Random Forest Classifier Dengan Grid Search Untuk Klasifikasi Hasil Psikotes Pada Rekrutmen Dosen dan Tenaga Kependidikan
Abstract
ABSTRACT
Effective selection of educational staff and lecturers requires appropriate evaluation techniques to assess the abilities and potential of candidates. This research recommends using the Random Forest Classifier to classify psychological test results, with optimization using Grid Search and validation via K-Fold Cross Validation. This approach was chosen because of its ability to handle complex and heterogeneous data. Grid Search is used to search for the best parameter combination that improves model performance, while K-Fold Cross Validation ensures generalization reliability. This research used 616 data, consisting of 307 lecturers' and 309 psychological test results educational staff psychological test results. Data is divided into training (80%) and testing (20%) data. Classification report analysis shows an accuracy of 85% for the education staff dataset and 82% for the lecturer dataset.
Keywords: Random Forest Classifier; Grid Research; Psychological Test Results; K-Fold Cross Validation
ABSTRAK
Seleksi tenaga kependidikan dan dosen yang efektif memerlukan teknik evaluasi yang tepat untuk menilai kemampuan serta potensi kandidat. Penelitian ini merekomendasikan penggunaan Random Forest Classifier untuk mengklasifikasikan hasil psikotes, dengan optimasi menggunakan Grid Search dan validasi melalui K-Fold Cross Validation. Pendekatan ini dipilih karena kemampuannya dalam menangani data yang kompleks dan heterogen. Grid Search digunakan untuk mencari kombinasi parameter terbaik yang meningkatkan kinerja model, sementara K-Fold Cross Validation memastikan keandalan generalisasi. Penelitian ini menggunakan 616 data, terdiri dari 307 hasil psikotes dosen dan 309 hasil psikotes tenaga kependidikan. Data dibagi menjadi data pelatihan (80%) dan pengujian (20%). Analisis classification report menunjukkan akurasi sebesar 85% untuk dataset tenaga kependidikan dan 82% untuk dataset dosen.
Kata Kunci: Random Forest Classifier; Grid Research; Hasil Psikotes; K-Fold Cross Validation
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References
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