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All Journal PROSIDING SEMINAR NASIONAL Jurnal Simetris Kompak : Jurnal Ilmiah Komputerisasi Akuntansi E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Elkom: Jurnal Elektronika dan Komputer Jurnal Manajemen Informatika dan Sistem Informasi MEANS (Media Informasi Analisa dan Sistem) INFORMATIKA Jurnal Publikasi Ilmu Komputer dan Multimedia Jurnal Manajemen Informatika & Teknologi Jurnal Publikasi Teknik Informatika (JUPTI) Akuntansi: Jurnal Riset Ilmu Akuntansi Journal of Technology Informatics and Engineering VISA: Journal of Vision and Ideas Jurnal Ilmiah Sistem Informasi Switch: Jurnal Sains dan Teknologi Informasi
Arsito Ari Kuncoro
Universitas Sains Dan Teknologi Komputer
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences

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Journal : Journal of Technology Informatics and Engineering

 1 
ADVANCED MALICIOUS SOFTWARE DETECTION USING DNN Sulartopo Sulartopo; Dani Sasmoko; Zaenal Mustofa; Arsito Ari Kuncoro
Journal of Technology Informatics and Engineering Vol 1 No 1 (2022): April: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.144

Abstract

The special component of malicious software analysis is advanced malicious software analysis which implicates interested the main framework of malicious software that can be executed after executing it and aggressive malicious software investigation depend on inquisitive of the practice of malicious software after running it in a composed habitat. Advanced malicious software analysis is usually performed by contemporary anti-malicious software operating systems using signature-based analysis. The purpose of this research is to propose also decide a DNN for the progressive identification of portable files to study the features of portable executable malicious software to minimize the occurrence of distorted likeness when aware of advanced malicious software. The model proposed in this study is a NN with a Dropout model contrary to a resolution tree model to examine how well it performs in detecting real malicious PE files. Setup-skeptic methods are used to extract features from files. The dataset is used to train the proposed approach and measure outcomes by alternative common malicious software datasets. The results from this study illustrate that the use of simple DNNs to study PE vector elements is not only efficient but more fewer system comprehensive than the traditional interested disclosure approach. The model proposed in this study achieves an A-UC of ninety-nine point eight with ninety accurate specifics at one percent inaccurate specific on the R-OC curve. For shows that this model has the potential to complement or replace conventional anti-malicious software operating systems so for future research, it is proposed to implement this model practically.
Optimization of Inclusive Education Through the Implementation of Artificial Intelligence: Opportunities and Challenges Jamaludin, Haris; Hartono, Budi; Kuncoro, Arsito Ari
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.219

Abstract

Inclusive education is critical to ensuring equitable learning opportunities for all students, regardless of their abilities or backgrounds. This study aims to analyze the optimization of inclusive education by implementing artificial intelligence (AI), focusing on identifying the opportunities and challenges that arise. A systematic literature review was conducted as the research method, referencing five journals related to the application of AI in education and other relevant sectors. The findings reveal that AI has significant potential to enhance the quality of inclusive education by enabling personalized learning materials, real-time student data analysis, and improved teacher-student interactions. These advancements can help address diverse learning needs and promote a more inclusive learning environment. However, several challenges must be addressed, including technological disparities, limited infrastructure, and ethical concerns related to AI usage, such as data privacy and algorithmic bias. The study concludes that the successful implementation of AI in inclusive education requires collaborative efforts among governments, educational institutions, and other stakeholders to ensure accessibility, equity, and sustainability. Key recommendations include the development of supportive policies, enhancement of digital literacy among educators and students, and investment in technological infrastructure to bridge the digital divide. This research contributes to the growing discourse on the integration of AI in education, providing insights for policymakers and practitioners aiming to harness AI's potential for inclusive education.
ADVANCED MALICIOUS SOFTWARE DETECTION USING DNN Sulartopo Sulartopo; Dani Sasmoko; Zaenal Mustofa; Arsito Ari Kuncoro
Journal of Technology Informatics and Engineering Vol. 1 No. 1 (2022): April: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.144

Abstract

The special component of malicious software analysis is advanced malicious software analysis which implicates interested the main framework of malicious software that can be executed after executing it and aggressive malicious software investigation depend on inquisitive of the practice of malicious software after running it in a composed habitat. Advanced malicious software analysis is usually performed by contemporary anti-malicious software operating systems using signature-based analysis. The purpose of this research is to propose also decide a DNN for the progressive identification of portable files to study the features of portable executable malicious software to minimize the occurrence of distorted likeness when aware of advanced malicious software. The model proposed in this study is a NN with a Dropout model contrary to a resolution tree model to examine how well it performs in detecting real malicious PE files. Setup-skeptic methods are used to extract features from files. The dataset is used to train the proposed approach and measure outcomes by alternative common malicious software datasets. The results from this study illustrate that the use of simple DNNs to study PE vector elements is not only efficient but more fewer system comprehensive than the traditional interested disclosure approach. The model proposed in this study achieves an A-UC of ninety-nine point eight with ninety accurate specifics at one percent inaccurate specific on the R-OC curve. For shows that this model has the potential to complement or replace conventional anti-malicious software operating systems so for future research, it is proposed to implement this model practically.
Co-Authors Abd. Rasyid Syamsuri Abidin, Rohmat Alex Ristanto Pratama Ayyub H.B.N.MS Bagus Panuntun Budi Hartono budi hartono Budi Hartono Budi Santoso Dani Sasmoko Deni Candra Saputra Dwi Budi Santoso Edy Noriyanto Eko Siswanto Eko Siswanto Elisa Satyaningrum Elok Rosyidah Endrahadi Rahadian Febrianna Vecillia Sukamto Febryantahanuji Febryantahanuji Fendy Suhono Hadi Yusuf Hadi Yusuf Hendri Rasminto Iman Saufik Suasana Jamaludin, Haris Kasih Purwantini Khoirur Rozikin Khoirur Rozikin LANNI DWI SAPUTRI, LANNI DWI Lida Hartatik Lutmawati Lutmawati Maya Utami Dewi Maya Utami Dewi, Maya Utami Migunani Migunani Muhamad Zaenudin Muhamad Zaenudin Nugroho, Didik Setio Nuha, Anandeka Alfin Ulin Paulus Hartanto Prastyanto, Dicky Dwi Pratama, Bayu Rizki Pratama Rizky Aji Prasetyo Siswanto , Siswanto Somad, Mohamad Ali Sulartopo Sulartopo Syifa Ashafatun Tomara Indrajaya Toni Wijanarko Adi Putra Triyani Triyani Yussi Anggraini Zaenal Mustofa