Abstract
The Employees are the most vital element of the company as they had a big contribution and involved almost for all section on how the company will go up and down. Employees and the company affect the efficiency, effectiveness,designing, producing goods and services, oversee the quality, market products, allocating financial resources, and determines the overall goals and strategies of the organization. Therefore, organizations need accurate information and sustainable in order to get suitable candidates with the qualifications of the organization. Model algorithms are widely used in research related to the employee is C4.5 decision tree classification model. Advantages of using adecision tree classification models are the result of the decision tree is simple and easy to understand. Many studies using the method of decision tree and classification tree in predicting the employees selection but results theaccuracy of the resulting value is less accurate. In this study created a C 4.5 Algorithm model and C 4.5 Algorithm model based on particle swarm optimization to get the rule in employees selection and provide a more accuratevalue of accuracy. After testing C 4.5 algorithm model based on particle swarm optimization, Implementation of particle swarm optimization can produce accuracy value of C 4.5 algorithm model from 80.80 % to 85.20 % and theAUC value from 0.878 to 0.891. By the formation the model selection of employees, the company can be helped for employee selection.
Highlights
Karyawan adalah unsur yang paling vital dalam organisasi yang berperan besar bagi kesuksesan organisasi
Model algorithms are widely used in research related to the employee is C4.5 decision tree classification model
Advantages of using a decision tree classification models are the result of the decision tree is simple and easy to understand
Summary
Karyawan adalah unsur yang paling vital dalam organisasi yang berperan besar bagi kesuksesan organisasi. Karena penelitian seleksi karyawan pernah dilakukan beberapa peneliti sebelumnya banyak menggunakan klasifikasi decision tree C 4.5 dan hasil akurasinya masih kurang akurat, maka pada penelitian ini akan diukur akurasinya untuk proses seleksi penerimaan karyawan menggunakan algoritma klasifikasi decision tree yang tepat dengan kriteria atribut data yang digunakan berbeda. Akan tetapi Particle Swarm Optimization akan digunakan sebagai algoritma optimasi dalam seleksi penerimaan karyawan untuk mencapai tingkat akurasi yang lebih baik. Penelitian dilakukan oleh Windy Julianto, Rika Yunitarini, Mochamad Kautsar Sophan, tentang penilaian kinerja karyawan menggunakan Algoritma C 4.5 dihitung dengan menggunakan teknik Confusion Matrix dengan nilai precission sebesar 58,33 %, Recall 82,35 %, Accuracy 91,39 % dan Error Rate sebesar 8,61 %. Penelitian ini membahas tentang penggunaan teknik data mining untuk membangun model klasifikasi dalam mempresiksi kinerja karyawan. Tujuan dari penelitian ini adalah untuk menerapkan Particle Swarm Optimization untuk meningkatkan akurasi dari algoritma C 4.5 untuk seleksi penerimaan karyawan
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