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All Journal Foristek Jurnal Teknologi dan Manajemen Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) ScientiCO : Computer Science and Informatics Journal BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Algoritma The Indonesian Journal of Computer Science
Rizka Ardiansyah
Program Studi S1 Sistem Informasi, Fakultas Teknik, Universitas Tadulako, Indonesia
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
NETWORK'S ACCESS LOG CLASSIFICATION FOR DETECTING SQL INJECTION ATTACKS WITH THE LSTM ALGORITHM Hafriadi, Fajar Dzulnufrie; Ardiansyah, Rizka
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2157

Abstract

SQL Injection attacks are one of the popular web attacks. This attack is a network security problem focused on the application layer which is one of the causes of a large number of user data leaks. Currently available SQL detection techniques mostly rely on manually created features. Generally, the detection results of SQL Injection attacks depend on the accuracy of feature extraction, so they cannot overcome increasingly complex SQL Injection attacks on various systems. Responding to these problems, this research proposes a SQL Injection attack detection method using the long short term memory (LSTM) algorithm. The LSTM algorithm can learn data characteristics effectively and has strong advantages in sorting data so that it can handle massive, high-dimensional data. The research results show that the accuracy of the model approach created is able to recognize objects with a high accuracy value of 98% in identifying SQL Injection attacks.
APPLICATION OF VGG16 ARCHITECTURE IN WOOD TYPE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK Afiah, Nurul Anggun; Syahrullah, Syahrullah; Ardiansyah, Rizka; Laila, Rahmah; Pohontu, Rinianty
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.3874

Abstract

Wood is an important natural resource in construction and the furniture industry, with various types possessing unique characteristics. The selection of wood types is often done manually, which is prone to errors that can negatively impact the working process, product quality, and the sustainability of the forests that source the wood. Therefore, this research aims to improve classification accuracy through the application of technology. This study utilizes Convolutional Neural Network (CNN) with the VGG16 architecture to process images in analyzing the visual characteristics of wood, with the goal of building a model capable of classifying wood types based on images. The dataset used consists of 1,584 samples of wood images sourced from Kaggle. Four models were tested with variations in the training and validation data splits, as well as the use of Adam and Adamax optimizers, over 100 epochs. Model 1 achieved a training accuracy of 96.68% and a testing accuracy of 98.10%. Model 2, with a training accuracy of 99.47% and a testing accuracy of 98.41%, showed the best performance. Models 3 and 4 also yielded testing accuracies of 97.46% and 97.78%, respectively. The results of this study indicate that the application of CNN with the VGG16 architecture can enhance the effectiveness of wood type classification and contribute to more accurate and efficient wood selection practices.
TWITTER (X) SENTIMENT ANALYSIS OF KAMPUS MERDEKA PROGRAM USING SUPPORT VECTOR MACHINE ALGORITHM AND SELECTION FEATURE CHI-SQUARE Sari, Mutiara; Syahrullah, Syahrullah; Lapatta, Nouval Trezandy; Ardiansyah, Rizka
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2037

Abstract

Ministry of Education, Culture, Research and Technology (Kemendikbudristek) has implemented numerous policies aimed at enhancing the quality of education in the country. One of these policies is Kampus Merdeka program. The program includes various initiatives such as Teaching Campus, the Merdeka Student Exchange program, and Internship and Independent Study programs, which have gained significant popularity among students across Indonesia. However, the Kampus Merdeka program has drawn many pros and cons, with some parties supporting the initiative, but also many criticisms related to its implementation, which is considered not optimal in some educational institutions. Social media is where many of these opinions are voiced, one of the most widely used of which is twitter. In light of these circumstances, this study conducted a sentiment analysis of the independent campus program to assess public sentiment towards it. The dataset used in this research consisted of 500 tweets containing the keyword "kampus merdeka" with 250 tweets reflecting positive sentiment and 250 tweets reflecting negative sentiment. The results of the tests carried out obtained the highest increase in results in the 10:90 ratio, namely with an accuracy that increased by 14% from the previous 66% to 80%, precision also increased by 22% from the previous 67% to 89%, recall increased by 16% from the previous 58% to 79%, and the f1-score value which was previously 62% turned into 79% because it also increased by 17%.
Co-Authors ., Rezki Afiah, Nurul Anggun Ain, Moch. Zukhruf Amriana Andi Hendra Anita Ahmad Kasim Anita, Ayu Deny Wiria Nugraha Dwi Shinta Angreni Dwi Wijaya, Kadek Agus Dwiwijaya, Kadek Agus Hafriadi, Fajar Dzulnufrie Hajra Rasmita Ngemba Indrajaya, Muhammad Aristo Iskandar Iskandar JULIAN WITJAKSONO Komang, Gusti Lamasitudju, Chairunnisa Lesnusa, Gideon Namlea Maroso, Akwan Mohamad Irfan, Mohamad Mohammad Yazdi Pusadan Muhammad Akbar Mutiara Sari Ngurah, Gusti Nouval Trezandy Lapatta Parenrengi, Andi Fathur Alamsyah A. Pratiwi, Inten Sakti Putra, Ryan Adi Qofifa, Sitti Nurlaili Rahim, Mohammad Arham Rahmah Laila Raivandy, I Made Randhy Rini Septiani Rinianty, Rinianty Rumampuk, Viola Gracella Ryfial Azhar, Ryfial Sahril Sahril Septiano Anggun Pratama Sondakh, Clivent Gerhard Syahrullah Syahrullah Syaiful Hendra Therry, Alvin Christian Davidson Ulhak, Muhamad Zia Wirdayanti Yudhaswana, Yuri Yulianti, Indira Yuri Yudhaswana Joefrie Yusuf Anshori