Sekolah Tinggi Meteorologi Klimatologi dan Geofisika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (532.173 KB) | DOI: 10.36754/jmkg.v4i2.45

Abstract

Peristiwa banjir yang terjadi seringkali menimbulkan permasalahan yang dapat mengakibatkan kerugian yang tidak sedikit nilainya. Tidak adanya sistem peringatan dini saat bencana banjir membuat masyarakat menjadi kurang waspada. Pada penelitian ini dirancang sistem deteksi banjir yang bekerja secara otomatis dengan cara mengetahui ketinggian (level) permukaan air. Sistem pemantauan ketinggian permukaan air ini dilakukan dengan mengimplementasikan sensor ultrasonik berbasis mikrokontroler yang akan mengetahui ketinggian permukaan air yang dibuat pada level-level tertentu. Apabila ketinggian air mencapai batas tertentu sistem akan membunyikan buzzer yang akan memberikan peringatan kepada sekitarnya. Sistem ini terhubung dengan LCD yang akan menampilkan data ketinggian air dan ditampilkan secara realtime pada komputer. Dengan adanya peringatan tersebut, masyarakat dapat lebih waspada terhadap bencana banjir yang terjadi.
Design of Realtime Web Application Firewall on Deep Learning-Based to Improve Web Application Security Muttaqin, Rofif Zainul; Sudiana, Dodi
Jurnal Penelitian Pendidikan IPA Vol 10 No 12 (2024): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i12.8346

Abstract

Web applications are widely used nowadays, but comprises several vulnerabilities that are often used by attacker to exploit the system. There is web application firewall (WAF) that could mitigate these problem. WAF generally works based on pre-established rules. However, the weakness of this system is the evolving nature of attacks, and configuring rules on WAF requires in-depth knowledge related to existing applications. Artificial intelligence technology, both machine learning (ML) and deep learning (DL), shows good potential in recognizing types of attacks. In this research, a Real-time DL-based WAF was built to enhance security in web applications. Various ML and DL models were tested to perform the task of web attack detection, including Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). Based on the test results, the CNN-LSTM model achieved the highest performance, namely an accuracy of 98.61%, precision of 99%, recall of 98.08%, and f1-score of 98.54%. From the testing results with a web vulnerability scanner, the performance of the DL-based WAF is not inferior to ModSecurity WAF, which is used as a comparison. From the analysis results, it can be concluded that the implementation of DL-based WAF can improve the security of web applications.
Co-Authors Anggraini, Eci Dodi Sudiana Umari, Citra