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All Journal 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 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 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 Journal of Economics, Business, and Government Challenges MUKADIMAH: Jurnal Pendidikan, Sejarah, dan Ilmu-ilmu Sosial Jurnal Informasi dan Teknologi 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 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 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
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 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 Neuroscience Nursing Physics Public Health Social Sciences Other

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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
Analisis bibliometrik tentang tren penerapan kebijakan kota hijau (green city) Bias Vilosa; Imam Yuadi
Jurnal Pengelolaan Lingkungan Berkelanjutan (Journal of Environmental Sustainability Management) JPLB, Vol 8, No 2 (2024)
Publisher : Badan Kerjasama Pusat Studi Lingkungan (BKPSL) se-Indonesia bekerjasama dengan Pusat Penelitian Lingkungan Hidup IPB (PPLH-IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36813/jplb.8.2.162-178

Abstract

Environmental pollution continues to occur due to the times, so it is necessary to make improvements through sustainable development that is environmentally friendly. One form of support for sustainable development can be realized in the implementation of green city policies. To find out more about the implementation of green city policies, it can be done through analyzing trends in the application of green city policies from 2013 - 2023 in literature review research that has been published in the Web of Science (WoS) and Scopus databases using bibliometric analysis. From the search for scientific articles on green city policies, the data was visualized with the VOSviewer and RStudio applications using the biblioshiny method. The results of the research analysis are; the scope of the most popular topics with 6 (six) green city discussions, topic trends namely urban planning, sustainability, and green city policy, the high average number of citations in 2015 due to world commitment and the green city phenomenon, and the most countries that publish the USA, China, and the UK. Research trends in the implementation of green city policies produce positive data and collaborate with each other.
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.
ANALISIS BIBLIOMETRIK TENTANG NETWORK GOVERNANCE PADA PELAYANAN PUBLIK Fitria Wulandari, Martina; Yuadi, Imam
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.
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 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 Abdul Halim, Yunus Alifka Cellina Velby Anastasya, Diva Berta Andini, Aulia Rizqi Anggraini, Pramudya Galuh Suci Anita Elizabeth Wettebossy Artha Rachma Widiastuti Azmi, Muhammad Izharul Berliani, Kezia Putri Bias Vilosa Christia, Tifani Dewi Condro Rahino Mustikaning Pawestri Dama Putri, Kania Dewanty, Alifia Kaltsum Endang Gunarti Fadilia Rinarwastu, Fadilia Febriano, Rizki Dwi Ferdiansah, Gilang Fitri Mutia, Fitri Fitria Wulandari, Martina Frisca Maria Unas Gunarti, Endang Halim, Yunus Abdul Handari Niken Anggraini Hapsari, Ratih Addina Hardevianty, Melissa Yunda Hasna, Dhia Alifia Izdihar Hendrawati, Lucy Dyah Hikmah Sabrina Hartianingrum 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 Mahardika, Synthia Amelia Putri Margono, Hendro Marsaa Salsabiila Mas Akhmad Nainunis Maulidah, Nofiyah Mochammad Edris Effendi Nabilla Salsabil Damayanti Zahraa Nazikhah, Nisak Ummi Niken Ayu Pratiwi, Bertha Novia, Asradiani Nur Muhammad, Rizqi Nurul Firdausy Owen Baihaqie Prasetyo Yuwinanto, Helmy Prasyesti Kurniasari, Meinia Prayitna, Thomas Wigung Aji Putra, Dwi Permana Putri Kinanti, Novrianti Putri, Selviana Azzira Ragil Tri Atmi, Ragil Tri Rahmadani, Sinta Raka Gading Raihanzaki Rosiana, Lidya Rosyani, Widha Sabayu, Brian Safina Innaf Mia Ardelia Santoso, Yuniawan Heru Sari, Tri Kartika Setiadi, Yusuf Sheva Alana Brilianty Sinta Rahmadani Soesantari, Tri Sugihartati, Rahma Suhada, Hofur Tri Atmi, Ragil Tri Hadi Wicaksono Ullin Nihaya Vivia Adriyanti, Elvetta Wardani, Hesti Ari Wildan Habibi Yuwinanto, Helmy Prasetyo