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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal TEKNIK INFORMATIKA Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Annual Research Seminar Jurnal Pengabdian UntukMu NegeRI Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Applied Engineering and Technological Science (JAETS) Computer Science and Information Technologies Sriwijaya Journal of Informatics and Applications Jurnal Pendidikan dan Teknologi Indonesia Jurnal Abdi Masyarakat Indonesia Jurnal Pengabdian West Science Jurnal Abdimas Maduma Jurnal Elektronika dan Telekomunikasi
Hadipurnawan Satria
Department Of Informatics, Faculty Of Computer Science, Universitas Sriwijaya
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Malware Detection in Portable Document Format (PDF) Files with Byte Frequency Distribution (BFD) and Support Vector Machine (SVM) Heru Saputra; Deris Stiawan; Hadipurnawan Satria
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27559

Abstract

Portable Document Format (PDF) files as well as files in several other formats such as (.docx, .hwp and .jpg) are often used to conduct cyber attacks. According to VirusTotal, PDF ranks fourth among document files that are frequently used to spread malware in 2020. Malware detection is challenging partly because of its ability to stay hidden and adapt its own code and thus requiring new smarter methods to detect. Therefore, outdated detection and classification methods become less effective. Nowadays, one of such methods that can be used to detect PDF files infected with malware is a machine learning approach. In this research, the Support Vector Machine (SVM) algorithm was used to detect PDF malware because of its ability to process non-linear data, and in some studies, SVM produces the best accuracy. In the process, the file was converted into byte format and then presented in Byte Frequency Distribution (BFD). To reduce the dimensions of the features, the Sequential Forward Selection (SFS) method was used. After the features are selected, the next stage is SVM to train the model. The performance obtained using the proposed method was quite good, as evidenced by the accuracy obtained in this study, which was 99.11% with an F1 score of 99.65%. The contributions of this research are new approaches to detect PDF malware which is using BFD and SVM algorithm, and using SFS to perform feature selection with the purpose of improving model performance. To this end, this proposed system can be an alternative to detect PDF malware.
Tutorial Pembuatan Aplikasi Android untuk E-Commerce bagi Mahasiswa PGRI secara Luring dan Mahasiswa Sumsel secara Daring Assaidah, Assaidah; Satria, Hadipurnawan; Ariani, Menik; Saleh, Khairul; Satya, Octavianus Cakra; Kaban, Hadir; Jorena, Jorena
Jurnal Pengabdian UntukMu NegeRI Vol. 10 No. 1 (2026): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v10i1.11201

Abstract

Kegiatan workshop Pembuatan Aplikasi Android untuk E-Commerce telah dilakukan secara luring dan daring pada Hari Sabtu, Tanggal 15 November 2025. Tim pelaksana dari Jurusan Fisika FMIPA Universitas Sriwijaya disambut hangat oleh Dekan Fakultas Sains dan Teknologi Universitas PGRI sesaat sebelum pelatihan dimulai. Terdapat 30 orang mahasiswa PGRI yang hadir pada pelatihan ini. Dari daftar hadir peserta daring, dapat diketahui peserta merupakan mahasiswa yang berasal dari Kampus Universitas Sriwijaya sebanyak 38 orang, dari kampus PGRI sebanyak 21 orang dan dari Kampus Universitas Lembah Dempo sebanyak 2 orang. Kegiatan ini diharapkan dapat berlanjut lewat program komunitas RULIKOFI (Rumah Literasi Koding Fisika). Pelatihan ini berlanjut dalam bentuk pendampingan melalui grup Whatsapp yang dikelola oleh anggota RULIKOFI. Peserta dapat bertanya jika terdapat kendala dalam memahami materi pelatihan untuk dapat menghasilkan karya aplikasi Android yang menarik dan fungsional.
Clustering man in the middle attack on chain and graph-based blockchain in internet of things network using k-means Sari Nuzulastri; Deris Stiawan; Hadipurnawan Satria; Rahmat Budiarto
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p176-185

Abstract

Network security on internet of things (IoT) devices in the IoT development process may open rooms for hackers and other problems if not properly protected, particularly in the addition of internet connectivity to computing device systems that are interrelated in transferring data automatically over the network. This study implements network detection on IoT network security resembles security systems from man in the middle (MITM) attacks on blockchains. Security systems that exist on blockchains are decentralized and have peer to peer characteristics which are categorized into several parts based on the type of architecture that suits their use cases such as blockchain chain based and graph based. This study uses the principal component analysis (PCA) to extract features from the transaction data processing on the blockchain process and produces 9 features before the k-means algorithm with the elbow technique was used for classifying the types of MITM attacks on IoT networks and comparing the types of blockchain chain-based and graph-based architectures in the form of visualizations as well. Experimental results show 97.16% of normal data and 2.84% of MITM attack data were observed.
A novel embedded approach to face recognition using multi-threaded controller based on weightless neural network Ahmad Zarkasi; Hadipurnawan Satria; Anggina Primanita; Deris Stiawan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3903-3918

Abstract

This research presents a high efficiency embedded face recognition system based on the weightless neural network-face recognition algorithm (WNN-FRA) integrated with a multi-thread controller to enhance execution time and recognition accuracy under limited hardware resources. The system implements a center-scan feature alignment model to address resolution discrepancies between reference and input facial images. The multi-threaded architecture divides processing into three concurrent threads front, left, and right facial orientations each handling approximately 20 facial patterns. Experimental evaluation on a dataset of 60 facial images demonstrated a maximum recognition accuracy of 96.83% and an average execution time ranging from 16 to 34 milliseconds per dataset, confirming real-time performance. Comparative analysis shows that the multi-threaded approach reduced the execution time by over 67% compared to single-thread processing 0.09 second vs. 0.271 second, while maintaining balanced workload distribution across threads. Memory analysis revealed that the entire system required only 24 KB from the available 512 KB flash capacity, indicating efficient resource utilization. The results confirm that integrating WNN-FRA with multi-threading provides a robust, low-cost, and scalable solution for real-time facial pattern recognition in embedded environments.
Co-Authors Abdurahman Ahmad Nizar Ahmad Zarkasi Ahmad Zarkasi Anggina, Edith Anggun Islami Arnaldo, Muhammad Arnelawati Arnelawati Arnelawati, Arnelawati Assaidah Assaidah, Assaidah Deris Stiawan Dian Palupi Rini Egga Asoka Egga Asoka Asoka Ermatita - Fathoni Fathoni Hadir Kaban, Hadir Hasby Rifky Heru Saputra Indra Griha Tofik Isa Iskandar, Ananda Haykel Islami, Anggun Jambak, Muhammad Ihsan Jorena Jorena, Jorena Julian Supardi Julian Supardi Khairul Saleh M. Nazhif Rizani Mohammad Eko Fitrianto Muhammad Afif Muhammad Arnaldo Muhammad Fachrurrozi Muhammad Ichsan Hadjri Muhammad Naufal Rachmatullah Muhammad Qurhanul Rizqie Muhammad Qurhanul Rizqie Nugraha, Wibisena Octavianus Cakra Satya Osvari Arsalan Philip Alger M. Serrano Primanita, Anggina Rahmat Budiarto Richardo, M Denny Rifkie Primartha Rizki Kurniati Samsuryadi Sahmin Samsuryadi Samsuryadi Samsuryadi Samsuryadi Sari Nuzulastri Serrano, Philip Alger M. Siti Nurmaini Sony Oktapriandi Sutarno Sutarno Syahroyni, Muhammad Widya Aprilini Yos Karimudin