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INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi
Published by Universitas Nusantara PGRI Kediri
ISSN : 2580409X     EISSN : 25496824     DOI : https://doi.org/10.29407/intensif
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
INTENSIF Journal is a publication container for research in various fields related to information systems. These fields includeInformation System, Software Engineering, Data Mining, Data Warehouse, Computer Networking, Artificial Intelligence, e-Bussiness, e-Government, Big Data, Application Development, Geograpic Information System, Information Retrieval, Information Technology Infrastructure, Knowledge Management System, Enterprise Architecture.Published periodically in February and August.
Arjuna Subject : -
Articles 184 Documents
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Framework for Scenario-Driven Black-Box Testing of NPC Behavior in Virtual Reality Games Matahari Bhakti Nendya; Antonius Rachmat Chrismanto; Dan Daniel Pandapotan; Vito Gautama; Lunchakorn Wuttisittikulkij; Gabriel Indra Widi Tamtama Tamtama
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 2 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i1.28379

Abstract

Background: Non-Playable Characters (NPCs) play an important role in the quality of interaction in Virtual Reality (VR) games. As NPC systems become more complex and multi-role, their behavior must be evaluated not only from a design perspective but also in terms of technical stability and interaction consistency. However, systematic methods for validating NPC behavior in VR games remain limited. Objective: This study proposes and implements a scenario-driven black-box testing framework for evaluating NPC behavior in VR games based on observable interaction outcomes. Methods: NPC behavior was evaluated using normal, boundary, and stress-based interaction scenarios. Four behavioral quality criteria were assessed: determinism, state integrity, role consistency, and recovery stability. Selected dimensions of the Game Experience Questionnaire (GEQ), namely Social Behaviour Activity and Post-game Module, were also evaluated. Results: Six test scenarios were evaluated. Four scenarios (66.7%) met all criteria, while two (33.3%) were categorized as partial due to minor delays in state transitions and temporary idle-state resets. No crashes, system failures, or fatal interaction breakdowns were observed. GEQ results indicated moderate-to-good social behavior involvement (mean = 3.3) and a comfortable post-game experience (mean = 3.5). Conclusion: The proposed framework provides a structured and reproducible approach for assessing NPC behavior in VR games without requiring access to internal implementation details. The findings highlight the importance of technical behavioral testing, as positive player experience does not necessarily indicate fully stable and consistent NPC behavior.
Optimization of Boosting-based Classification for Phishing Web Detection Using Ant Colony Optimization Kadek Adies Wiranegara; Dandy Pramana Hostiadi; Roy Rudolf Huizen
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 2 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i2.28480

Abstract

Background: Cybercriminals commonly use phishing attacks by manipulating domain and website characteristics to mislead users into revealing sensitive personal information. The increasing scale of phishing attacks demands automated detection mechanisms that are accurate, efficient, and reproducible. Objective: The purpose of this research is to compare and evaluate the performance of boosting-based machine learning models for phishing domain detection integrated with Ant Colony Optimization (ACO) for feature selection. This study also aims to analyze the impact of ACO-based feature selection on classification performance and feature efficiency under consistent experimental conditions. Methods: Experiments were conducted using a public phishing webpage dataset from Kaggle, comprising 11,430 samples and 87 numerical features extracted from URL structures and webpage characteristics. Two scenarios were evaluated: training models with all features and with a reduced feature subset selected by ACO. Performance was assessed using Accuracy, Precision, Recall, F1-score, and ROC–AUC under a fixed train–test split and predefined hyperparameters. Result: Experimental results showed that LightGBM without feature selection achieved the highest accuracy 97.24%. However, ACO reduced feature dimensionality and improved computational efficiency in some models, including faster execution and lower memory usage for LightGBM, while also slightly decreasing accuracy. These findings indicate that ACO effectiveness is model-dependent and involves a balance between predictive performance and computational efficiency. Conclusion: The results confirm that boosting-based models effectively detect phishing domains, while ACO showed model-dependent effects on feature efficiency and computational trade-offs. Future work should use diverse datasets and systematic hyperparameter optimization to improve generalizability and performance.
A Deep Learning NLP Framework with Directional Augmentation for Cybersecurity Compliance Monitoring Tri Ginanjar Laksana; Prima Dina Atika; Asep Ramdhani Mahbub; Ade Rahmat Iskandar; Wan Nooraishy Wan Ahmad
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 2 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i2.28533

Abstract

Background: The increasing complexity of cybersecurity mandates, such as BSSN and Kominfo regulations, presents a significant challenge for automated compliance auditing in Indonesia. Traditional NLP models often struggle with the semantic gap between formal regulatory language and raw technical system telemetry, leading to high false-negative rates in security monitoring. Objective: The purpose of this research to develop a robust deep learning framework to automate cybersecurity compliance assessments while addressing the linguistic challenges of the Indonesian regulatory landscape. The primary goal is to enhance the detection of non-compliant system behaviors by bridging the gap between documentation and real-time logs. Methods: The proposed framework utilized a Transformer-based BERT architecture integrated with a novel Directional Augmentation (DA) mechanism. The methodology follows a four-phase process: (1) data collection of 8,240 labeled points, (2) an Anti-Leak Grouping Strategy to prevent data memorization, (3) implementation of DA through Semantic Polarization and Technical Jargon Injection, and (4) model training and evaluation.  Result: The findings of this research are indicate that the proposed framework significantly outperformed the baseline BERT model. Test Accuracy rose from 90.15% to 95.72%, while Validation Accuracy improved from 91.20% to 96.88%. The final model achieved an Overall Accuracy of 94.39%, maintaining a balanced F1-score and effectively reducing False Negatives to only 32 cases in the detection of security violations, Conlussion : Integrating Directional Augmentation into BERT optimizes Indonesian cybersecurity auditing by synchronizing BSSN/Kominfo regulatory language with technical telemetry through semantic polarization and jargon injection.
A Tiered Geospatial Sensing Framework for Context-Aware Content Delivery: Application in Scriptural Information Systems Bagas Imanuel Pasaribu; Yudhy Setyo Purwanto
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 2 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i2.28584

Abstract

Background: Current digital Bible applications predominantly utilize "pull-based" interactions, which fail to synchronize content delivery with the user’s immediate physical environment or situational context. Objective: The purpose of this research is to develop and evaluate a "push-based" framework that leverages tiered geospatial sensing to deliver contextually relevant scriptural content automatically without human intervention. Methods: Adopting an engineering-focused system design, the study integrated an OS-level Geofencing API with a custom 180-second dwell-time algorithm to filter transient noise, including high-speed movement (e.g., driving) and GPS jitter. The architecture was validated through twenty simulated field trials across diverse urban and suburban environments, specifically measuring trigger accuracy and battery efficiency. Results: The system achieved an 83.3% success rate for intended events, with the dwell-time filter successfully suppressing erroneous triggers during transient movement. Technical logs demonstrated a 67% reduction in battery consumption compared to continuous GPS polling. However, signal attenuation was identified as a significant limiting factor in high-density medical facilities and "urban canyon" environments. Conclusion: The results indicate that a tiered sensing model provides a reliable, energy-efficient solution for context-aware content delivery. By documenting the relationship between architectural density and signal deviation, this study establishes a technical blueprint for the intersection of geospatial informatics and digital theology. Future research should focus on refining multi-path mitigation techniques to improve reliability in dense structural environments.

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