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All Journal Jurnal Manajemen Pendidikan dan Ilmu Sosial (JMPIS) Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Advances in Human Resource Management Research
Farida Yuliaty
Universitas Sangga Buana YPKP, Bandung, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences Other

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Analisis SDM dan Pembelajaran Mesin untuk Prediksi Perputaran Karyawan di Startup: Tinjauan Literatur Sistematis Deden Abdul Wahid; Farida Yuliaty; Kosasih; Vip Paramarta
Jurnal Manajemen Pendidikan dan Ilmu Sosial Vol. 7 No. 2 (2026): Jurnal Manajemen Pendidikan dan Ilmu Sosial (Februari - Maret 2026)
Publisher : Dinasti Review

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jmpis.v7i2.7877

Abstract

Perputaran karyawan tetap menjadi tantangan kritis bagi startup, di mana retensi talenta secara langsung memengaruhi kelangsungan dan pertumbuhan organisasi. Tingkat perputaran karyawan yang tinggi menimbulkan biaya finansial yang signifikan, diperkirakan mencapai 50-200% dari gaji tahunan, dan mengganggu kelangsungan organisasi, terutama merugikan dalam lingkungan startup yang terbatas sumber dayanya. Tinjauan literatur sistematis ini mengkaji penerapan analitik SDM dan pendekatan machine learning dalam memprediksi perputaran karyawan, dengan penekanan khusus pada konteks startup. Mengikuti pedoman PRISMA, kami melakukan pencarian komprehensif di enam basis data akademik utama (IEEE Xplore, ACM Digital Library, ScienceDirect, Springer, Emerald Insight, dan arXiv), menganalisis 39 studi yang telah direview oleh rekan sejawat yang diterbitkan antara tahun 2021-2025. Temuan kami menunjukkan bahwa metode ensembel, khususnya algoritma Random Forest dan Gradient Boosting, secara konsisten mencapai akurasi prediksi 88-99% di berbagai konteks organisasi. Fitur prediktif utama meliputi kepuasan kerja (skor penting 0.87), kompensasi relatif terhadap standar pasar (0.79), peluang pengembangan karier (0.74), keseimbangan kerja-kehidupan (0.68), dan untuk startup secara khusus, persepsi keamanan kerja (0.54). Tinjauan ini mensintesis kerangka kerja implementasi, mengidentifikasi praktik terbaik metodologis termasuk strategi implementasi bertahap, dan mengusulkan model adaptif tujuh tahap yang sesuai untuk lingkungan startup dengan sumber daya terbatas. Hasil menunjukkan bahwa model yang dapat diinterpretasikan dikombinasikan dengan rekayasa fitur strategis memungkinkan startup untuk menerapkan intervensi retensi proaktif sambil mempertahankan standar etika dalam pengambilan keputusan algoritmik. Tinjauan ini memberikan panduan praktis bagi praktisi startup dan mengidentifikasi celah penelitian kritis yang memerlukan penyelidikan lebih lanjut di masa depan.
THE EFFECT OF SERVICE COMPENSATION, MOTIVATION, AND DISCIPLINE ON WORK PRODUCTIVITY AND ITS IMPLICATIONS ON SERVICE QUALITY (Study at Malingping Regional Hospital, Banten Province) Yusup Erisyadi; Farida Yuliaty; Kosasih; Assoc. Prof. Dr. Dr. H. Sumeidi Kadarisman, S.E., M.M; Prof. Dr. VIP Paramarta; Drs., M.M5, Fittiana
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 6 No. 3 (2026): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20682606

Abstract

Improving the quality of hospital services is greatly influenced by employee productivity. Human resource management factors such as compensation for services, work motivation, and work discipline are crucial elements that can influence productivity and the quality of healthcare services provided to the public. This study aims to analyze the influence of service compensation, motivation, and discipline on work productivity and their implications for service quality at Malingping Regional General Hospital, Banten Province. The study used a quantitative approach with a survey method. Data were collected through questionnaires distributed to 193 hospital employee respondents. Data analysis was performed using path analysis with the help of SPSS version 26. The results of the study indicate that motivation and discipline have a positive and significant effect on employee work productivity, while service compensation does not have a significant partial effect on work productivity. Simultaneously, compensation, motivation, and discipline have a significant effect on work productivity. Furthermore, work productivity has a significant effect on service quality. The coefficient of determination indicates that the variables of compensation, motivation, discipline, and productivity are able to explain 94.1% of the variation in service quality. The results of this study indicate the importance of strengthening employee work motivation and discipline policies to improve the quality of hospital services.
Global Trends in Smart Healthcare and Smart Hospital Research: A Bibliometric and Systematic Literature Review Approach Bahar Sangkur Gusasih; Faradilla Savitri Prasetyawati; Rafi Yasnova; Vip Paramarta; Farida Yuliaty
Advances in Human Resource Management Research Vol. 4 No. 2 (2026): February - May
Publisher : Yayasan Pendidikan Bukhari Dwi Muslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60079/ahrmr.v4i2.880

Abstract

Purpose: This study analyzes global research trends, intellectual structures, collaboration networks, technological themes, and future directions in smart healthcare and smart hospital research. Research Method: A bibliometric analysis combined with a Systematic Literature Review (SLR) based on the PRISMA protocol was employed. From 169 records identified in Scopus, ScienceDirect, Wiley Online Library, and Google Scholar, 26 international journal articles published between 2020 and 2025 met the inclusion criteria. VOSviewer was used to map keyword co-occurrence, thematic clusters, collaboration patterns, and temporal evolution of research. Results and Discussion: The findings indicate a substantial increase in smart healthcare publications, particularly during 2024–2025. Four dominant research clusters emerged: (1) artificial intelligence and healthcare analytics, (2) Internet of Medical Things (IoMT) and connected healthcare systems, (3) smart hospitals and healthcare information systems, and (4) digital twins, cybersecurity, sustainability, and healthcare governance. The results also reveal growing international and interdisciplinary collaboration among scholars in healthcare, engineering, computer science, and information systems. The overlay visualization shows a thematic shift from healthcare digitalization to intelligent healthcare ecosystems driven by AI, predictive analytics, digital twins, and sustainable innovations. Implications: This study provides a comprehensive overview of smart healthcare development and offers insights for policymakers, healthcare organizations, and technology developers in designing intelligent, secure, and sustainable healthcare systems. Originality: This study integrates bibliometric analysis and SLR to provide a holistic understanding of the evolution, intellectual structure, and future trajectory of smart healthcare and smart hospital research.
Co-Authors Assoc. Prof. Dr. Dr. H. Sumeidi Kadarisman, S.E., M.M Bahar Sangkur Gusasih Deden Abdul Wahid Drs., M.M5, Fittiana Faradilla Savitri Prasetyawati Kosasih Prof. Dr. VIP Paramarta Rafi Yasnova Vip Paramarta Yusup Erisyadi