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All Journal Jurnal Simetris Nuansa Informatika Jurnal Teknologi Informasi dan Ilmu Komputer POSITIF Jurnal Ilmiah Matrik Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Informatika Global JUSIM (Jurnal Sistem Informasi Musirawas) Journal of Information Systems and Informatics Teknomatika (Jurnal Teknologi dan Informatika) Jurnal Ilmu Sosial, Manajemen, Akuntansi dan Bisnis Jurnal Nasional Ilmu Komputer Journal of Applied Data Sciences Jurnal Nasional Manajemen Pemasaran dan SDM Jurnal Nasional Pengabdian Masyarakat AKM: Aksi Kepada Masyarakat Jurnal Sistem Informasi Komputer dan Teknologi Informasi
Antoni, Darius
Universitas Bina Darma
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Social Sciences Other

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Journal : Journal of Information Systems and Informatics

 1 
Machine Learning Models for DDoS Detection in Software-Defined Networking: A Comparative Analysis Ferdiansyah, Ferdiansyah; Antoni, Darius; Valdo, Muhammad; Mikko, Mikko; Mukmin, Chairul; Ependi, Usman
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.864

Abstract

In today's digital age, Software-Defined Networking (SDN) has become a pivotal technology that improves network control and flexibility. Despite its advantages, the centralized nature of SDN also makes it susceptible to threats such as Distributed Denial of Service (DDoS) attacks. This study compares the effectiveness of three machine learning models Random Forest, Naive Bayes, and Linear Support Vector Classification (LinearSVC) using the 'DDoS SDN dataset' from Kaggle, which contains 104,345 records and 23 features. An equal 70/30 ratio was used on model. The models were then assessed using measures such as accuracy, precision, recall, and F1-score, and ROC curves. Among the models, Random Forest outperformed the others with a 97% accuracy, precision values of 1.00 (benign traffic) and 0.94 (malicious traffic), and an ROC AUC score of 1.00. In contrast, Naive Bayes and LinearSVC recorded lower accuracies of 63% and 66%, respectively. These findings underscore Random Forest's effectiveness in detecting DDoS attacks within SDN environments.
Development of a Digital Village Concept based on Information Technology Infrastructure and Strategy Management to Facilitate SPBE Ogan Ilir Regency Oktarina, Serly; Roseno, Muhammad Taufik; Ubaidillah, Ubaidillah; Antoni, Darius; Zahro, Lailatuz; Syaputra, Hadi
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.875

Abstract

Technology in the digital era is currently progressing very rapidly. This is marked by the increasingly massive number of social media users in everyday life. Survey results from the Indonesian Internet Service Providers Association (APJII) in 2022 recorded that the number of internet users in Indonesia reached 196.7 million people. This number increased by 23.5 million or 8.9% compared to 2018. With information technology and the internet, information is now becoming more easily spread and can be accessed by all levels of society thanks to the internet, not just people in urban areas, but people living there. in rural areas too. The Ogan Ilir Regency Government initiated information technology infrastructure including village internet or Digital Village to solve the problem of inequality in digitalization of society in rural and urban areas. Development and implementation of a digital village is a program that implements electronic-based government system (SPBE) services to the community and empowers the community based on the use of technology. This research aims to conduct a survey of information technology infrastructure to identify village potential, marketing and accelerating access and public services. Apart from that, this research also identifies digital-based life patterns of people in rural and urban areas, as well as to advance economic development in rural areas to improve SPBE services in Ogan Ilir Regency. The method used is a quantitative method for surveying and mapping the use of information technology in villages and ultimately producing the concept of an independent digital village. Research data was obtained from surveys and FGDs with the Ogan Ilir district government, village heads, village communities and micro, small and medium enterprises. Meanwhile, secondary data will be obtained through the results of MSME and village profiles from the Central Statistics Agency.
Performance Analysis of Convolutional Neural Network in Pempek Food Image Classification with MobileNetV2 and GoogLeNet Architecture Pratomo, Yudha; Roseno, Muhammad Taufik; Syaputra, Hadi; Antoni, Darius
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1026

Abstract

This research develops a pempek food image classification system using two Deep Learning architectures, namely MobileNetV2 and GoogLeNet. The dataset consists of five types of pempek with a total of 446 images, which are divided for training (70%), validation (15%), and testing (15%). The model was evaluated based on accuracy, precision, recall, and F1-score. The results showed that GoogLeNet achieved a validation accuracy of 96.21%, higher than MobileNetV2 which was only 70.58%. GoogLeNet is also more stable in convergence and more accurate in recognizing different types of pempek. This research shows that GoogLeNet is more optimal for pempek classification. In the future, this research can be extended by adding more datasets, exploring more sophisticated models, and developing mobile or web-based applications.
Co-Authors ., Janurita Ahmad Taufik Akbar, Muhamad Ali, Nazlena Mohamad Amelia, Putri Rizky ARDIANSYAH ARDIANSYAH Arif Fadillah Azim, Choirul Bitrayoga, Muhammad Coyanda, John Roni Damayanti, Nita Rosa Dedy Syamsuar Diana Diana Faradillah Fatoni, Fatoni Fatoni, Fatoni Febrianty, Istiqomah Ferdiansyah Ferdiansyah Firdaus Firdaus Fitriana Fitriana Harahap, Zakaria Heni Pujiastuti Herdiansyah, M Izman Hermizahadiwidastra, Hermizahadiwidastra Heryati, Agustina Imelda Saluza, Imelda Indah, Sylvia Uly Jaka Purnama Julizar, Salshabila Pratami Putri Kartina, Riza Kurniawan Kurniawan Kurniawan, Tri Basuki Lestari, Indri Endang Makkiyah, Feda Anisah Maria Veronica Maryani Maryani Merdasari, Iin Mikko, Mikko Mirza, A Haidar Muhamad Akbar Muhammad Taufik Roseno, Muhammad Taufik Mukmin, Chairul Nurmayanti, Nurmayanti Oktala, Rosananda Oktarina, Serly Putra, Erlangga Kesuma Rahmi Rahmi Retnawiyati, Eka Rosita, Dewi RR. Ella Evrita Hestiandari Samsuddin, Harun Saputra, Benny Wilson Sriyeni, Yesi Suhartini Suhartini Syaputra, Hadi Taufik Jamaan Taufiqurrahman Taufiqurrahman Terttiaavini, Terttiaavini Ubaidillah Ubaidillah Usman Ependi Valdo, Muhammad Widya Cholil Yudha Pratomo Zahro, Lailatuz