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All Journal Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Share : Journal of Service Learning SPIRIT Journal of Technology and Informatics (JoTI) Jurnal Sistem Informasi Universitas Dinamika Tekmulogi: Jurnal Pengabdian Masyarakat Journal of Community Service (Abdisci) Aksi Kita: Jurnal Pengabdian Kepada Masyarakat
Slamet
Universitas Dinamika
Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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DESAIN ARSITEKTUR APLIKASI QR CODE SEBAGAI ANTI PHISHING SERANGAN QR CODE Slamet Slamet
SPIRIT Vol 15, No 1 (2023): Spirit
Publisher : LPPM ITB Yadika Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53567/spirit.v15i1.280

Abstract

QR Codes are very vulnerable to falsification because it is difficult to distinguish the original QR Code from a fake QR Code. Because of this vulnerability, the scanning process on fake QR Codes can direct users to dangerous sites with important information or data from the user. To assess QR Code security vulnerabilities and actions using a secure Application-based QR Code Architecture as Anti Phishing against QR Code attacks using hash functions and digital signatures. In experiments simulated attack types to malicious QR codes that redirect users to phishing sites. The real URL is disguised into the QR Code, where the user does not suspect, the URL is redirected to the fake site. As a result, intruders can easily use QR codes as vectors for phishing attacks targeted at smartphone users, even if they are using a browser that has security features. 
DIGITAL LITERACY EDUCATION FOR HIGH SCHOOL TEACHERS IN SATU GEDANGAN TO INCREASE INSIGHT AND MITIGATE HOAXES Alit Widana; Tutut Wurijanto; Teguh Sutanto; Slamet
Jurnal Abdisci Vol 3 No 3 (2026): Vol 3 No 3 Tahun 2026
Publisher : Ann Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62885/abdisci.v3i3.809

Abstract

Abstract Background. The development of internet technology and artificial intelligence (AI) technology has increased rapidly. By 2025 alone, hundreds of artificial intelligence (AI)-based technologies are expected to help complete human work faster and more efficiently. Based on statistics reported in the mass media, the amount of content uploaded increased by up to 200%. The problem is that the content may contain false information, also known as hoaxes. Aims. The purpose of this service is to enhance the insight of SMA Negeri Satu Gedangan teachers in digesting content in a more structured manner and avoid falling victim to hoax content. Methods. The method employed was a digital literacy workshop, accompanied by questionnaires administered at the beginning and end of the workshop. Result. The result of the digital literacy workshop was an increase in the ability of SMA Negeri Satu Gedangan teachers to digest and understand content on social media from an average of 60% to 100%. Implementation. The expected impact is that educators with good digital literacy skills can educate students more effectively and prevent them from becoming victims of hoaxes.
Peningkatan Literasi Internet of Things melalui Workshop Pemrograman micro:bit Berbasis Smart Home bagi Siswa SMA Negeri 1 Gedangan Sidoarjo Slamet Slamet; I.G.N. Alit W.P; Tutut Wurijanto; Teguh Sutanto
Aksi Kita: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 2 (2026): MARET-APRIL
Publisher : Indo Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/ktnjjc78

Abstract

Perkembangan Internet of Things (IoT) menuntut peningkatan literasi teknologi pada siswa sekolah menengah atas. Namun, pemahaman siswa terhadap konsep IoT dan perangkat pendukungnya masih terbatas. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan literasi IoT melalui workshop pemrograman berbasis micro:bit dengan implementasi smart home, sensor suhu, dan automation pada siswa SMA Negeri 1 Gedangan Sidoarjo. Kegiatan diikuti oleh 38 siswa dan dilaksanakan melalui metode pelatihan berbasis praktik yang meliputi pengenalan konsep IoT, pengenalan perangkat micro:bit, praktik pemrograman, serta pembuatan proyek sederhana. Evaluasi dilakukan menggunakan kuesioner pre-test dan post-test yang mencakup tiga aspek, yaitu pengetahuan dasar micro:bit, keterampilan teknis, serta sikap dan motivasi terhadap pembelajaran IoT. Hasil menunjukkan peningkatan signifikan pada pengetahuan dasar micro:bit, dimana siswa yang sebelumnya belum memahami konsep micro:bit menjadi memiliki pemahaman yang lebih baik setelah pelatihan. Pada aspek keterampilan teknis, siswa mampu mengoperasikan dan memprogram micro:bit secara mandiri melalui praktik langsung. Sementara itu, pada aspek sikap dan motivasi, perubahan tidak terlalu signifikan karena motivasi siswa terhadap IoT sudah tinggi sejak awal, meskipun terdapat peningkatan keyakinan bahwa pemrograman IoT dengan micro:bit mudah dan menyenangkan.  
Analysis of Backdoor Shells in Web Servers Using Splunk and SPL-Based Machine Learning Anjik Sukmaaji; Slamet; Sophie Anastasya Putri BR
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7215

Abstract

Backdoor shell attacks pose a critical threat to web server security, allowing attackers to bypass authentication and gain persistent, unauthorized control. Conventional signature-based detection methods often fail against these attacks due to their polymorphic and obfuscation techniques. To address this, we propose an integrated detection approach leveraging Splunk as a log management platform combined with Search Processing Language (SPL)-based machine learning (ML) models. This study collected and preprocessed web server log data using SPL queries, transforming it into structured features for classification. We evaluated two supervised learning algorithms, Logistic Regression and Random Forest, on a labeled dataset comprising both normal traffic and simulated backdoor shell attacks. The evaluation showed that while Logistic Regression achieved a solid performance with 93.5% accuracy and 87.8% recall, the Random Forest model significantly outperformed it. Random Forest reached an accuracy of 97.2%, with a precision of 95.8%, recall of 94.1%, and an F1-score of 94.9%. Crucially, it also reduced the false negative rate (FNR) to 2.3% and the false positive rate (FPR) to 3.5%, making it more reliable for real-time applications. Our findings demonstrate that Random Forest, when integrated with Splunk's SPL, provides a highly robust and practical detection mechanism that effectively distinguishes malicious activities. The primary contribution of this research is an end-to-end architecture that combines scalable log management, effective feature engineering, and advanced ML detection, offering a scalable and practical solution for enterprise-level security monitoring.
Co-Authors Alit Widana Anjik Sukmaaji Delfan Azhar Andhika Ferdiansyah Darmawan Heri Pratikno I Gusti Alit Widana P I.G.N. Alit W.P Izzeldin Ibrahim Mohamed Abdelaziz Ningsih, Norma Shiba, Sarah Hasna Siswo Martono Sophie Anastasya Putri BR Taniwijaya, Haryanto Teguh Sutanto Tutut Wurijanto Yoppy Mirza Maulana