An Exploration of the Work Performance of Educators in Transformative Schools: Leveraging Machine Learning for Performance Insights


Rakhmad Maulidi(1); Jozua Ferjanus Palandi(2); Bagus Kristomoyo Kristanto(3); Laila Isyriyah(4); Rizky Rahmatullah(5); Puput Dani Prasetyo Adi(6*); Akio Kitagawa(7);

(1) Universitas Telkom
(2) Universitas Bhinneka Nusantara
(3) Universitas Bhinneka Nusantara
(4) Universitas Bhinneka Nusantara
(5) Kanazawa University
(6) Badan Riset dan Inovasi Nasional
(7) Kanazawa University
(*) Corresponding Author

  

Abstract


Education has gone through various phases, and entered the transformative school mode which can be said to change the existing order of the previous schooling system or procedures, because many modes can be done in the transformative school, students can learn in school buildings or classes, or in the field or real industry or the real world of work, with the introduction of a wider and more complex world, this is one of them. This research tries to create and analyze transformative schools in 3 algorithms, namely regression algorithms, classification algorithms, and clustering algorithms that provide a detailed analysis of the results of the analysis of transformative schools currently promoted by the government. from the results of the analysis raises performance conclusions, and in this phase a conclusion can be drawn whether the Transformative school is able to provide answers about the performance of teachers, students, teacher education levels, school locations, number of students, learning methods, or any paramaters that can provide detailed and detailed answers to get performance analysis from Machine Learning, and Work Performance of teachers in Transformative schools with precision. Quantitatively, the value of performance is determined by innovation by 43.2%, followed by technological capabilities and collaboration, 27.9% and 17.2% respectively. and based on cluster level, cluster 3 is the best with 118 educators, cluster 0, 127 educators with high innovators, and cluster 2, 126 educators, and cluster 1 with 129 educators. and from the paradox of transformative practices 30.6% are high Adopters


Keywords


Educator Work Performance; Transformative Schools; Machine Learning; Performance Analysis; Educational Data Analytics

  
  

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doi  https://doi.org/10.33096/ilkom.v18i1.2358.109-125 		  

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Copyright (c) 2026 Rakhmad Maulidi, Jozua Ferjanus Palandi, Bagus Kristomoyo Kristanto, Laila Isyriyah, Rizky Rahmatullah, Puput Dani Prasetyo Adi, Akio Kitagawa

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