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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) POSITIF Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pendidikan UNIGA Jurnal Ilmiah Universitas Batanghari Jambi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INOVTEK Polbeng - Seri Informatika IJIS - Indonesian Journal On Information System Sebatik ILKOM Jurnal Ilmiah INTECOMS: Journal of Information Technology and Computer Science Jiko (Jurnal Informatika dan komputer) IJISTECH (International Journal Of Information System & Technology) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika METIK JURNAL Jurnal Manajemen Informatika dan Sistem Informasi Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JATI (Jurnal Mahasiswa Teknik Informatika) PRAJA: Jurnal Ilmiah Pemerintahan Indonesian Journal of Electrical Engineering and Computer Science JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Pilar Teknologi : Jurnal Penelitian Ilmu-ilmu Teknik Jurnal Teknik Informatika (JUTIF) JiTEKH (Jurnal Ilmiah Teknologi Harapan) Journal of Electrical Engineering and Computer (JEECOM) IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Buletin Poltanesa Journal of Applied Computer Science and Technology (JACOST) International Research on Big-data and Computer Technology (IRobot) Journal of Applied Sciences, Management and Engineering Technology (JASMET) Journal of Information Technology (JIfoTech) Jurnal Informatika Teknologi dan Sains (Jinteks) JAIA - Journal of Artificial Intelligence and Applications Nusantara of Engineering (NOE) Jurnal Bangkit Indonesia Jikom: Jurnal Informatika dan Komputer Bulletin of Network Engineer and Informatics (BUFNETS) Journal of Informatics, Electrical and Electronics Engineering SmartComp Jurnal Informatika Polinema (JIP) TECHNOVATAR Intechno Journal : Information Technology Journal Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Teknologi : Jurnal Ilmiah Sistem Informasi
Alva Hendi Muhammad
Universitas Amikom Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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Business Process Model And Notation Untuk Memodelkan Proses Pengingat Pinjaman Pada Koperasi David Diamanta; Alva Hendi Muhammad
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.458

Abstract

Savings and loan cooperatives are strategic microfinance institutions facing challenges in managing loan reminder processes. XYZ Savings and Loan Cooperative operates manual reminder processes without standard documentation, creating risks of human error and operational inefficiency. This study aims to design Business Process Model and Notation (BPMN) to model and standardize loan reminder processes at XYZ Savings and Loan Cooperative. The research employed a qualitative approach with descriptive analytical methods. Data collection was conducted through direct observation for one month, interviews with the Secretary Department Cooperative Employee, and internal document studies. Business process analysis was performed to understand existing workflows, then modeled into BPMN elements using Bizagi Modeler software. Model validation was conducted through structured questionnaires with 20 validation aspects. BPMN model was successfully designed with two main scenarios namely Friday reminder process as the main process and Monday reminder process with follow-up mechanisms. The model involves three main actors (Cooperative Members, Cooperative Employees, and Cooperative Head) with clear swimlane divisions. The process starts from attendance checking, WhatsApp messaging, phone calls, to coordination for direct visit scheduling. Validation shows perfect conformity of 100% from 20 evaluated aspects. The BPMN model successfully transformed manual processes without documentation into structured and standardized visualization. The study concludes that BPMN implementation can effectively standardize previously manual and undocumented loan reminder processes, producing standard documentation that can be implemented for procedure standardization and new employee literacy, thereby improving operational effectiveness and reducing human error risks in cooperative loan reminder processes.
ANALYSIS OF INFORMATION TECHNOLOGY GOVERNANCE WITH COBIT 2019 ON THE BAI08 DOMAIN TO IMPROVE HIGHER EDUCATION PERFORMANCE (CASE STUDY: INSTITUT KEGURUAN DAN TEKNOLOGI LARANTUKA) Hewen, Maria Beliti; Muhammad, Alva Hendi; Nasiri, Asro
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6080

Abstract

Information technology governance is essential and must be appropriately managed to fulfill company strategy. Determination of the achievement of the capability level in the information technology governance process at the academic administration bureau at the Institut Keguruan dan Teknologi Larantuka (IKTL) as a case study site. This research aims to analyze the implementation of information technology governance and measure the achievement of capability values using COBIT 2019. The findings of the problem were that an evaluation had never been carried out, and lack of knowledge sharing caused a decrease in the quality of governance, so an evaluation was needed. The research stages help administrative staff responsible for developing and implementing knowledge and presenting information. Information technology management is carried out to facilitate the management, monitoring, and evaluation of each business process and information technology to achieve organizational goals. COBIT 2019 is used to assist organizations in managing and optimizing existing information technology by using factor design to determine important process domains according to the existing circumstances at the institution. Then, the level of capability in the selected domain BAI08 will be analyzed. The results of measuring the level of capability reached a value of 2.25 at level 2 of the expected goals. The solution to overcome the gap is to give recommendations for improving the governance of information technology
QUALITY MANAGEMENT OF INFORMATION TECHNOLOGY GOVERNANCE COBIT 2019 FRAMEWORK EDUCATION FACTORS IN INDONESIA: A REVIEW Bismar Rifki wahyu Prasetya; Alva Hendi Muhammad
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 1 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i1.9498

Abstract

This study examines information technology (IT) governance in Indonesia's education sector using the COBIT 2019 framework through a systematic literature review (SLR) approach. COBIT 2019 is a globally recognized framework designed to help organizations manage IT effectively by integrating quality management principles to achieve strategic objectives. In the education sector, implementing robust IT governance is crucial to supporting ongoing digital transformation efforts. The SLR process involved identifying, selecting, and analyzing relevant literature to assess the implementation of COBIT 2019 in the Indonesian education sector. The findings indicate that this framework can enhance IT governance quality, particularly in risk management, resource efficiency, and operational sustainability. However, challenges persist, including limited managerial understanding, shortages of skilled human resources, and inadequate infrastructure support. To address these challenges, collaboration among the government, educational institutions, and the private sector is essential. Additionally, continuous training programs are necessary to enhance the competencies of management and IT personnel in effectively implementing COBIT 2019. The study underscores the importance of integrating technological and educational aspects to improve service quality in the education sector. Furthermore, the COBIT 2019 framework is recognized as a valuable tool for fostering collaboration among stakeholders to achieve sustainable education development in Indonesia.
Federated Learning and Deep Reinforcement Learning Synergy: Opportunities for Multi-Cloud Serverless Deployment I Gusti Ngurah Wikranta Arsa Arsa; Arief Setyanto; Andi Sunyoto; Alva Hendi Muhammad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2694

Abstract

The advancement of distributed computing has enabled the use of multi-cloud and serverless computing, which are beneficial due to their flexibility, scalability, and cost efficiency. There are, of course, pertinent challenges associated with these computing paradigms, such as resource heterogeneity, cold-start latency, vendor lock-in, and privacy. Recent trends in Federated Learning (FL) and Deep Reinforcement Learning (DRL) offer promise in tackling these challenges. FL systems enable decentralised, privacy-preserving model training across heterogeneous systems, while DRL systems enable adaptive models for real-time decision-making to optimise system resources and improve performance. This Systematic Literature Review (SLR) covers the years 2020 to early 2026 and examines the intersection of FL and DRL in multi-cloud serverless computing, following the PRISMA methodology. A primary analysis of 50 quality studies was undertaken to answer four privacy-related resource management questions. The results demonstrated FL increases privacy and scalability utilizing decentralised training. Consolidating the Federated DRL and Multi-Agent stacks increases the system by obtaining a better trade-off and optimization among latency, energy, and operational efficiency. However, a few gaps still exist, such as the absence of a more holistic framework, elusiveness in cross-system integration and collaboration, and a lack of concrete real-world applications. More work is needed to build a cohesive Federated Learning framework to improve sustainability and security in the multi-cloud, serverless systems of the future. Beyond a systematic literature review, this study provides a comparative synthesis of FL–DRL-based approaches and proposes a conceptual orchestration framework as a foundation for intelligent resource allocation in serverless multicloud environments.
Identity-Aware Lightweight MobileNetV2 with Distillation and Optuna for Face Spoofing Detection Alif Sahputra; Alva Hendi Muhammad
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 2 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i2.34863

Abstract

Presentation attacks, commonly known as face spoofing, remain a major security challenge in facial recognition-based authentication systems because forged media such as printed photos and replayed videos can deceive biometric verification. Lightweight CNN models such as MobileNetV2 are suitable for practical implementation, but their limited representational capacity may affect their ability to capture subtle spoofing cues and generalize to unseen identities. Previous evaluations may also produce inflated performance estimates when images from the same identity appear across training and testing sets. This study evaluates Knowledge Distillation and Optuna-based hyperparameter tuning on MobileNetV2 for lightweight face anti-spoofing under an identity-aware evaluation protocol. The novelty lies in an identity-aware comparison between representation enhancement through EfficientNet-B0-based Knowledge Distillation and optimization-based improvement through Optuna. A total of 60,000 CelebA-Spoof images were divided using an 80:10:10 subject-disjoint split, and four scenarios were compared. The baseline MobileNetV2 achieved the best overall balance, with an accuracy of 0.9943, F1-score of 0.9958, and ACER of 0.0058. Meanwhile, Knowledge Distillation obtained the lowest APCER of 0.0035, indicating fewer spoof samples were incorrectly accepted as live under the identity-aware evaluation setting.
Perencanaan Strategi Sistem Informasi untuk Peningkatan Layanan Administrasi Berbasis Ward and Peppard dan AHP Intan Sari Gusti; Alva Hendi Muhammad; Dony Ariyus
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 12 No 1 (2026): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v12i1.15533

Abstract

This study aims to formulate an information systems strategy to optimize an administrative service owned by LPK Techno Flash. The main problems that can be identified include minimal administrative operational efficiency due to unnecessary repetition in processes and data, minimal integration between systems, which results in service delays and decreased user satisfaction. This study utilizes a combined approach between the Ward & Peppard framework for gap analysis and strategy formulation, and the Analytic Hierarchy Process (AHP) method to determine strategy priorities. The Ward & Peppard analysis includes an evaluation of the internal and external environment, analysis of application documentation, and identification of business and information system strategy needs. The results of this study provide structured information system strategic recommendations, including information management strategies, application strategies, and technology strategies. The strategic priorities obtained from AHP will serve as the basis for LPK Techno Flash in planning and implementing integrated information system solutions, increasing effectiveness, and supporting improvements in overall service quality and user satisfaction.
An Advanced Deep Learning Approach for Automatic Disease Recognition and Classification in paddy leaf disease detection Robert Marco; Alva Hendi Muhammad; Nur Aini; Yana Hendriana
Intechno Journal : Information Technology Journal Vol. 7 No. 2 (2025): December
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2025v7i2.2482

Abstract

Purpose: Accurate detection of paddy leaf diseases is essential to ensure optimal crop yield and effective disease management. Methods/Study design/approach: In this study, we propose a hybrid deep learning model combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and an Attention mechanism for paddy leaf disease classification using the Paddy Doctor dataset. The CNN layers extract spatial features from leaf images, the LSTM captures contextual relationships between these features, and the Attention mechanism emphasizes the most relevant patterns for accurate classification. Result/Findings: Experimental results show that the proposed CNN+LSTM+Attention model achieves 95.5% accuracy, 98.12% precision, 98.3% recall, and 0.994 macro AUC, outperforming a simple CNN-3 layer while offering competitive performance compared to state-of-the-art architectures such as ResNet34 and Xception. Novelty/Originality/Value: These results demonstrate that the proposed model is highly effective in detecting paddy leaf diseases with minimal false negatives, providing a reliable and practical solution for automated paddy disease monitoring systems
Systematic Review of Adaptive User Interfaces in E-Commerce for MSMEs: Gaps and User-Centric Indicators Solehatin, Solehatin; Wahyuni, Sri Ngudi; Muhammad, Alva Hendi; Hanafi, M.
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5529

Abstract

Objective – Observations of research results related to adaptive user interfaces in e-commerce have been widely conducted; however, there is a need for evaluation and assessment of indicators based on user requirements. This study aims to conduct a systematic literature review and bibliometric analysis on adaptive user interfaces for MSMEs, based on existing empirical research. Methodology – The methodology applied is a Systematic Literature Review, using the term “adaptive user interface for MSMEs” in “Article Titles, Abstracts, and Keywords” within the Sciencedirect database, resulting in 5,622 publications from 1998 to 2025. The evaluation was carried out on November 21, 2025. The collected articles were analyzed using bibliometric analysis with VOSviewer software, based on fields of study including computer science, decision science, engineering, social sciences, business, management, accounting, and materials science. Findings – Research on adaptive user interfaces for MSMEs has been extensively conducted in line with the digitalization of the e-commerce sector. The observations sought gaps and indicators in each article. Gaps were identified; however, further research is still needed to provide more specific, comprehensive, and well-founded recommendations. Indicators focus on how to provide ease and comfort for users, as well as offering recommendations to them. Research Limitations – This study used the Sciencedirect database for articles related to adaptive user interfaces for MSMEs. Future research could enhance generalizability by integrating other databases such as the Web of Science.
Comprehensive Systematic Review of TinyML Edge Deployment: Optimization Techniques, Application Domains, and Hardware Ecosystems Bakti, Very Kurnia; Setyanto, Arif; Muhammad, Alva Hendi; Wibowo, Ferry Wahyu
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5541

Abstract

The Internet of Things (IoT) is growing rapidly, making it even more crucial to deploy Machine Learning (ML) models directly on edge devices with limited resources. TinyML fixes this matter by giving microcontroller-class hardware the ability to think for itself. This makes it less reliant on the cloud and better for latency, energy efficiency, and data privacy. This study offers a comprehensive Systematic Literature Review (SLR) of TinyML research published between 2021 and 2025, in accordance with PRISMA principles. We identified 429 records, removed 326 duplicates, and added 83 studies to the final synthesis. The evaluation examines five research inquiries concerning optimization techniques, streamlined architectures, sophisticated learning frameworks, application sectors, and hardware ecosystems. The findings underscore four key themes: enhancing models, utilizing specialized tools and technology, and adapting strategies. Some of the challenges that keep recurring are broken ecosystems, different benchmarking approaches, and on-device learning that isn't compelling when ideas shift. This research presents an open-access taxonomy that categorizes optimization techniques, application trends, and hardware constraints, thereby laying the foundation for a TinyML research agenda within the informatics community. Future directions highlight the importance of adaptive TinyMLOps pipelines, federated learning, LLM-assisted model design, and NVM-based computing to support scalable and sustainable edge intelligence. The results underscore the relevance of TinyML for advancing informatics and computer science, particularly in enabling secure, efficient, and environmentally aligned IoT systems that support SDG 9 and SDG 12.
A Systematic Literature Review of Retrieval-Augmented Generation: Methods, Applications, and Future Research Directions Muhammad Rizky Hajar; Ema Utami; Alva Hendi Muhammad
Journal of Applied Computer Science and Technology Vol. 6 No. 2 (2025): Desember 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i2.1170

Abstract

Retrieval-Augmented Generation (RAG) represents a growing research direction in the advancement of large language models (LLMs) by incorporating external information sources into the response generation process. As LLM-based systems are increasingly deployed in information-sensitive domains such as healthcare, education, and law, the demand for responses that are not only fluent but also verifiable and context-aware has become more pronounced. This study conducts a systematic literature review (SLR) of 100 recent publications to examine methodological approaches, application domains, technical challenges, and research contributions related to RAG. The review draws on studies indexed in major academic databases, including IEEE, ACM, and Springer, and applies structured inclusion and exclusion criteria to ensure analytical rigor. The findings reveal a strong emphasis on architectural optimization, particularly in the interaction between retrieval and generation components, alongside widespread adoption in domain-specific contexts. Persistent challenges identified across the literature include limitations in retriever effectiveness, system integration complexity, and the absence of standardized evaluation benchmarks. Overall, this review provides a structured synthesis of current RAG research and highlights directions for future investigation and practical deployment..
Co-Authors Abdul latif Adhien Kenya Estetikha Aditama, Galih Agung Harimurti, Agung Agus Purwanto Ahmad Yusuf Alif Sahputra Alif Syaiful Huda Ananda Fikri Akbar Andi Sunyoto Anggit Dwi Hartanto Anggrainy, Shynta Eza Annisa Hestiningtyas Arief Rahman Hakim Arief Setyanto Arif Baktiar Arsad Arta Perdana, Bagus Gede Asro Nasiri Asro Nasiri A’yuni, Ashlih Qurota Bagus Setya Baiq Yulia Fitriyani Bambang Soedijono Bambang Soedijono W.A Bambang Soedijono W.A Bambang Soedijono, Bambang Bernadhed, Bernadhed Bismar Rifki wahyu Prasetya Chaedar Fatach, Muhamad Reza Christin Soyan Dengen Danu Prawira Utama David Diamanta DHANI ARIATMANTO Dhani Ariatmanto Dony Ariyus Eka Sakti, Putra Utama Eko Pramono Ema Utami Fauzi, Moch Farid Fendi Setiabudi Ferry Wahyu Wibowo Fitriyani, Baiq Yulia Hanafi Hanafi Harahap, Muhammad Sya'ban Haris, Ruby Hasan, Nurul Rahmawati Hasibuan, M. Rivai Hery Priandoko Hewen, Maria Beliti Husni Hidayat Malik I Gusti Ngurah Wikranta Arsa Arsa Ilham Setya Budi Indra Surya Permana Intan Sari Gusti Irawan, Hafizhan Irawan, Ridwan Dwi Irwan Oyong Jangkung Tri Nygroho Jeki Kuswanto Joko Dwi Santoso Juslan, Wulandari kurniawan, Ade Kurniawan Kusnawi Kusnawi Kusrini Kusrini, K Leo, Donatus Lubna Lubna M. Hanafi Malik, Husni Hidayat Maradona, Maradona MEI PARWANTO KURNIAWAN Melinne Maldini Rosady Muh Adha Muhamad Rodi Muhammad Husein Budiraharjo Muhammad Imam Munandar Muhammad Rizky Hajar Muhartini, Sitti Muktafin, Elik Hari Nadya Chitayae Nasiri, Asro Nor Riduan Novel Adil Dwijaksana Nugroho, Hanantyo Sri Nur Aini Nur Aziz Nugroho Prasetya, Bismar Rifki wahyu Prasetya, Rendra Prima Giri Pamungkas Puji Ariningsih Raynold, Raynold Razaq, Thata Authar Richki Hardi Rifqi Anugrah Robert Marco Roymond Chandra Pradana Saputra, Mahmuda Setiajid, Bayu Setyanto, Arif Sofian Dwi Hadiwinata Solehatin, Solehatin Sri Ngudi Wahyuni, Sri Ngudi Suparyati Suparyati Suseno, Hari Budhi Taryoko, Taryoko TONNY HIDAYAT Ula, M. Izul Verawati, Ike Very Kurnia Bakti, Very Kurnia Wahyunia Ningsih Syam Widodo, Cynthia Wiwi Widayani, Wiwi Yana Hendriana Yossy Ariyanto Zakiri, Hasani Zitnaa Dhiaaul Kusnaa Washilatul Arba'ah Zitnaa Dhiaaul KWA Zubaedi, Umam Faqih