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Journal : MUKADIMAH: Jurnal Pendidikan, Sejarah, dan Ilmu-ilmu Sosial

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.
HR Analytics for Predicting Job Satisfaction in Hybrid Work Hardevianty, Melissa Yunda; Yuadi, Imam
MUKADIMAH: Jurnal Pendidikan, Sejarah, dan Ilmu-ilmu Sosial Vol 9, No 2 (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.v9i2.12015

Abstract

This research investigates the application of machine learning classification models for predicting employee job satisfaction, considering demographic, professional, and psychosocial aspects. With a secondary dataset obtained from Kaggle, five supervised learning techniques were applied: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting. The best model was considered to be Gradient Boosting as it achieved the highest accuracy and F1 score. The model's explainability was enhanced with LIME. LIME enhanced the model's explainability. Stress, work-life balance, and job tenure were identified as the primary factors of job satisfaction. These results support the Job Demands-Resources (JD-R) theory and highlight the model's effectiveness in the hands of HR professionals. The study highlights the need to achieve a balance between predictive accuracy and explainability to ethically align the use of AI in HR analytics, aiming to enhance the well-being of employees and the effectiveness of organizations.
Co-Authors AA Sudharmawan, AA Alifka Cellina Velby Anastasya, Diva Berta Andini, Aulia Rizqi Anggraini, Pramudya Galuh Suci Artha Rachma Widiastuti Azmi, Muhammad Izharul Baihaqie, Owen Berliani, Kezia Putri Cahyani, Retno Tri 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 Gunarti, Endang Halim, Yunus Abdul Handari Niken Anggraini Hapsari, Ratih Addina Hardevianty, Melissa Yunda Hasna, Dhia Alifia Izdihar Hendrawati, Lucy Dyah Ira Puspitasari Ira Puspitasari Ismi Choirunnisa Prihatini Kartika Sari, Della Kezia Rahmawati Santosa Koko Srimulyo Lathifah, Lathifah Lestari, Santi Dwi Desy Lifindra, Stevanie Aurelia M Kafi Maulana Mahardika, Synthia Amelia Putri Margono, Hendro Marsaa Salsabiila Maulidah, Nofiyah Mayasari, Sentri Indah Mochammad Edris Effendi Nabilla Salsabil Damayanti Zahraa Nainunis, Mas Akhmad Nazikhah, Nisak Ummi Niken Ayu Pratiwi, Bertha Novia, Asradiani Nur Muhammad, Rizqi Nurul Firdausy 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 Raihanzaki, Raka Gading Rosiana, Lidya Rosyani, Widha Sabayu, Brian Sabrina Hartianingrum, Hikmah Sabrina Nur Amalia Safina Innaf Mia Ardelia Santoso, Yuniawan Heru Sari, Tri Kartika Setiadi, Yusuf Sheva Alana Brilianty Sinta Rahmadani Soesantari, Tri Sugihartati, Rahma Suhada, Hofur Tikamidia, Sonia Tri Hadi Wicaksono Triandari, Ayu Ullin Nihaya Unas, Frisca Maria Vilosa, Bias Vivia Adriyanti, Elvetta Wardani, Hesti Ari Wettebossy, Anita Elizabeth Wildan Habibi Yuwinanto, Helmy Prasetyo Zidny, Irvan