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All Journal J@TI (TEKNIK INDUSTRI) Jurnal Ilmiah Teknologi dan Rekayasa Jurnal Ilmu Perpustakaan 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 Management and Economics Journal (MEC-J) 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 JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 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 Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Pustaka Ilmiah 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 Jurnal Penelitian Pendidikan, Psikologi Dan Kesehatan (J-P3K) Journal of Computer Networks, Architecture and High Performance Computing Unilib: Jurnal Perpustakaan Jurnal Teknik Informatika (JUTIF) Jurnal Pemerintahan dan Kebijakan (JPK) BIOLOVA Journal La Multiapp Journal of Technology and Informatics (JoTI) International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) 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 Research and Development in Education (RaDEn) 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 Al Maktabah Jurnal kajian Ilmu dan Perpustakaan Jurnal Informatika
Imam Yuadi
Departemen Ilmu Informasi Dan Perpustakaan, Fakultas Ilmu Sosial Dan Ilmu Politik, Universitas Airlangga
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Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network (CNN) Berbasis Pengolahan Citra Digital untuk Mendukung Ketahanan Pangan Nasional Dwisusilo, Aditya; Yuadi, Imam
Jurnal Ilmu Multidisiplin Vol. 4 No. 6 (2026): Jurnal Ilmu Multidisplin (Februari - Maret 2026)
Publisher : Green Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jim.v4i6.1617

Abstract

Penelitian ini mengkaji penyakit daun padi sebagai faktor utama yang menyebabkan penurunan hasil panen di Indonesia, di mana pemeriksaan manual oleh petani atau petugas lapangan sering berlangsung lambat, bersifat subjektif, dan rentan salah identifikasi. Kondisi ini menunjukkan perlunya sistem deteksi yang cepat, konsisten, dan akurat berbantuan teknologi digital. Penelitian ini bertujuan mengembangkan model klasifikasi penyakit daun padi menggunakan Convolutional Neural Network dengan arsitektur EfficientNetB0 yang disesuaikan dalam kerangka pengolahan citra digital. Metode meliputi ekstraksi ciri otomatis, pembagian data secara proporsional menjadi kelompok pelatihan dan pengujian, serta optimasi model menggunakan prosedur komputasi. Kinerja model dinilai melalui akurasi, ketepatan, sensitivitas, skor F1, dan analisis matriks kebingungan. Hasil penelitian menunjukkan bahwa model mencapai tingkat akurasi tinggi pada kisaran 93–96 persen dengan performa yang stabil di seluruh kategori penyakit. Temuan ini menegaskan bahwa model mampu menangkap karakter visual kompleks penyakit daun padi dan memiliki potensi kuat untuk diintegrasikan dalam sistem deteksi dini otomatis. Sistem tersebut dapat meningkatkan ketepatan pemantauan penyakit dan mendukung ketahanan pangan nasional melalui pengurangan kehilangan hasil panen serta peningkatan kualitas pengambilan keputusan budidaya padi.
Batik Pattern Classification Using Logistic Regression, SVM, and Deep Learning Features Ratih Addina Hapsari; Imam Yuadi
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/

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.
Integrating generative AI into Society 5.0: A paradigm for sustainable education Sherly Deasy Anjuwita Gultom; Toetik Koesbardiati; Imam Yuadi
Research and Development in Education (RaDEn) Vol. 5 No. 1 (2025): July
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/raden.v5i1.40665

Abstract

In the era of Society 5.0, the integration of generative technologies such as artificial intelligence (AI) into the education system is very important to achieve sustainable learning outcomes. This educational paradigm aims to harness the potential of AI in creating a learning environment that is adaptive and responsive to the needs of individual students. By using generative technology, educators can develop more interactive and personalized teaching materials, and provide faster and more accurate feedback. Qualitative research methods will be used to gain an in-depth understanding of the experiences, perceptions, and challenges faced by educators and learners in this integration process. The subjects of the study will consist of: 1). Educators who use AI technology in teaching. 2). Students involved in AI-based learning. 3). Educational institution managers who implement policies related to the use of generative technology. The research will be conducted at SMA Gloria, one of them, and several educational institutions that have implemented AI technology in their curriculum, both at elementary, middle, and high levels. Data collection techniques using semi-structured interviews will be carried out with educators and students to explore their experiences related to the use of AI technology in the teaching and learning process. In addition, the application of AI in education also allows big data analysis to understand student learning patterns and identify areas that require more attention. With the results of this approach, it is hoped that the learning process will not only be more efficient but also more inclusive, so that all students can reach their maximum potential. Thus, the integration of generative technologies in education will contribute to the achievement of the goals of Society 5.0 which focuses on human well-being and sustainability.
Cyberloafing Analytics: Predicting Causes Using Machine Learning Models Ferdiansah, Gilang; Yuadi, Imam
JRST (Jurnal Riset Sains dan Teknologi) Volume 10 No. 1, March 2026: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v10i1.25997

Abstract

Cyberloafing refers to the practice of employees utilizing internet access for non-job-related activities during work hours. Cyberloafing poses a dilemma for organizations, as it is deemed aberrant conduct that might impact overall performance. Consequently, organizations must ascertain the determinants of cyberloafing. This study seeks to identify a suitable predictive model for the determinants of cyberloafing behavior in the workplace using a machine learning methodology. The employed methodology utilizes the conventional data mining cycle, namely the Cross-Industry Standard Process for Data Mining (CRISP-DM), with Orange Data Mining as the application tool. The findings indicate that Logistic Regression is the most effective model for forecasting cyberloafing. Logistic Regression yields performance scores of 90.5% Precision and 88.9% Recall. Conversely, the Naïve Bayes model had the lowest metrics, with a Precision of 64.8% and a Recall of 51.9%. This study serves as a reference demonstrating that Logistic Regression effectively predicts cyberloafing. This study enables firms to examine the factors contributing to cyberloafing, facilitating the development of policies aimed at mitigating its adverse effects.
Knowledge landscape of open access in academic libraries through bibliometric analysis 2020-2025 Bestari, Melati Purba; Yuadi, Imam; Albigaeri, Syahruly Nizar
Jurnal Kajian Informasi & Perpustakaan Vol 13, No 2 (2025): Accredited by Ministry of Education, Culture, Research and Technology of the Re
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jkip.v13i2.65064

Abstract

Background: The digital era transformation has changed the role of academic libraries, which initially served as repositories for physical collections and have evolved into facilitators of digital information access and initiators of change in open-access management. The COVID-19 pandemic accelerated the adoption of open access due to the urgent need for unrestricted access to scientific information. Purpose: This study aimed to map the knowledge landscape of open access in academic libraries through a comprehensive bibliometric approach for 2020-2025, identifying dominant themes, intellectual structures, collaboration patterns, and emerging trends. Methods: Data were collected from the Scopus database using the TITLE-ABS-KEY search strategy ("open access" AND "academic library"). Analysis was conducted using Bibliometrix in R Statistical Software version 4.3.0 and Biblioshiny, covering Conceptual Structure Analysis, Multiple Correspondence Analysis, Intellectual Structure Analysis, Social Structure Analysis, and Thematic Evolution Analysis. Results: The analysis showed that 118 documents from 57 publication sources were dominated by collaborative research (72.1%), with limited international collaboration (6.78%). Publication productivity peaked in 2020 (26 articles) and then declined continuously. The United States dominated with 114 citations, followed by Pakistan (36) and South Africa (33). Institutional repositories, digital libraries, and scholarly communication have emerged as central themes connecting various aspects of research. Conclusion: The open-access knowledge landscape has evolved from a focus on technical infrastructure to a strategic, holistic approach. Implications: This research provides practical guidance for librarians and policymakers to develop more effective strategies in the digital transformation era.  
Klasifikasi Cacat Permukaan Keramik Menggunakan Logistic Regression dan SVM Berbasis CNN Inggrid Nindia Aprila Palupi; Budiyan Mariyadi; Imam Yuadi; Taufik Roni Sahroni
Jurnal Ekonomi Manajemen Sistem Informasi Vol. 7 No. 4 (2026): Jurnal Ekonomi Manajemen Sistem Informasi (Maret - April 2026)
Publisher : Dinasti Review

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jemsi.v7i4.7551

Abstract

Klasifikasi dalam mendeteksi cacat permukaan pada ubin keramik merupakan langkah penting dalam memastikan kualitas produk di industri manufaktur. Klasifikasi yang akurat sangat diperlukan untuk meningkatkan kualitas hasil produksi dan mengurangi kesalahan faktor manusia. Penelitian ini bertujuan untuk deteksi dan klasifikasi secara akurat pada jenis cacat baik yang bertekstur 2D dan 3D. Metode yang diusulkan dengan menggunakan Logistic Regression dan dibandingkan dengan Support Vector Machine. Dalam Penelitian ini menggunakan 133 data jenis cacat yang diambil menggunakan kamera smartphone dengan sudut 45˚. Proses pelatihan menggunakan 66% data yang dilatih dengan model Inception V3, VGG-16 dan VGG-19 kemudian 34% data jenis cacat untuk pengujian. Logistic Regression dan Support Vector Machine dengan Inception V3 memberikan hasil klasifikasi terbaik dengan akurasi dan presisi 0,99 dengan kemampuan untuk klasifikasi 100% jenis cacat seperti gompal, lubang, terkelupas, retak dengan tekstur 2D. Sedangkan VGG-19 dapat melakukan klasifikasi 100% pada jenis cacat gelembung dengan tekstur 3D. Waktu pelatihan dan pengujian Logistic Regression dengan Inception V3 6,9 dan 2,1 detik dan VGG-19 membutuhkan waktu pelatihan dan pengujian 53,8 dan 5,36 detik. Sedangkan Support Vector Machine dengan Inception V3 membutuhkana waktu pelatihan dan pengujian 6,6 dan 4,7 detik, sedangkan VGG-19 membutuhkan waktu pelatihan dan pengujian 10,1 dan 4,7 detik.
IMPLEMENTATION OF MACHINE LEARNING IN IMPROVING WEBSITE USER EXPERIENCE AND SATISFACTION Alyusi, Shiefti Dyah; Yuadi, Imam
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.7439

Abstract

This research aims to analyze user satisfaction in accessing the Airlangga University library website through the application of machine learning algorithms. The benefit of this research is that it provides insight into improving the quality of digital library services based on data-based analysis. The methods used include user surveys, data preprocessing, and application of the Orange Data Mining with models Support Vector Machine (SVM) and K-Nearest Neighbor (kNN) algorithms to classify user satisfaction levels, as well as comparing the results of the two models. The results show that the SVM model is able to achieve a Recall accuracy of 0.587 in identifying user satisfaction, but the precision metric is greater in SVM and the AUC is greater in kNN so it still requires optimization. This research concludes that the application of machine learning, especially SVM, can be an effective tool for improving user experience and providing more precise recommendations for improving library services.
Enhancing the Accuracy of Competency Portfolio Assesments using Machine Learning: a Comparative Analysis of Predictive Models Saputra, Aditya Cahya; Yuadi, Imam
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

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

Abstract

This study elaborates the application of various machine learning (ML) models to measure competency portfolio assessments for job grade conversion needed of employees. The purpose is choose the best ML models to enhance the accuracy, scalability, and fairness. Logistic regression and support vector machines is two traditional methods were evaluated together with random forest and gradient boosting as ensemble models and neural network as deep learning models. This study taken data of 117 employees invited to join on the competency portfolio assessment event on November 2024, all models were measured through cross-validation on parameters such as accuracy, precision and recall by Orange Data Mining. The best performance model in this study is Random Forest, achieving the highest score on Precision and Recall parameters. While Neural Networks demonstrated potential performance that almost has the same result with logistic regression. Based on this research, Random Forest can be prioritized and implemented to help the company to enhance the accuracy of competency portfolio results that needed to develop employees career, eligible competencies, and help decision making of job grade conversion assessment. Keywords: Comparative Analysis, Competency Portfolio Assessment, Machine Learning
Mapping the Evolution of Quiet Quitting Research: A Five-Year Bibliometric and Topic Modeling Analysis Rahardian, Dwiky; Yuadi, Imam
MEC-J (Management and Economics Journal) Vol 10, No 1 (2026)
Publisher : Faculty of Economics, State Islamic University of Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mec-j.v10i1.32318

Abstract

Quiet quitting has emerged as a significant phenomenon in modern workplace dynamics, reflecting employee disengagement and dissatisfaction with organizational structures. This study provides a comprehensive bibliometric analysis of quiet quitting research over the past five years, utilizing data from the Scopus database and Orange Data Mining for analysis. The findings reveal key themes such as employee engagement, organizational culture, burnout, leadership, and workplace dynamics. The surge in publications related to remote and hybrid work during the period of the pandemic reflects a paradigm shift in academic literature towards the normalization of such work practices. Identifies five key thematic clusters, finding that Quiet Quitting and Organizational Structures and Employee Engagement and Workplace Analysis to be key themes. The insights underscore the need for a multidimensional approach, with implications for how organizations can foster more engaged workplaces by emphasizing supportive policies, kind and engaged leadership, and fairness in task allocation to mitigate the risk of quiet quitting. This study contributes to the literature through a new examination of research patterns to a qualitative research topic that utilized empirical methods drawing on a data-driven investigation highlighting pathways for which both researchers/academics and practitioners might consider exploring going forward.
Pikachu Image Classification Using Neural Network and Support Vector Machine with Painters, VGG-16, and Inception-V3 Feature Representations Putri, Muthia Andriana; Yuadi, Imam
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 4 (2026): OCTOBER 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i4.6905

Abstract

This study aims to evaluate the effectiveness of multiple feature representations in classifying Pikachu images into three distinct visual categories: anime, action figures, and hand-drawn illustrations. The primary challenge lies in limited data availability and the high visual variability across styles, resulting in significant inter-class similarity and intra-class diversity. To address this issue, the study employs a transfer learning approach utilizing pre-trained Convolutional Neural Networks (CNNs), namely VGG-16 and Inception-V3, alongside painterly feature descriptors. The dataset comprises 351 images collected from open-access sources with balanced class distribution. Extracted features are subsequently classified using Support Vector Machines (SVM) and shallow Neural Networks. The findings demonstrate that integrating deep semantic features with artistic representations significantly improves classification accuracy compared to single-feature approaches. These results highlight the critical role of hybrid feature engineering and classifier selection in achieving robust image classification performance under data-constrained conditions.
Co-Authors AA Sudharmawan, AA Achmad Djunawan Albigaeri, Syahruly Nizar Alifka Cellina Velby Alyusi, Shiefti Dyah Anastasya, Diva Berta Andini, Aulia Rizqi Anggraini, Pramudya Galuh Suci Artha Rachma Widiastuti Arum Karisma Nadya Lashita Azmi, Muhammad Izharul Baihaqie, Owen Berliani, Kezia Putri Bondan Ari Wijaya Cahyani, Retno Tri Christia, Tifani Dewi Chyntia Shafa Condro Rahino Mustikaning Pawestri Dama Putri, Kania Denaldy Oktavian Noor Rizki Dewanty, Alifia Kaltsum Dwisusilo, Aditya Endang Gunarti Enny Mar’atus Sholihah Erika Putri Fadilia Rinarwastu, Fadilia Febriano, Rizki Dwi Ferdiansah, Gilang Fitri Mutia, Fitri Gunarti, Endang Halim, Yunus Abdul Handari Niken Anggraini Hapsari, Ratih Addina Hardevianty, Melissa Yunda Hary Supriyatno Hasna, Dhia Alifia Izdihar Hendrawati, Lucy Dyah Hendro Margono Inggrid Nindia Aprila Palupi Ira Puspitasari Ira Puspitasari Irvan Zidny 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 Mariyadi, Budiyan Marsaa Salsabiila Martina Fitria Wulandari 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 Noviana Wahyu Basuki Nur Muhammad, Rizqi Nurahman, Yeni Fitria Nurul Firdausy Palupi, Inggrid Nindia Aprila Pradhana, Andrea Thrisiawan Prasetya Triputra Nugraha 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, Muthia Andriana Putri, Selviana Azzira Ragil Tri Atmi, Ragil Tri Rahardian, Dwiky 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 Saputra, Aditya Cahya Sari, Tri Kartika Setiadi, Yusuf Sherly Deasy Anjuwita Gultom Sheva Alana Brilianty Sinta Rahmadani Siswahyudianto Soesantari, Tri Sonia Tikamidia Sufryanto, Sukma Sugihartati, Rahma Suhada, Hofur Taufik Roni Sahroni Tikamidia, Sonia Toetik Koesbardiati Tri Hadi Wicaksono Triandari, Ayu Ullin Nihaya Unas, Frisca Maria Vilosa, Bias Vivia Adriyanti, Elvetta Wardani, Hesti Ari Wettebossy, Anita Elizabeth Wildan Habibi Yuniawan Heru Santoso Yuwinanto, Helmy Prasetyo