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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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Comparative Study of the Performance of Naïve Bayes, SVM, and K-NN Algorithms for Sentiment Analysis and Topic Modeling of #KaburAjaDulu Hashtags Sonia Tikamidia; Imam Yuadi
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.
Bahasa Inggris Irvan Zidny; Ira Puspitasari; Imam Yuadi
Jurnal Penelitian Pendidikan IPA Vol 9 No 8 (2023): August
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i8.4531

Abstract

This study presents a novel approach to assist learning analysts in identifying suitable learning pathways based on historical training data through the utilization of text mining techniques. The dataset utilized in this research comprises training data from the year 2021 and the Course Development Management Program (CDMP) catalogue. The BERT 'bert-base-nli-mean-tokens' model is employed for encoding purposes. By comparing the training data names from 2021 with the CDMP catalogue using cosine similarity and dot score, valuable insights are obtained. The findings indicate that cosine similarity is a more effective measure for interpreting the data, thereby simplifying the process for learning analysts and managers in identifying appropriate learning paths for their employees. This research provides a practical solution that leverages text mining techniques to optimize the analysis and decision-making processes in learning and development domains, enabling organizations to enhance the effectiveness and efficiency of their training programs.
Comparative Analysis of CNN Architectures for Clean and Non-Clean Outfit Classification in Fashion Images Noviana Wahyu Basuki; Imam Yuadi
Journal La Multiapp Vol. 7 No. 3 (2026): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v7i3.3256

Abstract

The purpose of this study is to create and contrast image classifiers which are able to identify clean clothing from dirty using machine learning and deep learning techniques. Our utilized dataset contains 200 images of an outfit which are obtained from different official fashion brand sites and well-known e-commerce platforms in Indonesia. The information is used from secondary data which are digital images of the two style categories. Image preprocessing (resizes, normalizations and data augmentations), feature extraction from VGG-16, VGG-19 and inception v3 is done. The extracted features are then fed into the classifiers namely Logistic Regression, Neural Network and Support Vector Machine (SVM). The evaluation of the model is performed by different metrics (e.g., AUC, accuracy, F1-score, precision, recall and MCC) and visual examination using MDS plot and Silhouette Plot. The results demonstrate that the integrated model involving VGG-16 and Logistic Regression performs best obtaining highest AUC when compared with other model combinations. The MDS and Silhouette Plot visualizations also supported that VGG-16 has the most superior feature separation between clean outfits and non-clean outfits. In a word, our study unveils that fashion style recognition accuracy can be improved significantly through CNN-based feature extraction and traditional classification model. We hope that our work will encourage the comparison of CNN feature extraction and classification algorithms, and also can lay the foundation for further research in image-based outfit guidance systems serving a range of fashion industry and service sectors where professional appearance is a criterion.
ANALISIS BIBLIOMETRIK TENTANG NETWORK GOVERNANCE PADA PELAYANAN PUBLIK Martina Fitria Wulandari; Imam Yuadi
INDONESIAN GOVERNANCE JOURNAL : KAJIAN POLITIK-PEMERINTAHAN Vol 6 No 2
Publisher : Universitas Pancasakti Tegal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24905/igj.v6i2.97

Abstract

Antusiasme terhadap teori Network Governance semakin meningkat dalam beberapa tahun terakhir. Lalu, bagaimana perkembangan implementasi teori network governance pada pelayanan publik? Penelitian ini bertujuan untuk memberikan pemahaman terkait teori network governance pada pelayanan publik melalui analisis bibliometrik perkembangan penelitian-penelitianl terkait selama 10 tahun terakhir. Analisis bibliometrik pada penelitian ini memanfaatkan databes jurnal Web of Science yang divisualisasikan menggunakan aplikasi VosViewer dan R Biblioshiny guna menganalisis 332 jurnal publikasi mengenai network governance dan pelayanan publik. Berdasarkan analisis yang telah dilakukan, salah satu penelitian dengan sitasi terbanyak menemukan bahwa teori ini relevan dalam menjawab kebutuhan publik. Penelitian terkait network governance pada pelayanan publik paling banyak diproduksi oleh Amerika Serkat. Berdasarkan analisis biblioshiny, beberapa sumber jurnal yang paling relevan terhadap topik penelitian ini dalam area penelitian Public Administration antara lain Public Management Review, American Review of Public Administration, International Journal of Public Sector Management, International Review of Administrative Science, dan Journal of Public Administration Research and Theory.
Advancing Winged Animal Classification Through Image Analysis Prasetya Triputra Nugraha; Imam Yuadi
Jurnal Penelitian Pendidikan, Psikologi Dan Kesehatan (J-P3K) Vol 7, No 2 (2026): J-P3K
Publisher : Yayasan Mata Pena Madani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51849/j-p3k.v7i2.994

Abstract

This study is to assess how well two classification algorithms, Support Vector Machine (SVM) and Logistic Regression, work with deep learning-based feature extraction techniques, including Inception V3, VGG-16, and VGG-19. The methodology comprised preprocessing a collection of photos of flying animals, using the three convolutional neural network (CNN) designs to extract features, and applying the two algorithms to do classification. AUC, Classification Accuracy (CA), F1 Score, Precision, Recall, and MCC were among the important metrics used to assess the models. According to the findings, Inception V3 performed better than VGG-16 and VGG-19 on every parameter, with Logistic Regression obtaining nearly flawless scores (AUC = 1.000, CA = 0.987, F1 = 0.987). Although it was marginally less effective than Logistic Regression (AUC = 0.998, CA = 0.943, F1 = 0.946), SVM also did well with Inception V3. The feature extraction techniques that performed the worst were VGG-16 and SVM in particular (CA = 0.890, F1 = 0.891). These results highlight the effectiveness of Logistic Regression for classification in this setting and the improved multi-scale feature extraction capabilities of Inception V3. This study demonstrates how effective classifiers and cutting-edge CNN architectures, such as Inception V3, may be combined to automatically classify winged animals.
Color Psychology on Book Covers: An Analysis of Visual Preferences Across Adult and Children’s Books for Enhanced Audience Targeting at Sampoerna Academy Grand Pakuwon Surabaya Arum Karisma Nadya Lashita; Imam Yuadi
Jurnal Ilmu Perpustakaan Vol 15, No 1 (2026): April 2026
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/jip.v15i1.77-100

Abstract

Book covers play a crucial role in capturing readers’ attention and shaping initial perceptions, making color an essential element in influencing emotional engagement and guiding audience targeting across children’s and adult literature. Color functions as a visual communication tool that reflects the tone, theme, and intended readership, while also aligning with psychological and developmental preferences. The objective is to identify how color attributes such as brightness, saturation, and dominant hues differentiate book categories and support effective visual classification. A quantitative content analysis approach is applied using 6,093 book cover images collected from the Sampoerna Academy Library. Data extraction is conducted using Python libraries, including OpenCV and NumPy, to measure RGB (Red, Green, Blue), HLS (Hue, Lightness, Saturation), and colorfulness attributes. The analysis is supported by visualization techniques such as Scatter Plot and Radial Visualization (RadViz) through Orange software to explore relationships between color variables and book categories. The results reveal clear visual distinctions between the two categories. Children’s book covers predominantly appear in higher brightness and color intensity ranges, with strong associations to warm and vibrant hues such as red, yellow, and orange. In contrast, adult book covers are more widely distributed across lower brightness levels and are associated with cooler and more subdued tones such as blue, green, and purple. Some overlap is identified, indicating that certain colors can function across categories depending on context. The findings confirm that color attributes serve as reliable indicators for audience targeting and visual classification. Future research is recommended to integrate additional design elements, including typography and layout, to provide a more comprehensive understanding of book cover design.
Classification of Roronoa Zoro Anime, Cosplay, and Action Figure Images Using VGG16 and Inception V3 with Logistic Regression and Support Vector Machine to Improve Popular Culture Object Recognition Denaldy Oktavian Noor Rizki; Imam Yuadi
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.2026.7.3.5516

Abstract

The diversity of visual representations of anime characters across anime scenes, cosplay photographs, and action figure images poses challenges for automated image classification due to variations in pose, lighting, background, and visual style. This study aims to develop a robust image classification system for the character Roronoa Zoro using deep learning–based feature extraction combined with classical classification algorithms. The method employs VGG16 and Inception V3 as feature extractors, followed by classification using Logistic Regression and Support Vector Machine. The dataset comprises three classes (anime, cosplay, and action figure), processed through image resizing, normalization, and data augmentation. Performance was evaluated using accuracy, F1-score, Area Under Curve (AUC), Matthews Correlation Coefficient (MCC), confusion matrix, silhouette plot, and multidimensional scaling. The experimental results show that Inception V3 combined with Logistic Regression achieved the best performance, with an AUC of 0.993, accuracy of 95.7%, F1-score of 0.957, and MCC of 0.935, outperforming VGG16 with Logistic Regression, which achieved 91.7% accuracy and an AUC of 0.986. Visualization-based evaluation indicates that Inception V3 produces more separable feature representations, particularly in distinguishing cosplay images from anime and action figure categories. This research demonstrates the effectiveness of multi-model feature extraction and classification for improving recognition performance in character-based image classification tasks and contributes empirically to the application of hybrid deep feature–machine learning approaches in computer vision.
ANALYSIS OF BOOK BORROWING PATTERNS IN STUDENTS OF THE FACULTY OF ECONOMICS AND BUSINESS, NATIONAL UNIVERSITY USING APRIORI ALGORITHM Chyntia Shafa; Imam Yuadi
JIPI (Jurnal Ilmu Perpustakaan dan Informasi) Vol 11, No 1 (2026)
Publisher : Progam Studi Ilmu Perpustakaan UIN Sumatera Utara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/jipi.v11i1.28679

Abstract

This study aims to analyze book borrowing patterns at the Library of the Faculty of Economics and Business, National University using the Apriori algorithm. The analysis was conducted on borrowing transaction data to identify relationships among book categories that are frequently borrowed together. The dataset consisted of 68 borrowing transactions covering 11 book categories. Although the number of transactions was relatively limited, the data were selected based on the availability of complete borrowing records during the observation period and were considered sufficient to identify initial borrowing patterns. The results reveal several significant patterns. The “Management” category obtained the highest support value of 40%, indicating that it was the most frequently borrowed category, while the rule “Management → General Management” achieved a confidence value of 70%, showing a strong tendency for both categories to be borrowed together. These findings demonstrate that the Apriori algorithm can effectively identify user borrowing preferences from circulation data. This study contributes to the development of data mining applications in library science, particularly in the use of association analysis to support evidence-based library management. The findings may assist librarians in optimizing collection arrangement, developing recommendation systems, and improving collection development strategies. Furthermore, this study highlights the potential of transaction data analysis as a practical approach for understanding user information needs in academic libraries.
ENHANCING JOB FIT PREDICTION IN CORPORATIONS – A COMPARATIVE MACHINE LEARNING STUDY UTILIZING GRADIENT BOOSTING Bondan Ari Wijaya; Imam Yuadi
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 4 (2026): MARCH
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20035466

Abstract

The test results demonstrated how well the Gradient Boosting model could predict outcomes, with the model achieving the best performance metrics, such as an overall accuracy of 98% with 10-fold cross-validation. using group learning techniques to evaluate job fit. This remarkable performance was attained despite the organizational dataset's inherent class imbalance. Crucially, the model showed constant effectiveness in every aspect of job fit. The majority class, Perfect Match (98.8%), is divided into groups based on the difference between PeG and PoG. The minor groups, Overqualified (96.2%) and Underqualified (96.5%), are also divided into groups with strong accuracy and memory. "Jenjang - Main Grp "Text" and "PeG" are the two most important things that can tell you work fit," according to the feature importance analysis. These data give us a solid, objective basis for future talent management and placement decisions by clearly demonstrating that there are distinct, data-driven patterns in placing people in jobs at a company. Machine Learning, Job Fit, Human Resources, Gradient Boosting and Personnel Analytics.
Analisis Visual dan Machine Learning untuk Mengukur Validitas Dokumen Akademik Perpustakaan: Studi pada Data Turnitin Wardani, Hesti Ari; Yuadi, Imam
Jurnal Pustaka Ilmiah Vol 11, No 1 (2025): Jurnal Pustaka Ilmiah
Publisher : Universitas Sebelas Maret Library

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jpi.v11i1.98828

Abstract

The issue of plagiarism in academic works is a major concern in higher education as it can undermine academic integrity. This study aims to analyze the distribution of Turnitin results in academic library documents from various study programs at Universitas Nasional, Jakarta. Using a data visualization approach and machine learning algorithms, this research explores the relationship between Turnitin scores and document validity status. The methods used include data visualization through Scatter Plots, Violin Plots, and Box Plots, with these visualizations utilizing Orange Data Mining as the data processing method. Additionally, a logistic regression algorithm is applied to classify documents based on Turnitin scores. Furthermore, the Chi-Square statistical test is implemented to evaluate the significance of the relationship between Turnitin results and document validity status. The findings of this study indicate that Turnitin scores exhibit significant distribution differences among study programs, with notable disparities between valid and invalid documents. Documents from certain study programs tend to have dominant scores in the 20-50% range, which serves as a critical threshold in determining document validity. This study provides in-depth insights into the patterns of academic document validity and offers a data-driven approach to improving the quality of academic evaluation in higher education. Additionally, this research is expected to serve as a foundation for academic policies in strengthening plagiarism detection systems, increasing transparency in evaluation, and promoting the development of more accurate and efficient document validation methods. However, this study has limitations regarding the scope of data used, particularly in terms of study program representation and external factors that may influence Turnitin scores. Further research is needed to examine pedagogical aspects and academic policies that contribute to variations in Turnitin scores across different study programs.
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