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All Journal Dinamik Teknika Jupiter PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business International conference on Information Technology and Business (ICITB) Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) International Journal of Artificial Intelligence Research Jurnal CoreIT Prosiding Seminar Nasional Darmajaya Jurnal Sinergitas PkM & CSR Jurnal Teknologi Informasi MURA Jurnal Informasi dan Komputer IJISCS (International Journal Of Information System and Computer Science) Jurnal Tekno Kompak Building of Informatics, Technology and Science JPGMI (Jurnal Pendidikan Guru Madrasah Ibtidaiyah Al-Multazam) Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian kepada Masyarakat Jurnal Sains Teknologi dan Sistem Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Mandiri NEAR: Jurnal Pengabdian kepada Masyarakat SIENNA Jurnal Indonesia Sosial Sains Jurnal Ilmu Komputer, Sistem Informasi, Teknik Informatika (JILKOMSITI) Jurnal Ilmiah ESAI Jurnal Teknologi Informasi Mura Scientica: Jurnal Ilmiah Sains dan Teknologi Darma Diksani: Jurnal Pengabdian Ilmu Pendidikan, Sosial, dan Humaniora International Journal of Computer Technology and Science Journal of Software Engineering And Technology IJISCS (International Journal of Information System and Computer Science)
Sutedi Sutedi
IIB Darmajaya
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Journal : International Journal of Artificial Intelligence Research

 1 
Detection of SQL Injection Attack Using Machine Learning Based On Natural Language Processing Triloka, Joko; Hartono, Hartono; Sutedi, Sutedi
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (673.401 KB) | DOI: 10.29099/ijair.v6i2.355

Abstract

There has been a significant increase in the number of cyberattacks. This is not only happening in Indonesia, but also in many countries.  Thus, the issue of cyber attacks should receive attention and be interesting to study.  Regarding the explored security vulnerabilities, the Open Web Application Security Project has published the Top-10 website vulnerabilities. SQL Injection is still become one of the website vulnerabiliteis which is often exploited by attacker. This research has implemented and tested five algorithms. They are Naïve Bayes, Logistic Regression, Gradient Boosting, K-Nearest Neighbor, and Support Vector Machine. In addition, this study also uses natural language processing to increase the level of detection accuracy, as a part of text processing. Therefore, the main dataset was converted to corpus to make it easier to be analyzed. This process was carried out on feature enginering stage. This study used two datasets of SQL Injection. The first dataset was used to train the classifier, and the second dataset was used to test the performance of classifier. Based on the tests that have been carried out, the Support Vector Machine get the highest level of accuracy detection. The accuracy of detection is 0.9977 with 0,00100 micro seconds per query time of process. In performance testing, Support Vector Machine classifier can detect 99,37% of second dataset. Not only Support Vector Machine, the study have also revealed the detection accuracy level of further tested algorithms: K-Nearest Neighbor (0,9970), Logistic Refression (0,9960), Gradient Boosting (0,99477), and Naïve Bayes (0,9754).
Brain Tumor Detection On Magnetic Resonance Imaging Using Deep Neural Network Sutedi, Sutedi; Maulana, Muh Royan Fauzi; Agarina, Melda; Karim, Arman Suryadi; Nurfiana, Nurfiana
International Journal of Artificial Intelligence Research Vol 7, No 1.1 (2023)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i2.1061

Abstract

Cancer is a heterogeneous disease that can attack all parts of the body. Cancer is caused by the abnormal and uncontrolled growth of body cells, resulting in damage to body tissue and the potential to cause death. Cancer is a type of malignant tumor that attacks the body, one of which is the brain. Every year there are 300 brain tumor patients in Indonesia, both adults and children. Generally, doctors use two methods to diagnose these tumors, namely: biopsy and magnetic resonance imaging (MRI). Although the use of biopsy is quite accurate in identifying brain tumors, the time required is relatively long, reaching 15 days. While using MRI is relatively fast, the resulting accuracy is low and depends on the experience of medical personnel. This research aims to develop a method for diagnosing brain tumors using MRI images to make it faster and more accurate. The method used in this research was a deep neural network with a convolutional neural network (CNN) architecture layer. This method was chosen because deep learning has the advantage of pattern recognition with a high level of accuracy and is directly proportional to the number of datasets. This study used a dataset of 300 MRI images with two-dimensional (2D) axial imaging. The metrics used as a basis for the performance of the deep neural network model are accuracy, sensitivity, specificity, precision, and dice similarity coefficient with the results of each metric, namely: 99.3%, 98.6%, 98%, 98%, 98.3%. The research results showed that a deep neural network can speed up the diagnosis of brain tumors with high accuracy within 0.2 seconds.Â
Co-Authors - Nurfiana Abda Abda Absor, M Ulil Adam Japal Adela, Cindy Nada Agariana, Melda Agarina , Melda Agung Nugroho Menggala Putra AGUS PURWANTO Anton Catur Atmoko Ardiansyah, Ari ARIF HIDAYAT Arman Suryadi Karim, Arman Suryadi DANU WIJAYA Diba Madya Margerefti Dona Yuliawati Edi Edi Pranyoto Fauzi Maulana, Muh Royan Fauzi, M.Royan Firdhayanti, Ayu Firmansyah Firmansyah Firmansyah Yunialfi Alfian Guntur Guntur Halimah Halimah handoyo widi nugroho Hartono Hartono Hasibuan, Said Hendra Kurniawan Hendri Purnomo Hendri Purnomo Hermanto, Muhammad Haris Ilhamiharja, Arbi Irianti , Artia Kaesar Azra Putra Zeva Kesuma, Martika Kurniawan, Putra Lestari, Mentari Puji Luh Putu Ratna Sundari M Said Hasibuan M Ulil Absor M. Royan Fauzi M.Ariza M.Royan Fauzi M.Royan Fauzi Maulana Maarif, Syaiful Mahfudzin, Abdul Halim Maulana, Muh Royan Fauzi Melda Agarina MS Hasibuan MUBAAROK, ABDURRAHMAN Muhamad Ariza Eka Yusendra Muhammad Ariza Eka Yusendra Nirma Nirma Nirmala Handayani Nirmala Handayani Ochi Marshella Febriani rinaldi rinaldi Rinaldo, Reyhandoni Rini Nurlistiani Risa Ayuna Sari, Erlinda Ratna Sri Karnila Sriyanto . Sriyanto, Sriyanto Stefanus Rumangkit Suryadi , Arman Susanti Susanti Susanti Susanti Susanti Tria Devi Miranti Triloka, Joko Ubaidilah, Romdan Muhamad Wasilah Wasilah Yusminar Yusminar