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Enhancing Academic Staff Performance Prediction in Higher Education: A Data-Driven Hybrid Machine Learning Approach Triyoga, Khavid Wasi; Laksono , Pringgo Widyo; Damayanti, Retno Wulan
International Journal of Electronics and Communications Systems Vol. 5 No. 2 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i2.28670

Abstract

Enhancing the performance of academic staff is a key factor in maintaining institutional productivity and service quality in higher education. This study aims to develop a data-driven hybrid model capable of enhancing performance management effectiveness through the integration of predictive intelligence and evidence-based managerial recommendations. This model combines K-Means Clustering, Data Envelopment Analysis (DEA), Exploratory Factor Analysis (EFA), and Random Forest to analyze digital attendance data, service satisfaction surveys, and performance records from 2022 to 2024. This research was conducted at the Faculty of Teacher Training and Education, Sebelas Maret University (FKIP UNS) as a representative case study. The test results show that the predictive model achieved 92 percent accuracy and an F1-score of 0.90 in classifying low performance risk. A strong negative correlation was found between attendance tardiness and service satisfaction levels. DEA analysis identified 32 percent inefficiency in resource utilization, while EFA revealed three dominant latent factors: compliance with SOPs (0.82), academic productivity (0.89), and psychosocial well-being (0.93). Intervention cluster management (SOP training and workload reduction) resulted in a 28 percent increase in SOP compliance. These findings indicate that the integration of hybrid machine learning with efficiency and factor analysis can be an effective framework for data-driven improvement in academic staff performance.
Integration of Service Quality and Sustainability on Revisit Intention Dwi, Doni Nugroho; Pujiyanto, Eko; Damayanti, Retno Wulan
Jurnal Kesehatan Vokasional Vol 10, No 4 (2025): November
Publisher : Sekolah Vokasi Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jkesvo.110324

Abstract

Background: Hospital service quality played an important role in improving patient satisfaction and loyalty. However, sustainability, particularly the triple bottom line, became a new challenge in modern hospital management. Several studies demonstrated significant influences of service quality and sustainability on patients' intentions to revisit, yet studies that systematically integrated both aspects, especially in specialist hospitals, remained limited.Objective: This study aimed to examine the relationship between service quality dimensions and sustainability in relation to patient satisfaction and revisit intention, as well as to formulate future research agendas concerning sustainable healthcare services.Methods: Literature was collected using the PRISMA method from the Scopus and Google Scholar databases, covering publications from 2019 to 2025.Results: The analysis indicated that most of the reviewed literature employed the PLS-SEM method to examine the relationship between service quality and patient satisfaction. The findings consistently showed that service quality dimensions affected patient satisfaction, while sustainability dimensions contributed significantly to service perception and patients’ revisit intention. However, studies examining the integration of service quality and the triple bottom line remained scarce.Conclusion: The integration of service quality and sustainability emerged as an important aspect in improving service quality and patient retention. This study provided direction for future research and hospital practices aiming to develop value-oriented service systems with long-term and operational sustainability.