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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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ANALYSIS OF LIBRARY VISITOR GROUPING THROUGH MASK USAGE IDENTIFICATION IN XIN ZHONG LIBRARY WITH ORANGE DATA MINING APPLICATION Putra, Dwi Permana; Yuadi, Imam
Publication Library and Information Science Vol 9, No 1 (2025)
Publisher : UPT. Perpustakaan Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/pls.v9i1.11508

Abstract

AbstractThe application of data mining in libraries plays a crucial role in supporting data management and monitoring health protocols, especially during the pandemic. A key challenge faced by librarians is effectively monitoring visitors' mask usage compliance. This study aims to analyze visitors' facial images at the library using the Orange Data Mining application, enabling librarians to identify whether visitors are wearing masks. The approach involves collecting random facial images of visitors, preprocessing the data for standardization of size and resolution, extracting features using the Inception V3 model, and conducting hierarchical clustering analysis with the Manhattan metric. The clustering results are visualized in a dendrogram, helping to group the data. The findings show that the dendrogram clearly differentiates between visitors with masks and those without. This visualization provides librarians with an effective tool for monitoring areas of the library that require more strict health protocol supervision. The study concludes that the Orange Data Mining application offers a practical solution for libraries to monitor compliance with health protocols. By utilizing data mining techniques, libraries can enhance visitor safety and comfort. Further research is suggested to expand the dataset and explore other methods to improve analysis accuracy.
Prediction of librarian interest in library management in Pamekasan with comparison of SVM and KNN algorithms Lathifah, Lathifah; Yuadi, Imam
Pustaka Karya : Jurnal Ilmiah Ilmu Perpustakaan dan Informasi Vol. 13 No. 1: Juni 2025
Publisher : S1 Ilmu Perpustakaan dan Informasi Islam FTK UIN Antasari Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18592/pk.v13i1.15839

Abstract

This study was conducted to determine the prediction of librarian interest in joining a library organization. Using survey data and interviews with librarians that produced 130 test data then divided into two groups of data, namely "interested" and "not interested". Using the Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) models as a comparison of the performance of the two algorithms in classifying librarian interests. The results of the test data were then evaluated using a confusion matrix to assess the accuracy, precision, and recall of each model. The results of the interest predictions tested showed that the use of the SVM model was more consistent in classifying librarian interests with high accuracy, although there were some errors in the "Not Interested" category. While the results of interest predictions using the KNN model tended to dominate the prediction of the "Interested" category, there were more errors in identifying the "Not Interested" category. Both models show their respective advantages and disadvantages in classifying librarian interest predictions. From the results of this study, it can be a picture and insight into the effectiveness of using the two models in classifying librarian interest predictions in joining a library organization and as a guide in choosing the right algorithm in similar research.
K-Means Clustering untuk Analisis Tren Peminjaman Buku di Perpustakaan Rosiana, Lidya; Yuadi, Imam
Journal of Technology and Informatics (JoTI) Vol. 7 No. 1 (2025): Vol. 7 No.1 (2025)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v7i1.933

Abstract

This study aims to analyze book borrowing trends in libraries using the K-Means Clustering algorithm in Orange Data Mining. The data used in this research includes historical book borrowing records, such as borrowing frequency, book categories, and borrowing times. The study clusters the data to identify significant patterns and trends. The analysis process begins with data preprocessing, including data cleaning, normalization, and transformation. Subsequently, the K-Means algorithm is applied to divide the data into several clusters based on similarities in borrowing patterns. The results show that books in certain categories, exhibit distinct borrowing patterns. The generated clusters provide insights into the characteristics of groups of book titles with high borrowing intensity and book titles that tend to be borrowed at specific times. These insights can be utilized for more effective book collection management, the development of library promotion strategies, and the creation of book recommendation systems. This study demonstrates that the K-Means Clustering algorithm is an effective tool for library data analysis, enabling libraries to understand user needs and improve the services they provide.
Mewujudkan Kesetaraan Gender Melalui Menstrual Hygiene Management: Studi Bibliometrik Mahardika, Synthia Amelia Putri; Yuadi, Imam
Az-Zahra: Journal of Gender and Family Studies Vol. 5 No. 2 (2025): June 2025
Publisher : UIN Sunan Gunung Dajti Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/azzahra.v5i2.33182

Abstract

Sulitnya akses perempuan terhadap produk menstruasi merupakan bagian permasalahan dari menstrual hygiene management. Menstrual hygiene management merupakan bagian dari kesetaraan gender (Sustainable Development Goals nomor 5), namun demikian kesadaran dan kepedulian masyarakat terhadap fenomena ini masih sangat minim sehingga menjadi penting untuk dibahas. Penelitian ini bertujuan untuk menganalisis perkembangan publikasi dan trend topics terkait menstrual hygiene management dan kaitannya dengan kesetaraan gender. Metode yang digunakan oleh penelitian ini adalah studi bibliometrik dengan Web of Science sebagai database untuk menghimpun data penelitian dan aplikasi Rstudio Biblioshiny digunakan untuk menganalisis data penelitian. Dari analisis ini ditemukan bahwa jumlah artikel dengan topik terkait yang telah dipublikasi sebanyak 183 artikel. Publikasi ini meningkat mulai dari tahun 2020 dan mencapai puncaknya pada tahun 2022. Ditemukan juga sumber, penulis, negara, dan afiliator dalam mempublikasi artikel dengan topik ini didominasi oleh negara-negara maju. Lalu trend topics “Health”, “Girls”, dan “Hygiene Management” menjadi istilah yang paling banyak digunakan dalam pembahasan topik menstrual hygiene management dan kaitannya dengan kesetaraan gender. Hasil temuan ini menunjukan bahwa produksi pengetahuan didominasi oleh perspektif negara maju, sehingga diperlukan juga kontribusi negara-negara berkembang untuk menjawab permasalahan dalam konteks lokal secara lebih relevan
Classification of Red Foxes: Logistic Regression and SVM with VGG-16, VGG-19, and Inception V3 Sabayu, Brian; Yuadi, Imam
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6356

Abstract

Deep learning models demonstrate a high degree of accuracy in image classification. The task of distinguishing between various sources of red fox images—such as authentic photographs, game-captured images, hand-drawn illustrations, and AI-generated images—raises important considerations regarding realism, texture, and style. This study conducts an evaluation of three deep learning architectures: Inception V3, VGG-16, and VGG-19, utilizing images of red foxes. The research employs Silhouette Graphs, Multidimensional Scaling (MDS), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to assess clustering and classification efficiency. Support Vector Machines (SVM) and Logistic Regression are utilized to compute the Area Under the Curve (AUC), Classification Accuracy (CA), and Mean Squared Error (MSE). The MDS plots and t-SNE data clearly demonstrate the capability of the three deep learning models to distinguish between the image categories. For game-captured images, VGG-16 and VGG-19 demonstrate quite outstanding performance with silhouette scores of 0.398 and 0.315, respectively. This study explores the enhancement of classification accuracy in logistic regression and support vector machines (SVM) through the refinement of decision boundaries for overlapping categories. Utilizing Inception V3, an artificial intelligence-generated image silhouette score of 0.244 was achieved, demonstrating proficiency in image classification. The research highlights the challenges posed by diverse datasets and the efficacy of deep learning models in the classification of red fox images. The findings suggest that integrating deep learning with machine learning classifiers, such as logistic regression and SVM, may improve classification accuracy.
Pemetaan Konseptual Kajian Feminisme melalui Analisis Bibliometrik Visual terhadap Literatur Tahun 2015–2025 Wardani, Hesti Ari; Yuadi, Imam
Populis : Jurnal Sosial dan Humaniora Vol. 10 No. 1 (2025)
Publisher : Universitas Nasional

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

Abstract

This study aims to map the conceptual structure of feminist scholarship through a visual bibliometric approach using data from Google Scholar, collected via the Publish or Perish software and analyzed with VOSviewer. A total of 1,000 academic articles published between 2013 and 2023 were analyzed. Three types of visualizations—density, overlay, and network—were employed to identify thematic density, temporal trends, and keyword co-occurrence within the literature on feminism. The results show that terms such as second wave feminism, radical feminism, and popular feminism dominate the field and serve as the foundation of contemporary feminist discourse. Meanwhile, terms such as white feminism, transnational feminism, and methodology have recently emerged, indicating a shift in research interest toward more reflective, intersectional, and global feminist frameworks. The network visualization reveals distinct thematic clusters that illustrate the complexity and diversity of feminist approaches. These findings suggest that feminist scholarship is evolving from ideological roots toward more methodological and transnational reflexivity. This study contributes to the intellectual mapping of feminism and provides a basis for future interdisciplinary and context-specific feminist research.
Tren Publikasi Tentang Model Kepemimpinan dalam Pelayanan Publik: Suatu Analisis Bibliometrik Condro Rahino Mustikaning Pawestri; Imam Yuadi
Jurnal Wacana Kinerja: Kajian Praktis-Akademis Kinerja dan Administrasi Pelayanan Publik Vol 26, No 2 (2023)
Publisher : Center fo State Civil Apparatus Training and Development and Competency Mapping

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31845/jwk.v26i2.834

Abstract

Research related to leadership models that focus on public service is currently experiencing an increase. This is based on the urgency of leadership in public service. With the phenomenon of the development of research on leadership models in public services, the question arises of how to apply the right leadership model for public services. On the basis of this problem formulation, this study aims to describe a leadership model in public services in the 2015-2020 period using bibliometric analysis which in the process uses the VOSViewer and Biblioshiny applications. The research results show that there are 54 keywords related to the topics discussed. Of the 54 keys found, 1,259 relationships were created between the subject and the keywords. Of the 1,259 linkages, there are 4,367 total link strengths. Based on the analysis of one of the most widely cited studies, it was found that the leadership model influences employees' innovative behavior by increasing the emotional approach. A leader can also influence his employees' behavior with the leadership model he applies. Therefore, selecting the right leadership model is necessary to create quality public services.
Evaluating Logistic Regression and SVM for Image Analysis Using VGG-16, VGG-19, and Inception V3 Features Habibi, Wildan; Yuadi, Imam
Jurnal Ilmiah Teknologi dan Rekayasa Vol 30, No 2 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i2.14056

Abstract

This paper presents a comparison of the classification accuracy of Logistic Regression (LR) and Support Vector Machine (SVM) classifiers on facial expression classification based on image embeddings obtained from pre-trained models like VGG-16, VGG-19, and Inception V3. Facial expression classification has relevance in emotion analysis, human-computer interaction, and security. The dataset consisted of five expressions: Angry, Fear, Happy, Neutral, and Sad. Feature embeddings were extracted by using CNN models, which are said to learn spatial features, and were classified using LR and SVM. Performance metrics like accuracy, precision, recall, and F1-score were evaluated. Inception V3 topped with 89.3% accuracy on SVM, followed by VGG-19 (87.6%) and VGG-16 (85.4%). Inception V3 was best in discriminating fine-grained expressions, as confirmed through confusion matrix analysis and visualization techniques like MDS and t-SNE. In contrast to earlier works on individual models or conventional approaches, this work emphasizes the merits of fusing powerful CNNs with strong classifiers. Limitations encompass a limited dataset and just five expressions, indicating that future research should address larger, varied datasets and real-time responsiveness for enhanced system robustness.
Batik Pattern Classification Using Logistic Regression, SVM, and Deep Learning Features Hapsari, Ratih Addina; Yuadi, Imam
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.25855

Abstract

This study presents the integration of deep learning-based feature extraction with conventional machine learning classifiers for automatically categorizing Indonesian batik patterns. The research utilizes five traditional motifs: Alas Alasan, Kokrosono, Semen Sawat Gurdha, Sido Asih, and Sido Mulyo. Feature extraction was conducted using three deep learning models: Inception V3, VGG16, and VGG19, followed by classification through Logistic Regression and Support Vector Machines (SVM), with data processing performed in Orange. Experimental results show that Inception V3 combined with Logistic Regression achieved the highest classification performance, reaching 99.2% classification accuracy and an F1-score of 0.992. These results confirm the effectiveness of deep feature embeddings in improving the automatic classification of batik motifs. The study contributes to developing intelligent classification frameworks, offering a scalable approach to cultural heritage preservation through technology. Future work will focus on enhancing feature extraction methods and expanding the dataset to address motif overlap challenges.
SENTIMENT ANALYSIS ON TRAINING IMPLEMENTATION’S FEEDBACK IN PT XYZ Rinarwastu, Fadilia; Yuadi, Imam
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6641

Abstract

Customer satisfaction is an important aspect in building a company's image, both for employees and external parties. In order to improve employee satisfaction and performance, training that organized by the company needs to receive feedback so that the training organizers can continue to provide the best service to employees who participate in the training. The large volume of feedback that must be processed in text form, leads to prolonged identification of comments and the omission of certain training programs from further analysis. This study applies text mining using sentiment analysis and Word Cloud visualization to evaluate the effectiveness of training methods and identify areas for improvement based on employee feedback on training programs at PT XYZ. The amount of data used after preprocessing was  48,910 open feedback responses from 4,314 training sessions consisting of three forms: classroom training, digital learning, and hybrid learning. The evaluation for clustering used the K-Means method, which turned out to use two optimal clusters based on the silhouette. Overall satisfaction with the training was determined through key points such as stable internet connection, overlapping of training schedule, and poor learning environment. Issues frequently that identified in the Word Cloud analysis revealed keywords describing positive and negative aspects of the situation that are requiring further improvement. This identification is useful for developing recommendations to enhance the implementation of the training and participants' experience. Further research may also involve advanced sentiment analysis and more accurate classification methods.
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