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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUITA : Jurnal Informatika Infestasi Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Madura Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) Emerging Science Journal Insect (Informatics and Security) : Jurnal Teknik Informatika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer CYBERNETICS Literasi Hukum Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan Buletin Ilmiah Sarjana Teknik Elektro Jurnal Teknologi Dan Sistem Informasi Bisnis International Journal of Robotics and Control Systems Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Journal Transformation of Mandalika SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Journal of Advanced Health Informatics Research Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Control Systems and Optimization Letters
Purwono, Purwono
Universitas Harapan Bangsa
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Integrating User Acceptance Evaluation into District-Level Mobile Health System Design for Maternal Care Arif Setia Sandi Ariyanto; Purwono Purwono; Deny Nugroho Triwibowo
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29616

Abstract

Many mobile health information systems are developed based primarily on technical requirements, while user acceptance is often assessed only after deployment and rarely integrated into the design process. This study addresses that gap by incorporating user acceptance evaluation as a design-informed feedback mechanism in the development of a district-level mobile health information system for maternal care. An applied research approach was used through system development, pilot deployment across five sub-districts, and post-use evaluation involving 74 active users. User acceptance was assessed using structured questionnaires covering perceived usefulness, perceived ease of use, consultation feature acceptance, and automated conversational support acceptance. The results showed high overall acceptance, with direct consultation with local midwives receiving the highest score (mean = 4.41; SD = 0.52), followed by consultation routing effectiveness (mean = 4.35; SD = 0.55). Perceived ease of use was positively associated with consultation feature acceptance (ρ = 0.46, p < 0.01). Automated conversational support was positively perceived but obtained lower scores (mean = 3.92) than human-based consultation features, indicating its complementary role. These findings demonstrate that user acceptance evaluation can provide actionable evidence for iterative system refinement and support the development of context-aware mobile health systems for maternal care.
Understanding Large Language Models: A Review Annastasya Nabila Elsa Wulandari; Purwono Purwono; Alfian Ma’arif; Noorulden Basil; Hamzah M. Marhoon
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.292

Abstract

Large Language Models (LLMs) have experienced rapid development and have been established as the dominant paradigm in modern Natural Language Processing (NLP), with high performance demonstrated across various language understanding and generation tasks. Increasing architectural complexity has led to the need for a structured conceptual framework to explain how architectural design, training paradigms, and inference mechanisms are collectively associated with model behavior. A conceptual and analytical review of LLMs is presented in this article through an examination of the relationship between Transformer-based architectures, multi-stage training processes, and the resulting capabilities and limitations. Encoder-only, decoder-only, and encoder–decoder architectural variants are examined in relation to structural characteristics and functional implications. The roles of pretraining, supervised fine-tuning, and instruction tuning are analyzed to clarify how output characteristics are shaped during model development. This study emphasizes how architectural and training strategies causally influence generative capabilities and inherent limitations. Fundamental issues, including hallucination, bias, data dependency, computational cost, and evaluation challenges, are critically examined as consequences of the probabilistic modeling paradigm adopted in LLMs. This review contributes a structured analytical perspective for evaluating LLMs design choices and their operational consequences, supporting more informed development and deployment practices.
Knowledge Distillation in Lightweight U-Net Transformer Architectures for Brain Tumor Segmentation Toat Tuloh; Purwono Purwono; Iis Setiawan Mangkunegara
Journal of Advanced Health Informatics Research Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v4i1.369

Abstract

Brain tumor segmentation from Magnetic Resonance Imaging was an essential step for therapy planning, prognosis evaluation, and treatment monitoring in glioma patients. Manual delineation required substantial time and was prone to inter-observer variability. Although deep learning models achieved high segmentation accuracy, performance improvements were often accompanied by increased computational complexity, limiting their applicability in resource-constrained clinical environments. To address this issue, an efficiency-oriented segmentation framework was developed based on a lightweight three-dimensional U-Net enhanced with a shallow Transformer module and guided by knowledge distillation. The main contribution of this study was the integration of logit-level and feature-level distillation to improve segmentation capability while maintaining low inference complexity. The framework emphasized a balanced trade-off between segmentation accuracy and computational efficiency rather than benchmark maximization. Experiments were conducted using the BraTS 2020 and BraTS 2021 datasets. The official training sets were internally split into eighty percent for training and twenty percent for validation. The student network was trained using a hybrid segmentation loss combined with temperature-scaled logit distillation and bottleneck feature alignment from a higher-capacity teacher model. Model performance was evaluated using Dice score, Intersection over Union, and computational complexity measured in floating-point operations. On the BraTS 2021 dataset, the proposed model achieved Dice scores of 0.6538 for Whole Tumor, 0.5382 for Tumor Core, and 0.5304 for Enhancing Tumor. Per-class Dice values were 0.9706 for background, 0.3028 for necrotic or non-enhancing tumor core, 0.4938 for edema, and 0.4936 for enhancing tumor. The corresponding Intersection over Union values followed similar trends. The model maintained an inference complexity of approximately 54.38 gigafloating-point operations for input patches of size 64 × 64 × 64. These findings indicated that the proposed framework achieved a stable balance between segmentation performance and computational efficiency, supporting practical deployment under limited computational resources
Co-Authors Adhi Wibowo Agung Budi Prasetio Agung Nurmansyah Agung Pangestu Ahmad Toha Alfian Ma'arif Alfian Ma’arif Amanah Wulandari Annastasya Nabila Elsa Wulandari Ariefah Khairina Islahati Arif Setia Sandi A. Asmat Burhan Asmat Burhan Axl Adilla Bala Putra Dewa Bala Putra Dewa Bala Putra Dewa Bala Putra Dewa Barlian Kristanto Burhanuddin bin Mohd Aboobaider Deny Nugroho Triwibowo Dewi Astria Faroek Dimas Febri Kuncoro Dimas Herjuno Eko Ariyanto Elsa Wulandari, Annastasya Nabila Endang Setyawati Hadi Jayusman Hamzah M. Marhoon Hesti Ayu Wahyuni Iin Dyah Indrawati Iis Setiawan Mangkunegara Iis Setyawan Mangku Negara Imam Ahmad Ashari Imam Ahmad Ashari, Imam Ahmad Imam Riadi Imam Riadi Irfan Arfianto Jatmiko Indriyanto Jihad Rahmawan Khoirun Nisa Khoirun Nisa Khoirun Nisa Lutviana Lutviana Lutviana Lutviana Lutviana Mangku Negara, Iis Setiawan Mangkunegara, Iis Setiawan Marlia Hafny Afrilies Maya Ruhtiani Muchammad Naseer Muhammad Ahmad Baballe Muhammad Baballe Ahmad Muhammad Haikal Satria Muhammad Hery Santoso Muntiari, Novita Ranti Musafa Widagdo Noorulden Basil Pramesti Dewi Qazi Mazhar ul Haq Rahmadhani, Berlina Riska Suryani Riyanarto Sarno Rosyid Ridlo Al-Hakim Rubaeah, Siti Rusydi Umar Safar Dwi Kurniawan Salah, Wael A. Sandi Najib Iskandar Sharkawy , Abdel-Nasser Slamet Slamet Sony Kartika Wibisono Sony Kartika Wibisono Sony Kartika Wibisono Sony Kartika Wibisono Supriyatin Supriyatin Toat Tuloh Tohari Ahmad Tusaria Tri Wahyu Ningrum Wahyu Rahmaniar Windu Gata Wirasto, Anggit Wulandari, Annastasya Nabila Elsa Yanuar Zulardiansyah Arief Yudhistira , Aimar Yuris Tri Naili Yuris Tri Naili Yuslena Sari, Yuslena Yusuf Fadlila Rahman