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All Journal Applied Information System and Management Nusantara Hasana Journal
Runal Rezkiawan
Universitas Muhammadiyah Makassar
Agriculture, Biological Sciences & Forestry Humanities Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Nursing

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PENGEMBANGAN GAME EDUKASI AR DENGAN NPC GURU VIRTUAL BERBASIS MEDIAPIPE DAN AI VOICE INTERACTION Muh. Hasrul; Fahrim Irhamna Rachman; Runal Rezkiawan
Nusantara Hasana Journal Vol. 6 No. 3 (2026): Nusantara Hasana Journal, August 2026
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v6i3.2328

Abstract

The development of interactive learning media has been supported by the integration of Augmented Reality (AR), Artificial Intelligence (AI), and voice interaction technologies. However, learning applications that integrate AR with virtual teacher characters and multiple forms of interaction are still challenging to implement, particularly in terms of system integration and device limitations. This study aims to develop an educational game based on Augmented Reality with a Virtual Teacher NPC that integrates AI, voice interaction, ML Kit Optical Character Recognition (OCR), MediaPipe, chat, and quiz features. The application was developed using Unity and AR Foundation, with Google Gemini used to generate AI responses. Voice interaction was implemented through Speech-to-Text (STT) and Text-to-Speech (TTS), while ML Kit OCR was used to recognize text from scanned learning materials. MediaPipe was implemented to process visual information from the user's face as a supporting component for NPC interaction. The system was evaluated using Black Box Testing, System Usability Scale (SUS), and User Acceptance Testing (UAT). The results of Black Box Testing showed that most of the main functions operated according to the designed scenarios, although several functions were partially successful due to OCR image quality and MediaPipe implementation limitations. Testing with 20 respondents produced an average SUS score of 63.88, while UAT produced an average score of 3.58 out of 5 or 71.61%. These results indicate that the developed application can function as an interactive educational media, although improvements are still required in usability, OCR performance, and MediaPipe implementation.
A Hybrid Random Forest–Support Vector Machine Model for Classifying Cyberattacks Using SSH Honeypot Log Data Firrofiqil A'la Kadram; Muhammad Faisal; Runal Rezkiawan; Aedah Abd Rahman; Andi Makbul Syamsuri; Muhammad Syafaat S. Kuba
Applied Information System and Management (AISM) Vol. 9 No. 2 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i2.51928

Abstract

Secure Shell (SSH) is widely used for remote server administration; however, its exposure to public networks makes it a frequent target of cyberattack activities. This article presents an attack classification framework for Cowrie SSH Honeypot logs, using a hybrid Random Forest–Support Vector Machine approach. The raw logs were processed through log auditing, data cleaning, session reconstruction, class labeling, feature construction, feature selection, and model evaluation. The dataset consisted of 38 log files, 213,494 valid records, 29,099 unique sessions, and 1,618 unique source IP addresses. The analyzed attack categories included failed authentication, other suspicious interactions, successful compromise, and command execution. Random Forest ranked ten candidate behavioral features, selected the five highest-ranked features within each rolling fold, and generated time-aware out-of-fold class-probability estimates. The selected features and RF probability outputs were combined, standardized, and passed to a support vector machine with a radial basis function (RBF) kernel as the stage-1 classifier; sessions predicted as successful interactions were then refined into successful compromise or command execution based on observed command activity. Evaluation using eight rolling time-based folds achieved an accuracy of 99.65% ± 0.38%, a balanced accuracy of 97.02% ± 5.77%, and a macro F1-score of 97.84% ± 4.37% for the final four-class classification. The event_count, username_diversity, interarrival_std, and password_diversity features were selected in all folds. Theoretically, RF–SVM provides a stable session-level representation; in practice, it supports honeypot log triage and the prioritization of higher-risk SSH sessions.
Co-Authors Aedah Abd Rahman Andi Makbul Syamsuri Fachrim Irhamna Rachman Firrofiqil A'la Kadram Muh. Hasrul Muhammad Faisal Muhammad Syafaat S. Kuba