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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Sistem dan Informatika Journal of Information Systems Engineering and Business Intelligence Jiko (Jurnal Informatika dan komputer) Jurnal ULTIMA InfoSys The IJICS (International Journal of Informatics and Computer Science) Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Cyber Security dan Forensik Digital (CSFD) JATI (Jurnal Mahasiswa Teknik Informatika) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Info Kripto JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Raden Budiarto Hadiprakoso
Politeknik Siber Dan Sandi Negara
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Mathematics Other

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Penerapan Gamifikasi dan Personal Extreme Programming pada Aplikasi Ensiklopedia terkait Secure Coding Berbasis Android I Komang Setia Buana; Nofrisal Dwi Syahputra; Nurul Qomariasih; Raden Budiarto Hadiprakoso
Jurnal Sistem dan Informatika (JSI) Vol 17 No 1 (2022): Jurnal Sistem dan Informatika (JSI)
Publisher : Direktorat Penelitian,Pengabdian Masyarakat dan HKI - Institut Teknologi dan Bisnis (ITB) STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/jsi.v17i1.483

Abstract

Dalam merancang sebuah aplikasi, masih banyak para pelajar/mahasiswa yang belum bisa membangun aplikasi yang aman. Seringkali, para pelajar/mahasiswa gagal dalam menerapkan standar-standar dalam keamanan aplikasi, sehingga membuat aplikasi rentan terhadap banyak serangan. Untuk mencegah hal tersebut, maka diperlukan suatu cara untuk meningkatkan pemahaman terkait keamanan dalam program, yaitu secure coding. Pada penelitian ini, dibangun sebuah aplikasi ensiklopedia yang menjelaskan tentang secure coding yang menerapkan gamifikasi demi meningkatkan pemahaman user terkait materi yang diberikan. Aplikasi ini diberi nama Secure-C dan dibangun dengan menerapkan Personal Extreme Programming. Berdasarkan hasil post-test yang disebar kepada responden, terdapat peningkatan pemahaman yang cukup signifikan setelah menggunakan aplikasi ini. Sehingga dapat disimpulkan bahwa aplikasi Secure-C dapat memberikan pemahaman lebih terkait secure coding.
PENGEMBANGAN APLIKASI REGISTRASI RAWAT JALAN PASIEN RSUD MENGGUNAKAN PERANGKAT ANDROID Hadiprakoso, Raden Budiarto
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 5 No. 2 (2021): JATI Vol. 5 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v5i2.3800

Abstract

In the last decade, technology has developed very rapidly, one of which is smartphones. The rapid development of smartphone technology has produced benefits for humans. These benefits include ease of access, efficiency, effectiveness, and convenience in carrying out daily activities. With this online doctor consultation application, patients do not need to go to the puskesmas or hospital when they are going to carry out a consultation. Especially in this COVID-19 pandemic, it will be very dangerous if the patient has to travel out of the house. This application is built on the android platform, using the android studio editor. The research method used is a model prototyping technique. Prototyping is development that is carried out quickly and tests prototypes of applications that are being developed and through interactions and iterative processes. By using the Java programming language on android studio and using the firebase realtime database. For the sample of observation data used is the Regional General Hospital (RSUD) Soedarso, Pontianak, West Kalimantan with an implementation time of June to July 2021. The result is a prototype version 1.0 has been developed with features in the form of patient registration, view doctor's schedule, queue messages and online doctor consultation
CodeGuardians: A Gamified Learning for Enhancing Secure Coding Practices with AI-Driven Feedback Hadiprakoso, Raden Budiarto; Sihombing, Rudolf Paris Parlindungan
ULTIMA InfoSys Vol 15 No 2 (2024): Ultima Infosys: Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v15i2.3858

Abstract

This paper introduces CodeGuardians, a gamified platform designed to improve secure coding practices using AI-driven, real-time feedback. The platform focuses on key secure coding concepts, such as input validation, authentication, session management, and cryptography. Developed using the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) instructional model, CodeGuardians enhances engagement and knowledge retention by incorporating interactive challenges. The AI component, powered by OpenAI, provides adaptive feedback on user-submitted code, helping users to learn secure coding practices more effectively. To assess its impact, a one-group pretest-posttest design was conducted. The results of a paired sample t-test showed a significant improvement in secure coding knowledge (t = 19.50, p = 0.048), confirming the platform’s effectiveness. In addition, the system’s usability was rated highly, with a score of 0.93 on the Computer System Usability Questionnaire (CSUQ), classifying it as "Very Good." The practical implications of this research suggest that CodeGuardians could be implemented in both educational and professional settings to enhance secure coding skills and reduce software vulnerabilities. From a theoretical standpoint, this study advances cybersecurity education by integrating AI-driven feedback into gamified learning environments. The research supports the theory that gamification improves engagement and learning retention, while also highlighting the value of adaptive technologies in addressing real-world security challenges. Future work will examine the long-term retention of knowledge and scalability across diverse learning environments.
Deteksi Serangan Spoofing Wajah Menggunakan Convolutional Neural Network Raden Budiarto Hadiprakoso; I Komang Setia Buana
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 3 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i3.4001

Abstract

Facial recognition-based biometric authentication is increasingly frequently employed. However, a facial recognition system should not only recognize an individual's face, but it should also be capable of detecting spoofing attempts using printed faces or digital photographs. There are now various methods for detecting spoofing, including blinking, lip movement, and head tilt detection. However, this approach has limitations when dealing with dynamic video spoofing assaults. On the other hand, these types of motion detection systems can diminish user comfort. As a result, this article presents a method for identifying facial spoofing attacks through Convolutional Neural Networks. The anti-spoofing technique is intended to be used in conjunction with deep learning models without using extra tools or equipment. Our CNN classification dataset can be derived from the NUAA photo imposter and CASIA v2. The collection contains numerous examples of facial spoofing, including those created with posters, masks, and smartphones. In the pre-processing stage, image augmentation is carried out with brightness adjustments and other filters so that the model to adapt to various environmental conditions. We evaluate the number of epochs, optimizer types, and the learning rate during the testing process. The test results show that the proposed model achieves an accuracy value of 91.23% and an F1 score of 92.01%.
Adaptive Multi‑Layer Framework for Detecting and Mitigating Prompt Injection Attacks in Large Language Models Hadiprakoso, Raden Budiarto; Wilujengning , Wiyar; Amiruddin, Amiruddin
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 3 (2025): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.3.473-487

Abstract

Background: Prompt injection attacks are methods that exploit the instruction‐following nature of fine‐tuned large language models (LLMs), leading to the execution of unintended or malicious commands. This vulnerability shows the limitation of traditional defenses, including static filters, keyword blocks, and multi‐LLMs cross‐checks, which lack semantic understanding or incur high latency and operational overhead. Objective: This study aimed to develop and evaluate a lightweight adaptive framework capable of detecting and neutralizing prompt injection attacks in real-time. Methods: Prompt-Shield Framework (PSF) was developed around a locally hosted Llama 3.2 API. This PSF integrated three modules, namely Context-Aware Parsing (CAP), Output Validation (OV), and Self-Feedback Loop (SFL), to pre-filter inputs, validate outputs, and iteratively refine detection rules. Subsequently, five scenarios were tested, comprising baseline (without any defenses), CAP only, OV only, CAP+OV, and CAP+OV+SFL. The evaluation was performed over a near-balanced dataset of 1,405 adversarial and 1,500 benign prompt, measuring classification performance through confusion matrices, precision, recall, and accuracy. Results: The results showed that baseline achieved 63.06% accuracy (precision = 0.678; recall = 0.450), while OV only improved performance to 79.28% (precision = 0.796; recall = 0.768). CAP reached 84.68% accuracy (precision = 0.891; recall = 0.779), while CAP+OV yielded 95.25% accuracy (precision = 0.938; recall = 0.966). Finally, integrating SFL over 10 epochs further improved performance to 97.83% accuracy (precision = 0.980; recall = 0.975) and reduced the false-negative count from 48 (CAP+OV) to 35 (CAP+OV+SFL). Conclusion: The results show the significance of using multiple defenses, such as contextual understanding, OV, and adaptive learning fusion, which are needed for efficient prompt injection mitigation. This shows that PSF framework is an effective solution to protect LLMs against advancing threats. Moreover, further studies should aim to refine the adaptive thresholds in CAP and OV, particularly in multilingual or highly specialized environments, and examine other forms of SFL solutions for better efficiency.  Keywords: Prompt Injection, LLMs Security, Jailbreak, Natural Language Processing
Double Face Masks Detection Using Region-Based Convolutional Neural Network Carita, Sa'aadah Sajjana; Hadiprakoso, Raden Budiarto
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.v9i4.23902

Abstract

Because of the fast spread of coronavirus, the globe is facing a significant health disaster of COVID-19. The World Health Organization (WHO) released many suggestions to combat the spread of coronavirus. Wearing a face mask in public places and congested locations is one of the most effective preventive practices against COVID-19. However, according to recent research wearing double face masker even provide better protection than just one mask. Based on this finding, various public places require double masks to proceed more. It is pretty tricky to monitor individuals in crowded public places personally. Therefore, a deep learning model is suggested in this paper to automate recognizing persons who are not wearing double face masks. A faster region-based convolutional neural network model is developed using the picture augmentation approach and deep transfer learning to increase overall performance. We apply deep transfer learning by fine-tuning the low level pre-trained Visual Geometry Group (VGG) Face2 model. This study used the publicly accessible VGGFace2 dataset and the self-processed dataset. The findings in this study show that deep transfer learning and image augmentation can increase detection accuracy by up to 11%. Consequently, the created model achieves 93.48% accuracy and 93.19% F1 score on the validation dataset, demonstrating its excellent performance. The test results show the proposed model for further research by adding the predicted dataset and class.
Implementasi Single Sign-On (SSO) dengan Pendekatan Alaca's Framework untuk Peningkatan Keamanan Layanan Web Terintegrasi Wicaksono, Herlambang Rafli; Kabetta, Herman; Hadiprakoso, Raden Budiarto; Qomariasih, Nurul
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 11, No 1 (2025): Volume 11 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v11i1.81743

Abstract

Disintegrasi beberapa layanan yang menyangkut pengguna yang sama dapat membuat proses autentikasi menjadi redundan dan mengurangi kenyamanan pengguna. Single Sign On (SSO) membuat seorang pengguna bisa menggunakan sebuah kredensial untuk mengakses beberapa layanan dengan identitas yang sama. Sistem SSO dapat membuat pengguna melakukan satu proses autentikasi untuk mengakses beberapa layanan dan sumber daya. Penelitian ini bertujuan untuk mengatasi tantangan integrasi layanan web yang sudah ada menggunakan SSO untuk menghilangkan redundansi. Sebuah aplikasi SSO baru dirancang dan diimplementasikan untuk mengintegrasikan layanan-layanan tersebut dengan memenuhi persyaratan fungsional dan non-fungsional, termasuk kebijakan keamanan yang ketat. Dengan menggunakan Alaca's SSO System Evaluation Framework, manfaat dari aplikasi ini dievaluasi, menunjukkan keunggulan dibandingkan skema OpenID Connect, terutama dalam memberikan jaminan sinyal kepada Penyedia Layanan.
CodeGuardians: A Gamified Learning for Enhancing Secure Coding Practices with AI-Driven Feedback Hadiprakoso, Raden Budiarto; Sihombing, Rudolf Paris Parlindungan
ULTIMA InfoSys Vol 15 No 2 (2024): Ultima Infosys: Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v15i2.3858

Abstract

This paper introduces CodeGuardians, a gamified platform designed to improve secure coding practices using AI-driven, real-time feedback. The platform focuses on key secure coding concepts, such as input validation, authentication, session management, and cryptography. Developed using the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) instructional model, CodeGuardians enhances engagement and knowledge retention by incorporating interactive challenges. The AI component, powered by OpenAI, provides adaptive feedback on user-submitted code, helping users to learn secure coding practices more effectively. To assess its impact, a one-group pretest-posttest design was conducted. The results of a paired sample t-test showed a significant improvement in secure coding knowledge (t = 19.50, p = 0.048), confirming the platform's effectiveness. In addition, the system's usability was rated highly, with a score of 0.93 on the Computer System Usability Questionnaire (CSUQ), classifying it as "Very Good." The practical implications of this research suggest that CodeGuardians could be implemented in both educational and professional settings to enhance secure coding skills and reduce software vulnerabilities. From a theoretical standpoint, this study advances cybersecurity education by integrating AI-driven feedback into gamified learning environments. The research supports the theory that gamification improves engagement and learning retention, while also highlighting the value of adaptive technologies in addressing real-world security challenges. Future work will examine the long-term retention of knowledge and scalability across diverse learning environments.
Co-Authors Amiruddin Amiruddin Asyrafi Adnil Ma'ali Aulia Tri Rahmawati Carita, Sa'aadah Sajjana Febriora Nevia Pramitha Girinoto Herman Kabetta Hermawan Setiawan I Komang Setia Buana Buana I Komang Setia Buana, I Komang Ikhsan Gustya Firmansyah Muhammad Novrizal Ghiffari Nofrisal Dwi Syahputra Nur Annisa Kadarwati Febriyani Nurul Qomariasih Qomariasih, Nurul Ray Novita Yasa Ray Novita Yasa Sihombing, Rudolf Paris Parlindungan Wahyu Rendra Aditya Wicaksono, Herlambang Rafli Wilujengning , Wiyar Wina Agus Satria