Article Per Year (5 Year)
p-Index From 2021 - 2026
1.048
P-Index
This Author published in this journals
All Journal INTI Nusa Mandiri Jurnal Teknologi Informasi dan Komunikasi International Journal of Community Service Jurnal Penelitian Sistem Informasi
Febryawan Yuda Pratama
Lecturer Of Politeknik Jatiluhur, Purwakarta, Indonesia
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Implementation Of Finite State Automata In A Laundry Perfume Vending Machine For Clothes And Carpets Desvia, Yessica Fara; Pratama, Febryawan Yuda; Suhendri, Suhendri
Jurnal Teknologi Informasi dan Komunikasi Vol 18 No 2 (2025): October
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v18i2.296

Abstract

Perfume is popular among various groups of people, including laundry fragrances. Laundry perfumes come in a variety of scen$ts, such as fruity, floral, a combination of fruit and floral, and woody aromas. These fragrances are typically applied during the final stage of the laundry process. Currently, customers receive their laundry with a randomly selected scent based on the availability at the laundry service, which means they cannot choose the fragrance they prefer. Therefore, a Vending Machine (VM) design is needed to allow customers to select their desired laundry perfume. The VM is designed using the Finite State Automata (FSA) approach, specifically the Non-Deterministic Finite Automata (NFA) type, as it can accommodate multiple conditions for a single option. The development of the NFA method involves stages such as business process analysis, state diagram creation, VM design, and system testing. The results of this study indicate that the implementation of this VM simplifies the process for customers to choose their preferred laundry perfume, ensuring that their laundry has a scent that matches their personal preferences.
PENINGKATAN AKURASI KNN DALAM PREDIKSI KELULUSAN MAHASISWA MELALUI OPTIMASI PARAMETER PSO Desvia, Yessica Fara; Pratama, Febryawan Yuda; Wijaya, Ganda
INTI Nusa Mandiri Vol. 20 No. 1 (2025): INTI Periode Agustus 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i1.7076

Abstract

Predicting student graduation is a crucial aspect in supporting academic planning and ensuring timely completion of studies. However, no prior research has specifically applied the integration of K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) for graduation prediction using student data. This study aims to evaluate the effectiveness of combining KNN and PSO in improving classification accuracy. The KNN algorithm is used for classification, while PSO is implemented as a feature selection technique to identify the most relevant attributes. A dataset of 750 student records was processed through data preprocessing and attribute weighting using PSO, followed by model training and evaluation with 10-fold cross-validation. The evaluation results show that the KNN+PSO model improves accuracy from 80.91% to 84.31%, along with increases in precision and recall. These findings indicate that PSO enhances the performance of KNN, particularly in identifying students likely to graduate on time
Analisis Big Data Perpajakan untuk Mendeteksi Potensi Kecurangan Pelaporan Pajak Febryawan Yuda Pratama; Angga Rahmat Pinanggih; Yessica Fara Desvia; Nina Mardiana; Aura Mutiara Zahra
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 2 (2026): Mei: JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v4i2.3877

Abstract

Tax administrations are undergoing a fundamental transition from conventional audit practices based on manual inspection and limited sampling toward data-driven supervision supported by big data analytics, artificial intelligence, and digital transaction infrastructures. However, developing economies, particularly in Southeast Asia, continue to face structural constraints such as fragmented legacy systems, informal economic activities, uneven digital literacy, corruption risks, weak data interoperability, and evolving privacy regulations. This study aims to develop a contextual framework for detecting potential tax-reporting fraud by integrating big data tax analytics, localized machine learning, explainable artificial intelligence, blockchain-enabled value-added tax data integrity, and socio-organizational governance. The study adopts a mixed-method sequential explanatory approach combined with Design Science Research. The methodological design integrates policy and institutional analysis, machine learning model design, and socio-organizational validation using secondary literature, Southeast Asian case studies, regulatory review, and simulated data architecture. The main contribution of this study is the Contextual Tax Analytics with AI and Blockchain Framework, or C-TAX-AIB Framework, consisting of three interrelated layers: Data Layer, Analytics Layer, and Governance and Human Layer. The Data Layer proposes a hybrid blockchain architecture for e-Faktur and value-added tax reporting integrity; the Analytics Layer introduces localized machine learning and explainable AI to support transparent risk scoring and anomaly detection; and the Governance and Human Layer embeds privacy protection, taxpayer digital literacy, auditor readiness, and trust-building mechanisms. The framework advances prior studies by moving beyond algorithmic fraud detection toward an integrated governance model suitable for developing economies. The study provides theoretical implications for public finance analytics and practical guidance for ASEAN tax administrations in designing accountable, explainable, and context-sensitive digital tax systems.
Integrasi ISO 27001, Zero Trust, dan AI untuk Keamanan Sistem Informasi Keuangan Kampus Nina Mardiana; Yessica Fara Desvia; Angga Rahmat Pinanggih; Febryawan Yuda Pratama; Farah Diva Fadila
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 2 (2026): Mei: JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v4i2.3879

Abstract

Financial information systems in higher education institutions manage highly sensitive assets, including tuition payments, scholarships, payroll, vendor transactions, budgeting, and institutional financial reporting. Although ISO/IEC 27001:2022 provides a risk-based foundation for establishing an Information Security Management System, its implementation in universities is frequently constrained by fragmented governance, limited resources, complex asset environments, inconsistent managerial commitment, cultural resistance, and limited real-time monitoring capability. This study aims to develop an integrated security evaluation model for campus financial information systems by combining ISO/IEC 27001:2022, Zero Trust Architecture, AI-driven threat detection, security maturity assessment, and human-factor analysis. The study adopts a mixed-method sequential explanatory design integrated with Design Science Research. Quantitative stages include asset identification, risk scoring, ISO 27001 control gap analysis, maturity assessment, Zero Trust readiness assessment, and AI-driven detection readiness assessment. Qualitative stages include document analysis, semi-structured interviews, observation, expert judgment, and thematic analysis to examine organizational, cultural, and behavioral factors influencing security control effectiveness. The proposed outcome is the HEFIS-ISMS Model, an integrated framework consisting of seven layers: ISO 27001 control compliance, risk-based asset protection, security maturity, human and organizational factors, Zero Trust readiness, AI-driven detection readiness, and improvement roadmap. The model is expected to address the static and compliance-oriented limitations of conventional ISO 27001 assessments by introducing adaptive access control, continuous monitoring, anomaly detection readiness, and phased implementation guidance. The study contributes theoretically to cybersecurity governance in higher education and practically to risk-prioritized security improvement for resource-constrained universities.
Pengembangan Sistem Keamanan Data Berbasis Blockchain untuk Perlindungan Informasi Sensitif di Sektor Keuangan Angga Rahmat Pinanggih; Nina Mardiana; Febryawan Yuda Pratama; Yessica Fara Desvia; Arman Maulana
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 2 (2026): Mei: JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v4i2.3880

Abstract

The protection of sensitive information in the financial sector requires a security architecture capable of preserving confidentiality, integrity, availability, auditability, and regulatory accountability across multiple institutions. Conventional centralized security models remain vulnerable to single points of failure, unauthorized access, data manipulation, and limited transparency in inter-organizational data sharing. Blockchain offers tamper-resistant records, decentralized trust, and verifiable audit trails; however, its direct implementation in financial systems is constrained by scalability limitations, smart contract vulnerabilities, privacy leakage, and conflicts between immutable ledgers and data protection principles. This study aims to develop a blockchain-based data security system for protecting sensitive financial information by integrating permissioned blockchain and Zero-Knowledge Proofs. The proposed method adopts a consortium-oriented permissioned blockchain architecture, represented by Hyperledger Fabric, to ensure controlled participation, certificate-based identity management, endorsement policies, and auditable transaction validation. Smart contracts are designed as policy-enforcement components for consent management, access authorization, data commitment, revocation, and audit logging. Zero-Knowledge Proofs are incorporated to verify customer attributes, eligibility, and access rights without disclosing raw personal or financial data. Sensitive information is stored off-chain in encrypted form, while the blockchain records only cryptographic commitments, hashes, consent states, and audit events. The expected result is a security model that improves data integrity, controlled access, privacy-preserving verification, and compliance-oriented accountability while reducing unnecessary exposure of sensitive data on-chain. The implication of this research is the provision of a technically coherent framework for financial institutions seeking to adopt blockchain securely in regulated environments, especially where data confidentiality, auditability, and privacy compliance must be achieved simultaneously.
Empowering Local MSMEs in Purwakarta through AI-Based Market Intelligence (a Case Study at Ceramic Product) Hetifah Sjaifudian; Nuzul Husnah; Nina Mardiana; Febryawan Yuda Pratama; M. Yani Syafei; Prattana Srisuk; Nina Rosida; Dede Kurniawan
International Journal Of Community Service Vol. 6 No. 2 (2026): May 2026 ( Indonesia - Thailand - Malaysia - Philippines)
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijcs.v6i2.984

Abstract

This international community service program aims to empower local Micro, Small, and Medium Enterprises (MSMEs) in Purwakarta through the application of AI-based market intelligence, focusing on ceramic product MSMEs. Unlike conventional digital marketing assistance, this program emphasizes the development of market-sensing capability, customer insight, product positioning, and data-informed promotional planning. The activity was implemented through participatory training, guided practice, mentoring, and evaluation involving lecturers, students, an international academic partner, and local MSME actors. Participants were introduced to practical AI tools to identify customer segments, generate product narratives, analyze market opportunities, and develop promotional content for ceramic products. The results show that the program improved participants’ awareness of market-oriented decision-making and enhanced their ability to translate product uniqueness into customer-centered marketing messages. Participants were also able to develop simple customer personas, AI-assisted product descriptions, competitor observation notes, and digital content ideas. The activity demonstrates that AI-based market intelligence can be used as an empowerment instrument for MSMEs when introduced through simple, contextual, and practice-based methods. Continuous mentoring is recommended to strengthen digital confidence, improve content consistency, and support sustainable market expansion for local ceramic MSMEs.
Co-Authors Angga Rahmat Pinanggih Arman Maulana Aura Mutiara Zahra Dede Kurniawan Desvia, Yessica Fara Farah Diva Fadila Ganda Wijaya, Ganda Hetifah Sjaifudian M. Yani Syafei Nina Mardiana Nina Mardiana Nina Rosida Nuzul Husnah Prattana Srisuk Suhendri Yessica Fara Desvia