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p-Index From 2021 - 2026
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This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Business Intelligence (IJUBI) Systematic Literature Review Journal
Hendro Wijayanto
Universitas Tiga Serangkai
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Health Professions Nursing Public Health Social Sciences Other

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IMPLEMENTASI ALGORITMA RIVEST SHAMIR ADLEMAN (RSA) MULTIPRIMA DAN CHINESE REMAINDER THEOREM (CRT) PADA PENGAMANAN PESAN TEKS Adhi Yoga Pratama; Hendro Wijayanto
Indonesian Journal of Business Intelligence (IJUBI) Vol 8 No 1 (2025): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v8i1.5523

Abstract

RSA (Rivest-Shamir-Adleman) adalah algoritma kriptografi kunci public yang popular dalam implementasinya. Sulitnya memfaktorkan bilangan besar menjadi factor-faktor prima adalah dasar keamanan algoritma RSA. Tahap awal RSA adalah memilih bilangan prima acak p dan q, untuk selanjutnya dihitung modulus n. Besarnya nilai modulus n berbanding lurus dengan tingkat keamanan dan tingginya waktu performansi algoritma. Dalam aspek keamanan, untuk memperoleh nilai modulus n yang besar maka diimplementasikan RSA Multiprima dengan tiga bilangan prima acak (p, q, dan r) dalam pembangkitan kunci. Menangani efisiensi waktu proses dekripsi, memanfaatkan Chinese Remainder Theorem (CRT) untuk menghitung kunci privat dalam beberapa modul terpisah, sehingga dapat mempercepat  kalkulasi dalam proses dekripsi. Hasil dari kajian ini menunjukan bahwa implementasi CRT pada RSA Multiprima dapat mempercepat proses dekripsi, sehingga dapat menjadi solusi efisiensi sumber daya dalam implementasi algoritma kriptografi RSA Multiprima. Dalam kajian ini, implementasi menggunakan Bahasa pemrograman Python dengan versi 3.12.2. Kajian ini diharapkan dapat memberikan manfaat dalam pengembangan kriptografi modern yang lebih efisien.
Computational framework for smart tourism management: hybrid time series decomposition and predictive modeling Iwan Ady Prabowo; Hendro Wijayanto; Teguh Susyanto
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.pp3421-3430

Abstract

Smart tourism management in rural multi-destination settings requires forecasting methods that are accurate enough to support visitor allocation, infrastructure readiness, and ecological protection. This study presents a decomposition-based forecasting framework for Sidowayah Village, Central Java, Indonesia, which integrates three attractions with different demand profiles: Umbul Manten, Siblarak, and Kampung Dolanan. Using monthly visitation data from May 2023 to April 2024, the study compares additive and multiplicative decomposition models within a common workflow of data collection, preprocessing, trend-seasonal decomposition, model evaluation, and sustainability-oriented interpretation. The contribution of the study lies in clarifying destination-specific criteria for selecting additive versus multiplicative models, improving methodological transparency in preprocessing and temporal validation, and translating forecast outputs into practical smart tourism actions aligned with sustainable development goals (SDGs) 11 and 12. The results show that the multiplicative-average all model yields the lowest mean absolute percentage error (MAPE) for Umbul Manten (14.1%) and Siblarak (56.8%), while the additive-centered moving average model is more suitable for Kampung Dolanan based on mean absolute deviation (MAD) (162.6). Although the 12-month dataset limits long-term generalization, the framework provides a reproducible basis for data-informed tourism management in rural destinations.
Threats and Mitigations of Cybersecurity in Augmented, Virtual, and Extended Reality: A Systematic Literature Review Hendro Wijayanto; Paulus Harsadi; Bayu Dwi Raharja; Dwi Remawati
Systematic Literature Review Journal Vol. 2 No. 3 (2026): July : Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v2i3.313

Abstract

The rapid adoption of augmented reality (AR), virtual reality (VR), and extended reality (XR) technologies has introduced a distinct cybersecurity attack surface shaped by continuous biometric and spatial data collection, immersive interfaces, and multi-user collaborative environments. While individual studies have examined specific threats such as privacy leakage, side-channel attacks, and social engineering within immersive systems, a consolidated mapping of threats and their corresponding mitigation strategies across the AR/VR/XR domain remains limited. This study presents a systematic literature review guided by the PRISMA 2020 reporting framework to synthesize indexed research on cybersecurity threats and mitigations in AR/VR/XR published between 2018 and 2026. A structured search strategy combining terms related to immersive technologies and cybersecurity was applied across major computer-science literature sources, followed by criteria-based screening, yielding 45 studies included in the final synthesis. The review organizes threats into six categories: privacy and data leakage, side-channel and input-inference attacks, authentication and access-control weaknesses, perceptual manipulation and social engineering, malware and session hijacking, and physical safety risks. Correspondingly, five categories of mitigation strategies are identified: privacy-preserving techniques, detection and defense frameworks, authentication mechanisms, policy and risk-assessment frameworks, and interface- or awareness-based defenses. The synthesis reveals that mitigation research lags behind threat identification, particularly for perceptual manipulation and multi-user collaborative attacks. These findings offer a structured reference for researchers and developers seeking to design more secure immersive systems and highlight priority directions for future empirical research.
Co-Authors Adhi Yoga Pratama Bayu Dwi Raharja Dwi Remawati Iwan Ady Prabowo Paulus Harsadi Teguh Susyanto