Article Per Year (5 Year)
p-Index From 2021 - 2026
4.913
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JURNAL SISTEM INFORMASI BISNIS Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Computech & Bisnis (e-Journal) Jurnas Nasional Teknologi dan Sistem Informasi Jurnal CoreIT Informatics for Educators and Professional : Journal of Informatics Matrix : Jurnal Manajemen Teknologi dan Informatika IJIS - Indonesian Journal On Information System Dinamisia: Jurnal Pengabdian Kepada Masyarakat Conference SENATIK STT Adisutjipto Yogyakarta International Journal of New Media Technology Jurnal ULTIMA InfoSys Jurnal ULTIMATICS MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Creative Research Journal IJISTECH (International Journal Of Information System & Technology) KOMPUTIKA - Jurnal Sistem Komputer KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Majalah Ilmiah UNIKOM Jurnal Teknologi dan Informasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming @is The Best [Accounting Information System & Information Technology Business Enterprise] Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi Indonesian Community Service and Empowerment Journal (IComSE) IJISTECH International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Research and Applied Technology (INJURATECH) Abdi Teknoyasa Jurnal Pengabdian Teknik dan Ilmu Komputer (PETIK) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Jurnal Pengabdian Masyarakat Tekno INFOKOM Integrated System and Management Technology Big Data Analytics and Data Science
Irawan Afrianto
Universitas Komputer Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

Published : 56 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 56 Documents
Search

 2   3   4   5   6 
Indonesian Oil and Gas Export Data Forecasting Using Autoregressive Integrated Moving Average and Exponential Triple Smoothing Methods Kurniawan, Deni; Afrianto, Irawan
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study analysed historical data movement patterns and forecasted the value of oil and gas exports for the next 12 periods. The data obtained was secondary data in the form of monthly time series for the period (2021–2025). A time series is a series of observations of a variable that occur in relation to the time of its occurrence, while time series data is data collected over a specific time period. The analysis method used was the Box-Jenkins method or Autoregressive Integrated Moving Average with the help of R software. The model obtained through the stages of model identification, parameter estimation, and diagnostic testing was then used to project the data. The best model was selected based on the smallest Akaike Information Criterion (AIC) value and valid parameter significance tests. The forecast results for 2026 show a trend that tends to be constant or flat following the latest data level, with a confidence interval that widens as the time period increases. This indicates that the value of oil and gas exports is predicted to be stable but carries a high risk of uncertainty in the future, thus requiring adaptive policy anticipation to address market volatility.
Success Factors for Implementing Robotic Process Automation (RPA) in Accounting Information Systems within the Trade Sector: a Systematic Literature Review Firdaus, Dony Waluya; Zulkifli, Ridwan; Nawawi, Muhamad; Afrianto, Irawan; Rijanto, Estiko; Sumitra, Irfan
@is The Best : Accounting Information Systems and Information Technology Business Enterprise Vol 10 No 2 (2025): @is The Best : Accounting Information Systems and Information Technology Busines
Publisher : Labkat Press KA FTIK UNIKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/aisthebest.v10i2.18687

Abstract

Digital transformation requires more operational efficiency in the trade sector, with RPA turning out to be one of the key technologies for modernizing Accounting Information Systems. This paper seeks to identify the adoption patterns and dominant factors determining the success of RPA implementation in finance and accounting functions within the trade sector-a domain which, so far, has been underexplored as compared to the banking sector. By means of a Systematic Literature Review with PRISMA 2020 guidelines, along with the bibliometric analysis of 72 Scopus-indexed articles published between 2020 and 2025, this study maps the evolution in technology adoption. The study portrays that RPA's adoption has moved beyond the automation of mere back-office repetitive tasks, such as reconciliation and data entry, toward the orchestration of end-to-end trade processes spanning procurement and logistics, while integrating with AI for handling unstructured data. The study further finds evidence that successful implementation relies on a maturity path leading from accurate selection of routine and high-volume processes to standardization of data input. The sustainability of RPA advantages is defined by organizational capabilities through CoE and successful change management, while supported by strategic governance through alignment of business objectives with information technology.   The result of this study provides a theoretical framework and practical guidance for trading firms in ensuring investments in RPA result in performance metrics and increased compliance.
Systematic Literature Review : UI/UX Design Methods for Government Applications Rifqi Fahrudin; Petrus Sokibi; Ridho Taufiq Subagio; Irfan Dwiguna Sumitra; Irawan Afrianto; Estiko Rijanto
Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Vol 5 No 1 (2026): Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK)
Publisher : STMIK Amika Soppeng

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70247/jumistik.v5i1.254

Abstract

Digital transformation in the public sector requires the implementation of user interface and user experience (UI/UX) design that ensures government services are effective, inclusive, and easy to use. However, numerous studies indicate that government applications continue to face challenges related to usability, accessibility, and misalignment between system design and user needs. This study aims to map UI/UX evaluation and design approaches in the context of government applications through a Systematic Literature Review (SLR) guided by the PRISMA framework. The literature search was conducted in the Scopus database using the keywords (“user interface” OR “user experience”) AND (“government application” OR “e-government”) AND (“method” OR “model” OR “framework”), resulting in 275 records. Following a rigorous screening and eligibility assessment, 10 articles met the inclusion criteria and were analyzed further. The findings indicate that the most commonly employed evaluation methods include Heuristic Evaluation, Cognitive Walkthrough, Usability Testing, and the Think-Aloud Protocol, while frequently used measurement instruments include the System Usability Scale (SUS) and ISO 25022/25023 standards. Recurrent issues identified across studies include non-intuitive navigation, limited system feedback, and accessibility barriers for users with special needs. This study highlights the importance of integrating expert-based and user-centered evaluations to enhance the quality of digital government services.
Optimalisasi Manajemen Data Prostetik : Tinjauan Sistematis Integrasi Robotic Process Automation Agus Sevtiana; Irawan Afrianto; Irfan Dwiguna; Estiko Rijanto
Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Vol 5 No 1 (2026): Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK)
Publisher : STMIK Amika Soppeng

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70247/jumistik.v5i1.271

Abstract

Kompleksitas administrasi dalam rehabilitasi prostetik sering kali menghambat efisiensi klinis dan presisi data. Penelitian ini bertujuan efektivitas penerapan Robotic Process Automation (RPA) serta integrasi Intelligent Automation berbasis AI dalam tata kelola data pasien prostetik. Melalui metode Systematic Literature Review (SLR) yang mematuhi protokol PRISMA 2020, studi ini menyaring literatur dari basis data Scopus (2020–2025) menggunakan kerangka PICO. Sintesis terhadap 36 studi primer mengungkap bahwa penerapan RPA mampu memangkas durasi tugas administratif hingga 66,67 % dan mereduksi tingkat kesalahan entri data sebesar 90%. Lebih jauh lagi, kolaborasi teknologi OCR dan Machine Learning terbukti meningkatkan akurasi pembacaan bentuk tulisan tangan medis hingga mendekati 100%. Pembahasan menguraikan peta konsep di mana RPA mentransformasi layanan kesehatan dengan mengotomatiskan tugas berulang, sehingga memungkinkan profesional medis fokus pada interaksi pasien. Studi menyimpulkan bahwa RPA bertransformasi dari sekadar alat efisiensi biaya menjadi instrumen penting untuk menjamin kualitas klinis. Keberhasilan jangka panjang menuntut adanya tata kelola digital yang ketat guna memitigasi masalah privasi dan kompatibilitas sistem lama.
Developing an AI-Driven Prescriptive Framework for Orchestrating MSME Supply and Demand: Integration of Hyper-Local Community Engagement Signals Nanang Abdurahman; Zainal Arifin Hasibuan; Irawan Afrianto; Ifan Dwiguna
Integrated System and Management Technology Vol. 1 No. 2 (2026): July: Integrated System and Management Technology
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/ismat.v1i2.389

Abstract

Micro, Small, and Medium Enterprises (MSMEs) face significant challenges in balancing supply and demand amidst the rapid volatility of the digital economy era. This condition is primarily driven by conventional, reactive decision-making and a failure to utilize social media data beyond mere marketing metrics, resulting in operational inefficiencies such as overstocking or stockouts. This research aims to address this gap by developing an Artificial Intelligence-Based Prescriptive Framework for MSME Supply-Demand Orchestration. Adopting a Design Science Research (DSR) methodology, specifically focusing on problem identification, objective definition, and conceptual artifact design, this study synthesizes insights from 100 recent articles (2020-2025). The proposed conceptual artifact introduces a novel three-tier system architecture: a Sensing Layer utilizing NLP (BERT) to extract hyper-local community engagement signals into a Social Engagement Index (SEI), a Reasoning Layer employing a Hybrid Neuro-Fuzzy engine to accommodate MSME data sparsity, and a Prescriptive Actuation Layer. The findings present a mechanism that transforms unstructured qualitative social sentiment into quantitative, actionable logistical commands (e.g., "Priority Restock"), demonstrating superiority over existing predictive or macro-level models by offering a closed-loop, hyper-local solution. In conclusion, this research successfully formulates a framework that shifts MSME management paradigms from being purely reactive to actively responsive to social signals, theoretically expanding Social-SCM integration and practically offering a pathway to mitigate inventory risks and enhance economic sustainability.
Artificial Intelligence-Based Early Warning System for Disaster Management: A Literature Review Systematic and Bibliometric Analysis Ridwan Zulkifli; Zainal Arifin Hasibuan; Irawan Afrianto; Bella Hardiyana; Sri Supatmi
Big Data Analytics and Data Science Vol. 1 No. 2 (2026): June: Big Data Analytics and Data Science
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/bdas.v1i2.392

Abstract

The increasing frequency and intensity of natural disasters globally demands the development of more accurate and responsive Early Warning Systems (EWS). In recent years, Artificial Intelligence (AI) has been increasingly applied in natural disaster mitigation, but the approaches used are still diverse and spread across various domains. This study aims to present a systematic literature review on the application of AI and deep learning in natural disaster early warning systems. This review was conducted following the PRISMA 2020 guidelines by analyzing literature published during the 2020–2025 period. The selection process resulted in 102 studies meeting the inclusion criteria, with 30 full-text articles being analyzed in depth to map disaster types, AI methods, data sources, and characteristics of early warning systems developed in various regions, including Asia and Africa. The review results show the dominance of deep learning approaches, particularly time series-based models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), particularly in flood forecasting and land deformation prediction. More advanced architectures, such as Transformer, are beginning to be adopted to capture long-term temporal patterns, while the combination of convolutional neural networks (CNN) with remote sensing data is widely used for spatial mapping of disaster events. Furthermore, the integration of sensor data and the Internet of Things (IoT) shows potential in supporting more responsive early warning systems. However, most research remains limited to the modeling or simulation stage, with little discussion of the real-time and operational implementation of EWS. This review highlights the gap between AI model development and the implementation of reliable early warning systems and provides a conceptual foundation for the future development of more integrated AI-based disaster mitigation systems.
Co-Authors Abdul Razak, Rizky Septiana adang badru jaman,anggun fergina, adang badru jaman,anggun fergina Agus Sevtiana Agustia, Richi Dwi Al Husaeni, Dwi Novia Alif Finandhita Ana Hadiana Andri Heryandi Andri Heryandi Andri Heryandi Andry Maulana Awaludin Angga Setiyadi Anisyah, Ani Arinten Dewi Hidayat Asali, Frans Fernando Atin, S Atin, Sufa Bella Hardiyana Bisulthon, Ibrahim Danial Bobi Kurniawan, Bobi Dedeng Hirawan Dedeng Hirawan, Dedeng Deni Kurniawan, Deni Derry Berni Cahyady Devy Normalasari Diana Effendi Dwi Agustia, Richi Dwiguna Sumitra, Irfan Eddy Prasetyo Nugroho Edwin Fajar Nurdiansyah Egi Cahyo Prabowo Eko Budi Setiawan Elisabet Nila S. C. P Estiko Rijanto Estiko Rijanto Fazri Muhamad Kurnia Finadhita, Alif Finandhita, Alif Firdaus Musyafi Firdaus, Dony Waluya Furqon, Rifan Muhammad Hanhan Maulana Harjono, Risqi Windu Heryandi, Andri Ifan Dwiguna Irfan Dwiguna Irfan Dwiguna Saputra Irfan Dwiguna Sumitra Irmayanti, Hani Janivita Joto Sudirman Joto Sudirman, Janivita Jundurrahmaan, Irham Lia Warlina M Wisnu Hidayatulloh Maulana Awaludin, Andri Maulana, Hanhan Mouhamad Hatta Hiroshi Sasmita Muhammad Fahmi Irfan Muhammad Ilham Budiawan Muhammad Yanuar, Eko Mutia, S Nanang Abdurahman Nawawi, Muhamad Nizar Rabbi Radliya Nurhikmah Taliasih Petrus Sokibi Piantari, Erna Putra Hilmana, Primarazaq Noorshalih Rafdhi, A A Raju Riyanda Rakhmanuddin, Iid Ramadhan Wijanarko Resviani, Devi Richi Dwi Agustia Ridho Taufiq Subagio Ridwan Zulkifli Rifqi Fahrudin Rini Nuraini Sukmana Rizki, Ikhsan Nurul Setiyadi, Angga Sri Nurhayati Sri Supatmi Sufa Atin Sufa Atin Sufa Atin Sufa'atin Sufa’atin Sufa’atin Sumitra, Irfan Sumitra, Irfan Dwiguna Suryana, Taryana Suseno, Taufiq Rizky Darmawan Taryana Suryana Widayanti, A Yanuar, Eko Muhammad Yanyan Herdiansyah Yasmi Afrizal Yatawa, H S Yulistian Suryono Zainal Arifin Hasibuan zulkifli, ridwan