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All Journal J@TI (TEKNIK INDUSTRI) Jurnal Ilmiah Teknologi dan Rekayasa Techno.Com: Jurnal Teknologi Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Forum Ilmu Sosial Jurnal Adabiya Edulib Lentera Pustaka Jurnal Kajian Informasi & Perpustakaan JIPI (Jurnal Ilmu Perpustakaan dan Informasi) Jurnal Tamaddun Populis : Jurnal Sosial dan Humaniora Publication Library and Information Science Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Jurnal Khatulistiwa Informatika HIGIENE: Jurnal Kesehatan Lingkungan JBMP (Jurnal Bisnis, Manajemen dan Perbankan) Jurnal Pilar Nusa Mandiri Jurnal Penelitian Pendidikan IPA (JPPIPA) JURNAL YAQZHAN: Analisis Filsafat, Agama dan Kemanusiaan Indonesian Journal of Artificial Intelligence and Data Mining JRST (Jurnal Riset Sains dan Teknologi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo Angkasa: Jurnal Ilmiah Bidang Teknologi Martabe : Jurnal Pengabdian Kepada Masyarakat International Journal of Community Service Learning JURNAL GOVERNANSI Cakrawala: Jurnal Litbang Kebijakan Tibanndaru : Jurnal Ilmu Perpustakaan dan Informasi Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Transparansi Jurnal Ilmiah Ilmu Administrasi Jurnal Kesehatan Medical Technology and Public Health Journal Applied Technology and Computing Science Journal Jurnal Ekonomi Manajemen Sistem Informasi Dinasti International Journal of Education Management and Social Science Journal of Economics, Business, and Government Challenges MUKADIMAH: Jurnal Pendidikan, Sejarah, dan Ilmu-ilmu Sosial Jurnal Informasi dan Teknologi Jatilima : Jurnal Multimedia Dan Teknologi Informasi Responsive: Jurnal Pemikiran dan Penelitian Administrasi, Sosial, Humaniora dan Kebijakan Publik Bubungan Tinggi: Jurnal Pengabdian Masyarakat J-3P (Jurnal Pembangunan Pemberdayaan Pemerintahan) Info Bibliotheca: Jurnal perpustakaan dan ilmu Informasi Teknosains : Jurnal Sains,Teknologi dan Informatika Journal of Computer Networks, Architecture and High Performance Computing Unilib: Jurnal Perpustakaan Jurnal Pemerintahan dan Kebijakan (JPK) BIOLOVA Journal of Technology and Informatics (JoTI) Az-Zahra: Journal of Gender and Family Studies Media Pustakawan Pustaka Karya : Jurnal Ilmiah Ilmu Perpustakaan dan Informasi Bidik : Jurnal Pengabdian kepada Masyarakat Journal of Law, Poliitic and Humanities Jurnal Ilmu Multidisplin Malcom: Indonesian Journal of Machine Learning and Computer Science MIMBAR INTEGRITAS Journal of Governance and Social Policy Eduvest - Journal of Universal Studies SATIN - Sains dan Teknologi Informasi Journal of Economics and Management Scienties Riwayat: Educational Journal of History and Humanities (Journal of Environmental Sustainability Management) Indonesian Governance Journal : Kajian Politik-Pemerintahan Jurnal Wacana Kinerja: Kajian Praktis-Akademis Kinerja dan Administrasi Pelayanan Publik Jurnal Informatika
Imam Yuadi
Departemen Ilmu Informasi Dan Perpustakaan, Fakultas Ilmu Sosial Dan Ilmu Politik, Universitas Airlangga
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

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Analisis Bibliometrik Kebijakan Berbasis Bukti dalam Bidang Pendidikan Febriano, Rizki Dwi; Yuadi, Imam
Cakrawala Vol. 17 No. 2: Desember 2023
Publisher : Badan Riset dan Inovasi Daerah Provinsi Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32781/cakrawala.v17i2.552

Abstract

Kebijakan berbasis bukti merupakan bentuk pemanfaatan ilmu pengetahuan dan teknologi guna mendorong transformasi dan menunjang berbagai kegiatan manusia melalui produk kebijakan.Namun, penggunaan bukti dalam perumusan kebijakan masih menjadi tantangan dalam sektor publik. Tujuan dari penelitian ini untuk mengidentifikasi, melihat alur, dan arah penelitian secara terstruktur dalam studi kebijakan berbasis bukti dalam bidang pendidikan yang terbit pada tahun 2013-2022. Menggunakan metode analisis bibliometrik dengan memanfaatkan database jurnal dari Web of Science yang divisualisasikan menggunakan aplikasi VosViewer dan R Biblioshiny untuk menganalisis 158 artikel publikasi yang telah disaring berkaitan dengan kebijakan berbasis bukti dan pendidikan. Penelitian ini menghasilkan temuan bahwa bidang subjek Education Educational Research dan publikasi di dalam Journal Evidence and Policy menjadi sumber referensi utama. Britania Raya dan Australia menjadi negara yang banyak memproduksi dan melakukan menjadi subjekdalam topik kebijakan berbasis bukti dalam bidang pendidikan.
Prediction of Student Participation in the Library of the University of Muhammadiyah Malang Based on Social Media Activities Using Decision Tree Bestari, Melati Purba; Yuadi, Imam
Lentera Pustaka: Jurnal Kajian Ilmu Perpustakaan, Informasi dan Kearsipan Vol 11, No 2 (2025): December
Publisher : Library and Information Science Study Program, Faculty of Humanities, Univ. Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/lenpust.v11i2.74771

Abstract

Background: Social media has become an effective tool in promoting library services. This study uses the Decision Tree algorithm to predict student participation in the University of Muhammadiyah Malang library based on their social media activities.Objective: The data used included features such as the type of social media (Instagram, TikTok, YouTube, Twitter), the frequency of visits to the library, and the level of social media engagement.Methods: The decision tree model is processed using Orange Data Mining, which results in a clear separation between participation and non-participation based on a combination of these features.Results: The study results show that social media, especially Instagram and TikTok, significantly influence student participation in the library. The accuracy of the obtained model is about 76.7%, indicating that decision trees are an effective method for predicting library participation.Conclusion: This research provides valuable insights for designing strategies to increase library student engagement based on social media analysis  
Selisih Klaim INA-CBGs dengan Tarif Rumah Sakit Aktual di RS X Surabaya Pradhana, Andrea Thrisiawan; Yuadi, Imam; Puspitasari, Ira; Djunawan, Achmad; Sholihah, Enny Mar'atus
Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo Vol 11, No 2 (2025): JMK Yayasan RS.Dr.Soetomo, Oktober 2025
Publisher : STIKES Yayasan RS.Dr.Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29241/jmk.v11i2.2309

Abstract

The hospital payment system in Indonesia uses the Indonesia Case Based Groups (INA-CBGs). It is expected to control service costs within the National Health Insurance (JKN) program. However, there is a challenge in the form of a negative difference between BPJS Kesehatan claims and the hospital's actual tariffs. If left unchecked, this could impact the financial sustainability of hospitals. This study aims to predict and analyze the factors influencing the difference between BPJS claims and hospital tariffs at Hospital X Surabaya for the period February to June 2024. The method used is an analytical quantitative study with a time series design using secondary data from BPJS claims and the hospital’s actual tariffs for 6,523 inpatient cases. Data analysis was performed using graphs, cross-tabulation, and multiple linear regression. The results show that the difference between BPJS claims and hospital tariffs is always negative. The largest negative difference occurred in inpatient class 3. Variables such as costs of non-surgical procedure tariffs, surgical procedures, nursing care, supporting services, radiology, laboratory, blood services, rehabilitation, and room accommodation have a significant effect in increasing the claim deficit (p < 0.05). However, drug costs have a significant positive effect in reducing the deficit. The limitation of this study is that the data is not normally distributed and indicates heteroskedasticity. In conclusion, most medical and non-medical cost components contribute to the claim deficit experienced by the hospital during the study period.
Optimizing Library Visitor Satisfaction Analysis with Machine Learning Nurahman, Yeni Fitria; Yuadi, Imam
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 1 (2025): June
Publisher : Universitas Nahdlatul Ulama Surabaya

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

Abstract

In today’s increasingly digital era, libraries continue to play a vital role as centers of information, knowledge, and culture. Despite the widespread availability of online information, libraries remain essential for providing diverse resources, services, and convenient facilities. The role of libraries has evolved to meet the needs and expectations of visitors, requiring ongoing innovation in services and amenities to ensure user satisfaction. This study aims to assess the level of visitor satisfaction at UNUSA Library regarding the services provided. The research utilized questionnaire data, initially collected from 802 respondents, of which 224 valid responses were analyzed. Furthermore, this study compares the predictive performance of three machine learning methods K-Nearest Neighbor, Decision Tree, and Support Vector Machine to determine which method achieves the highest accuracy in predicting visitor satisfaction. The analysis was conducted using the Orange Data Mining application as the prediction model. The results indicate that library visitors generally report a high level of satisfaction, with certain services rated more positively than others, and that machine learning models can effectively predict satisfaction levels based on visitor feedback.
FOSTERING DIGITAL LITERACY THROUGH GLAM COLLABORATION: THE STRATEGIC ROLE OF LIBRARIES IN EDUCATIONAL TRANSFORMATION Putra, Nawwaf Faruq Adina; Yuadi, Imam; Margono, Hendro
JIPI (Jurnal Ilmu Perpustakaan dan Informasi) Vol 10, No 2 (2025)
Publisher : Progam Studi Ilmu Perpustakaan UIN Sumatera Utara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/jipi.v10i2.27880

Abstract

Digital transformation has fundamentally changed the ways information is accessed, evaluated, and utilized, making digital literacy an essential competence in contemporary education. Within this context, Galleries, Libraries, Archives, and Museums (GLAM) hold significant potential as providers of cultural and knowledge resources that support learning in the digital era. This article examines the strategic role of libraries in fostering digital literacy through GLAM collaboration as part of educational transformation. The study employs a Systematic Literature Review (SLR) method by analyzing 17 peer-reviewed articles published between 2013 and 2024, retrieved from Google Scholar, Scopus, ScienceDirect, DOAJ, and JSTOR. The findings indicate that libraries are well positioned to act as central coordinators of GLAM collaboration due to their established digital infrastructure, expertise in information organization, and metadata management capabilities. Five key themes emerge from the analysis: the urgency of GLAM integration in digital education, libraries as strategic connectors among GLAM institutions, the contribution of GLAM to digital literacy development, collaborative learning models supported by GLAM, and socio-technical challenges in implementation. Overall, GLAM collaboration led by libraries enhances critical thinking, information evaluation, and contextual understanding through access to authentic and multimodal resources. This study highlights the transformative leadership role of libraries within the GLAM ecosystem in higher education.
Classify a path on tire by using Logistic Regression and Support Vector Machine (SVM)Based on VGG-16, VGG-19, and INCEPTION V3 Modes Sufryanto, Sukma; Yuadi, Imam
Eduvest - Journal of Universal Studies Vol. 5 No. 8 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i8.50960

Abstract

This study focuses on the classification of tire tread patterns using machine learning and deep learning approaches, emphasizing Logistic Regression (LR) and Support Vector Machine (SVM) combined with feature extraction methods like Inception V3, VGG-16, and VGG-19. Results indicate that Inception V3 outperformed other feature extraction methods, yielding the highest classification accuracy (CA) of 93.2% when used with SVM. SVM demonstrated superior robustness and adaptability, especially in handling complex data, as evidenced by its high AUC values (up to 0.987) across multiple configurations. Logistic Regression, while slightly less robust, performed consistently well with simpler features, achieving stable metrics with VGG-16 (AUC: 0.976, CA: 90.7%). These findings highlight the importance of selecting appropriate feature extraction and classification combinations to optimize performance. The study recommends using Inception V3 with SVM for high-accuracy applications and Logistic Regression for scenarios prioritizing computational efficiency. These insights contribute to developing adaptive and efficient tire classification systems suitable for diverse road and environmental conditions.
Device-Based Majapahit Inscription Classification with Multi-Filter Enhancement Muhammad Rafi Raihan; Imam Yuadi
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 04 (2025): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i04.1792

Abstract

The preservation of cultural heritage through digitalization has become increasingly essential in modern archaeological and information technology research. This study focuses on classifying Majapahit inscription images based on the recording device using machine learning approaches enhanced by multiple image filtering techniques. A dataset comprising seven inscriptions photographed with seven different devices was used to evaluate the performance of three classification models: Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Four preprocessing filters Grayscale, Sobel, Histogram Equalization, and Canny Edge Detection were applied to assess their effects on model accuracy. The results revealed that the SVM consistently achieved the highest accuracy and robustness, particularly under Sobel and Histogram Equalization filters, confirming its superior ability to capture discriminative texture and edge-based features. In contrast, KNN showed unstable results due to its sensitivity to noise and intensity variations, while Logistic Regression performed moderately well in linearly separable data conditions. Paired t-test analysis further validated that SVM’s performance advantage was statistically significant. These findings highlight that edge-preserving preprocessing techniques can substantially enhance the accuracy of device-based image classification and provide a computational framework that supports digital preservation efforts in cultural heritage research.
Multi-Device Image Dataset With Manual And Python-Based Augmentations For Cross-Device Robustness In Image Classification Research Erika Putri; Imam Yuadi
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 04 (2025): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i04.1887

Abstract

This study presents a multi-device image collection and a reproducible VSCode/Python pipeline for analyzing image classification and the effects of data augmentation under real hardware variation. Images were captured at the Galeri Inovasi Institut Teknologi Sepuluh Nopember (GRITS) using four devices Infinix Note 4, LG G6, Samsung S23+, and Xiaomi Pad 6s Pro with 31 images per device. We applied manual and Python-based augmentations (rotation, flips, brightness, sharpening, contrast) and organized outputs by device and augmentation type for controlled comparisons. Using stratified 80:20 splits, we evaluated Logistic Regression (LR), SVM (RBF), and KNN. Results: LR reached accuracy 0.90 (macro-F1 0.88; weighted-F1 0.90), SVM 0.89 (macro-F1 0.88; weighted-F1 0.89), and KNN 0.67 (macro-F1 0.65; weighted-F1 0.68). Augmentation enhanced robustness and cross-device generalization, though Xiaomi Pad 6s Pro remained the most challenging class, indicating a persistent device-specific shift. The dataset and scripts provide a transparent, baseline-ready testbed for research on image classification, cross-device variability, and the impact of augmentation.
Pemetaan Bibliometrik Tren Penelitian Artificial Intelligence dalam Bidang Pendidikan Tahun 2015–2025 Salsabila, Chyntia Shafa; Yuadi, Imam
Populis : Jurnal Sosial dan Humaniora Vol. 10 No. 2 (2025)
Publisher : Universitas Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47313/ppl.v10i2.4260

Abstract

Penelitian ini bertujuan untuk memetakan tren penelitian tentang Artificial Intelligence (AI) dalam bidang pendidikan pada kurun tahun 2015–2025 dengan menggunakan metode bibliometrik. Analisis dilakukan secara deskriptif berbasis data publikasi yang diperoleh dari basis data Scopus melalui distribusi publikasi per tahun, identifikasi penulis, jurnal, institusi, serta pemetaan keywoard co-occurance dan kolaborasi antar penulis dengan memanfaatkan perangkat VOSViewer. Hasil penelitian menunjukkan bahwa terdapat pertumbuhan publikasi yang signifikan dari tahun ke tahun terutama di tahun 2020 disertai lonjakan yang tinggi pada tahun 2023 – 2025 yang dipicu dengan adanya generative AI seperti ChatGPT. Analisis kata kunci mengungkapkan tiga kluster utama, yaitu pengembangan teknologi (machine learning, natural language processing, intelligent tutoring systems), isu sosial dan etika (AI ethics, student perceptions), serta aspek pedagogis yang menekankan peran guru dan pengalaman belajar. Jejaring kolaborasi memperlihatkan dominasi peneliti dari Tiongkok, Amerika Serikat, dan Eropa, dengan beberapa tokoh berperan sebagai penghubung lintas negara. Pada temuan ini juga menyoroti bahwa penelitian AI tidak hanya fokus pada aspek teknis namun juga menyoroti etika, sosial, dan pedagogis. Abstract This research aims to map the research trends on Artificial Intelligence (AI) in the field of education during the period 2015–2025 using a bibliometric method. The analysis is conducted descriptively based on publication data obtained from the Scopus database through the distribution of publications per year, identification of authors, journals, institutions, and mapping of keyword co-occurrence and collaboration between authors using the VOSViewer tool. The results show a significant growth in publications from year to year, especially in 2020, accompanied by a high spike in 2023 and 2025, triggered by the presence of generative AI such as ChatGPT. Keyword analysis revealed three main clusters: technology development (machine learning, natural language processing, intelligent tutoring systems), social and ethical issues (AI ethics, student perceptions), and pedagogical aspects that emphasize the role of teachers and the learning experience. Collaboration networks show the dominance of researchers from China, the United States, and Europe, with several figures acting as cross-border liaisons. This finding also highlights that AI research does not only focus on technical aspects but also highlights ethical, social, and pedagogical aspects.
Comparative Study of the Performance of Naïve Bayes, SVM, and K-NN Algorithms for Sentiment Analysis and Topic Modeling of #KaburAjaDulu Hashtags Tikamidia, Sonia; Yuadi, Imam
Dinasti International Journal of Education Management and Social Science Vol. 7 No. 1 (2025): Dinasti International Journal of Education Management and Social Science (Octob
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijemss.v7i1.5119

Abstract

The #KaburAjaDulu hashtag phenomenon that has been widely discussed on platform X reflects the increasing anxiety of Indonesia's younger generation towards socio-economic conditions and the direction of state policy. This research aims to assess public perception of the hashtag through sentiment analysis and topic modeling approaches. Data was collected from X users' tweets from May to June 2025. The methods used include text preprocessing, sentiment classification using Naïve Bayes, SVM, and K-NN algorithms, and topic modeling with Latent Dirichlet Allocation (LDA). The analysis results show that SVM performs best with 98.93% accuracy and optimal precision-recall balance. The Naïve Bayes model also shows competitive results but tends to favour positive classes. In contrast, K-NN showed the lowest performance due to its inability to overcome the curse of dimensionality in TF-IDF representation. LDA topic modeling identified three main themes: the employment crisis, distrust of institutions due to corruption, and the nationalism vs. migration dilemma. These three topics indicate deep psychological conflicts experienced by youth. The findings support the Self-Determination Theory, which emphasizes the importance of autonomy, competence, and social connection for individual attachment to the environment. Lack of fulfilment of these needs triggers migration intentions as a form of escape or adaptive strategy. This research provides a practical contribution to designing HR policies based on social data. In addition, this approach can be used as the basis for a real-time public perception monitoring system.
Co-Authors AA Sudharmawan, AA Achmad Djunawan Albigaeri, Syahruly Nizar Alifka Cellina Velby Anastasya, Diva Berta Andini, Aulia Rizqi Anggraini, Pramudya Galuh Suci Artha Rachma Widiastuti Azmi, Muhammad Izharul Baihaqie, Owen Berliani, Kezia Putri Budiyan Mariyadi Cahyani, Retno Tri Christia, Tifani Dewi Condro Rahino Mustikaning Pawestri Dama Putri, Kania Dewanty, Alifia Kaltsum Dwisusilo, Aditya Endang Gunarti Enny Mar’atus Sholihah Erika Putri Fadilia Rinarwastu, Fadilia Febriano, Rizki Dwi Ferdiansah, Gilang Fitri Mutia, Fitri Fitria Wulandari, Martina Gunarti, Endang Halim, Yunus Abdul Handari Niken Anggraini Hapsari, Ratih Addina Hardevianty, Melissa Yunda Hasna, Dhia Alifia Izdihar Hendrawati, Lucy Dyah Inggrid Nindia Aprila Palupi Ira Puspitasari Ira Puspitasari Ismi Choirunnisa Prihatini Kartika Sari, Della Kezia Rahmawati Santosa Koko Srimulyo Lathifah, Lathifah Lestari, Santi Dwi Desy Lifindra, Stevanie Aurelia M Kafi Maulana M. Fariz Fadillah Mardianto Mahardika, Synthia Amelia Putri Margono, Hendro Mariyadi, Budiyan Marsaa Salsabiila Maulidah, Nofiyah Mayasari, Sentri Indah Melati Purba Bestari, Melati Purba Mochammad Edris Effendi Muhammad Rafi Raihan Nabilla Salsabil Damayanti Zahraa Nainunis, Mas Akhmad Nazikhah, Nisak Ummi Niken Ayu Pratiwi, Bertha Novia, Asradiani Nur Muhammad, Rizqi Nurahman, Yeni Fitria Nurul Firdausy Palupi, Inggrid Nindia Aprila Pradhana, Andrea Thrisiawan Prasetyo Yuwinanto, Helmy Prasyesti Kurniasari, Meinia Prayitna, Thomas Wigung Aji Purba, Trie Dinda Maharani Putra, Dwi Permana Putra, Nawwaf Faruq Adina Putri Kinanti, Novrianti Putri, Selviana Azzira Ragil Tri Atmi, Ragil Tri Rahmadani, Sinta Raihanzaki, Raka Gading Ratih Addina Hapsari Rosiana, Lidya Rosyani, Widha Sabayu, Brian Sabrina Hartianingrum, Hikmah Sabrina Nur Amalia Safina Innaf Mia Ardelia Salsabila, Chyntia Shafa Santoso, Yuniawan Heru Sari, Tri Kartika Setiadi, Yusuf Sheva Alana Brilianty Sinta Rahmadani Soesantari, Tri Sufryanto, Sukma Sugihartati, Rahma Suhada, Hofur Taufik Roni Sahroni Tikamidia, Sonia Tri Hadi Wicaksono Triandari, Ayu Ullin Nihaya Unas, Frisca Maria Vilosa, Bias Vivia Adriyanti, Elvetta Wardani, Hesti Ari Wettebossy, Anita Elizabeth Wildan Habibi Yuwinanto, Helmy Prasetyo Zidny, Irvan