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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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Peningkatan Produktivitas Ibu-Ibu Janda dan Pensiunan Melalui Pelatihan Manajemen Pengetahuan Tacit dan Explicit Tri Atmi, Ragil; Mutia, Fitri; Yuadi, Imam; Srimulyo, Koko; Kartika Sari, Della; Nur Muhammad, Rizqi
International Journal of Community Service Learning Vol. 7 No. 1 (2023): February 2023
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/ijcsl.v7i1.54751

Abstract

Desa Mojorejo Kecamatan Modo pada kurun waktu tiga tahun terakhir mengalami peningkatan jumlah perempuan berstatus janda dan purna tugas Pegawai Negeri Sipil (PNS) maupun swasta. Status tersebut membawa dampak pada penurunan kualitas hidup seperti kesehatan fisik, perekonomian keluarga, maupun kesehatan psikis. Atas dasar permasalahan tersebut, diadakan kegiatan pelatihan manajemen pengetahuan tacit dan explicit yang mengoptimalkan pengelolaan ide, gagasan, wawasan, pengalaman, dan pengetahuan agar menjadi manfaat bagi lingkungan sekitar. Tujuan dari kegiatan ini antara lain (1) Memberikan pelatihan pengelolaan pengetahuan tacit ke explicit; (2) Memaksimalkan potensi diri baik bakat maupun minat dari ibu berstatus janda dan purna tugas dari PNS maupun swasta; (3) Mengkodifikasi pengetahuan masyarakat sasaran agar tidak musnah. Metode yang digunakan dalam penelitian ini menggunakan metode pelatihan dan pembinaan secara intensif. Kegiatan pelatihan yang dilaksanakan berupa pemahaman terkait pentingnya mengelola pengetahuan yang dimiliki, memberikan metode penyaluran pengetahuan melalui media konten digital berupa video, memberikan pemahaman terkait pembuatan konten video yang informatif dan kreatif, praktik pembuatan video secara individu, dan pelatihan mengunggah konten video melalui media sosial. Peserta sasaran mampu secara mandiri untuk menyebarkan pengetahuan explicit ke dalam media informasi digital sehingga dapat meningkaykan produktivitas ibu-ibu janda dan pensiunan dalam kehidupan sehari-hari.
Stunting Prevention in Indonesia Between 2018-2023 from Scopus: A Bibliometric Study Putri, Selviana Azzira; Yuadi, Imam
TEKNOLOGI MEDIS DAN JURNAL KESEHATAN UMUM Vol 9 No 1 (2025): Medical Technology and Public Health Journal March 2025
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/mtphj.v9i1.5572

Abstract

Stunting remains a significant concern, particularly in Indonesia. This study aims to assess the development of scientific publications related to stunting prevention in the country. A bibliometric analysis method was utilized to identify research trends concerning stunting prevention in Indonesia. Data were collected from the Scopus database and processed using Biblioshiny software from R-Studio. The results indicate a steady increase in publications on stunting prevention in Indonesia from 2018 to 2023, suggesting a rise in research activity during this period. Future research topics may include the relationship between malnutrition and children in Indonesia, poverty, being small for gestational age, immunization, and attitudes toward health.  Keywords: Bibliometric, biblioshiny, indonesia, stunting prevention
Word Cloud Visualization of Media Reactions to USAID Shutdown Rahmadani, Sinta; Yuadi, Imam
MUKADIMAH: Jurnal Pendidikan, Sejarah, dan Ilmu-ilmu Sosial Vol 9, No 1 (2025)
Publisher : Prodi Pendidikan Sejarah Fakultas Keguruan dan Ilmu Pendidikan Universitas Islam Sumatera

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/mkd.v9i1.10777

Abstract

The closure of USAID prompted various reactions by the media, which in turn affected public opinion and ongoing policies. This research investigates how media narratives cover the shutdown of USAID using word cloud visualization to capture themes and sentiments. The research attempts to find: What are the dominant media narratives regarding the shutdown? Using tokenization, stopword elimination, and frequency analysis before generating a word cloud to illustrate prominent words. The data shows that US media focuses on government spending, foreign aid, employment, and diplomatic activity, which all influence the public perception of the shutdown. The study argues that computational text analysis aids in the understanding of media discourse and sentiments on policies, which help policymakers and scholars concerned with public opinion and policy discourse on international aid and development issues. This study advances the field of media by expanding the scope of the study of visual politics and political communication. The analysis reveals that the conversation revolves around government activities, consequences of foreign aid, workforce considerations, and spatial politics, with “funding,” “security,” and “diplomatic” standing out the most. The analysis of the media coverage shows that the shutdown is framed as a political as well as an economic crisis, constructing a narrative that is later used in public discourse and policy discussions. This project adds to the body of work on media by employing visual analysis in the study of political communication and analyzing media framing from a computational perspective.
Analisis Bibliometrik Tentang Persebaran Hoax Di Indonesia Azmi, Muhammad Izharul; Yuadi, Imam
UNILIB : Jurnal Perpustakaan Vol. 16 No. 1 2025
Publisher : Direktorat Perpustakaan Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/unilib.Vol16.iss1.art3

Abstract

Penelitian ini bertujuan untuk menganalisis menggunakan metode bibliometrik mengenai persebaran hoax di Indonesia dengan mengumpulkan data dari database Scopus dan Web of Science dan menggabungkannya menjadi satu dan kemudian menganalisanya menggunakan Biblioshiny. Tujuan dari penelitian ini adalah untuk mencoba memberikan gambaran mengenai tren tentang hoax yang tersebar di Indonesia. Dalam penelitian ini menganalisis penulis yang menulis terkait topik ini, jumlah sitasi dari jurnal, sumber jurnal, instansi yang relevan, WordCloud, TreeMap, dan Thematic Map. Hasil analisis bibliometrik ini diharapkan dapat membantu memberikan pemahaman dan menjadi acuan untuk penelitian yang akan mendatang
Cracking Overtime: Unleashing Machine Learning at PT XYZ with Linear Regression, Neural Networks, and Random Forests Christia, Tifani Dewi; Yuadi, Imam
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5678

Abstract

Excessive overtime at PT XYZ is a significant issue for the organization. Besides the significant financial repercussions, they may also affect employee health and productivity. This research aims to forecast future overtime hours, facilitating strategic planning, mitigating excessive overtime, and developing more effective overtime policies. This study employs an overtime realization dataset encompassing many characteristics that influence overtime determinations. The dataset is partitioned into training and testing data to serve as inputs for the three predictive algorithm models: linear regression, artificial neural network, and random forest. The random forest model demonstrates superior performance, evidenced by a mean squared error (MSE) of 158.78, which is proximate to the actual value. The root mean squared error (RMSE) of 12.601 is lower than that of the other two models, indicating a reduced average prediction error. The mean absolute error (MAE) of 8.931 reflects the average deviation from the actual value, while the mean absolute percentage error (MAPE) of 0.336 indicates a prediction error of 34%. Furthermore, the coefficient of determination (R²) of 0.914 signifies that approximately 91.4% of the variation in overtime hours is accounted for, in contrast to the other models, which accounted for 78.8% and 79.6%, respectively. The results indicate that the random forest model demonstrates superior predictive accuracy compared to the other two algorithms, owing to its capacity to handle non-linear data and outliers. Consequently, the random forest model is advocated as the most efficacious method for forecasting the amount of supplementary working hours in the future.
Understanding Political Narratives: Word Cloud Analysis of Yoon Seok-Yeol's Impeachment Sinta Rahmadani; Imam Yuadi
Journal of Law, Politic and Humanities Vol. 5 No. 4 (2025): (JLPH) Journal of Law, Politic and Humanities
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jlph.v5i4.1427

Abstract

This study uses computational text analysis to analyze political rhetoric in the context of the impeachment of South Korea’s former president and chief prosecutor Yoon Seok-yeol. This Project analyzes statements of ten different international media outlets a month prior to the impeachment held in December 2024 through nexus tools within multi dimensional scaling and word clouds. The study illuminates startling issues South Korea is currently grappling with, and how stark the media’s influence is on public perception by exhibiting political themes such as “impeachment”, “martial law”, “opposition” and “party”. A distance matrix and MDS plot aids in understanding the correlation between public issues, the legal angle and issues regarding partisan divides. Such conversations can now be segmented into core narratives in lieu of these visuals, and Cohen easily be elaborated through computational models which highlight which topic or idea is popular in the public eye. The findings are commensurate with the literature discussing the role of public and media sentiment in impeachment process and suggest the opportunities of coupling qualitative and computer approaches. The research offers a technique on how to evaluate political narratives which would aid in enhancing the communication sought by the Penn State University Department of Communication in the governance of the society and democratic processes.
Membangun kepercayaan publik: visualisasi data interaktif capaian kinerja Kantor Regional IX BKN Jayapura Setiadi, Yusuf; Margono, Hendro; Yuadi, Imam
Jurnal Governansi Vol 11 No 1 (2025): Jurnal Governansi Volume 11 Nomor 1, April 2025
Publisher : Universitas Djuanda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30997/jgs.v11i1.15091

Abstract

This research aims to build public trust in the performance of XYZ Regional Office through interactive data visualization using Power BI. Public trust in government agencies is often influenced by transparency and openness in delivering performance information. In this context, interactive and easy-to-understand data visualization is important to improve public understanding of agency performance achievements. The urgency of this research lies in the need for transparency and public accountability, which can encourage active public participation in monitoring and supporting government performance. This research uses a descriptive method with a quantitative approach. The data used is the performance data of XYZ Regional Office, which is then processed and visualized using Power BI. Interactive data visualization is designed to facilitate users in exploring relevant performance information, with a focus on key indicators of agency achievement. By interpreting data findings, this research identifies factors that influence staffing dynamics, such as the total number of civil servants, retirement proposals, and promotion proposals. The implications of the findings are also discussed to provide relevant recommendations for stakeholders related to strategic staffing decision-making. The results showed that the interactive data visualization created was able to increase public understanding and positive perception of the performance of XYZ Regional Office. The novelty of this research lies in the use of Power BI as an interactive visualization tool in the context of government agencies, which has not been widely applied. This research is expected to be a reference for other government agencies to increase public trust through a data-driven approach and performance transparency.
Implementation of Orange Data Mining for Employee Turnover Prediction of Company X Prayitna, Thomas Wigung Aji; Yuadi, Imam
Eduvest - Journal of Universal Studies Vol. 5 No. 5 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i5.50814

Abstract

Employee turnover presents a significant challenge in Human Resources, particularly for companies operating across broad geographic areas such as Indonesia. High turnover rates can disrupt organizational continuity, increase recruitment costs, and affect overall performance. To mitigate these impacts, companies need to predict employee turnover likelihood accurately. This study uses the Orange Data Mining platform to compare the effectiveness of various machine learning models in predicting employee turnover. The models evaluated in this research include Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (K-NN), Neural Network, Decision Tree, and Logistic Regression. Model performance was assessed using cross-validation, Receiver Operating Characteristic (ROC) analysis, and confusion matrix metrics such as precision, recall, and false positives. The findings reveal that the Naive Bayes model outperforms the other models, demonstrating the highest precision rate and the lowest false positive rate. These results suggest that Naive Bayes offers a reliable and efficient approach to turnover prediction, enabling Human Resource departments to implement proactive retention strategies. This study implies that data-driven decision-making in HR analytics can substantially improve workforce planning and reduce the operational costs associated with high turnover.
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.
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