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Abstrak :
Genius has no race. Strength has no gender. Coruage has no limit. The phenomenal true story of the black female mathematicians at NASA whose calculations helped fuel some of America's greatest achievements in space. Before John Glenn orbited the earth or Neil Armstrong walked on the moon, some of the brightest minds of their generation, known as 'human computers', used pencils and adding machines to calculate the numbers that would launch rockets, and astronauts, into space. Starting in World War II and moving through to the Cold War, the civil rights movement, and the space race, Hidden Figures follows the interwoven accounts of Dorothy Vaughan, Mary Jackson, Katherine Johnson, and Christine Darden, four African American women who participated in some of NASA's greatest successes. It chronicles their careers over nearly three decades they faced challenges, forged alliances, and used their intellect to change their own lives, and their country's future. It is a powerful and revelatory tale of race, discrimination and achievement in the modern world. Starting in World War II and moving through to the Cold War, the civil rights movement, and the space race, [this book] follows the interwoven accounts of Dorothy Vaughan, Mary Jackson, Katherine Johnson, and Christine Darden, four African American women who participated in some of NASA's greatest successes. It chronicles their careers over nearly three decades they faced challenges, forged alliances, and used their intellect to change their own lives, and their country's future.

Abstrak :
PT Perusahaan Gas Negara (PGN) Tbk merupakan perusahaan yang memiliki pegawai sebanyak 1.383 orang pada Januari 2020 berdasarkan hasil wawancara dengan divisi Human Capital Management (HCM). Pengelolaan pegawai di PT PGN Tbk dibawah Direktorat SDM dan Umum yaitu pada divisi HCM. Salah satu departemen di HCM yaitu Human Capital Business Partner (HCBP) yang mempunyai tugas untuk memutasi, rotasi dan promosi. Tingginya frekuensi mutasi dan rotasi pegawai sebanyak 1.323 orang pada tahun 2018 dan 933 orang pada tahun 2019, menyebabkan sulitnya mendapatkan kandidat yang sesuai. PT PGN Tbk mempunyai aplikasi untuk mengelola data pegawai yaitu aplikasi HRMS (Human Resources Management System). Penelitian memiliki tujuan untuk menentukan model prediksi kandidat pegawai untuk menempati kekosongan jabatan di PT PGN Tbk menggunakan data 2017-2020 yang bersumber dari aplikasi HRMS. Penelitian ini menggunakan pendekatan mixed method. Pengumpul data menggunakan wawancara dengan analyst career & succession planning di PT PGN Tbk dan observasi terhadap aplikasi HRMS PT PGN Tbk yang berbasiskan Oracle Versi-12. Data dianalisis dengan membandingkan metode K-Nearest Neighbor (KNN), Naïve Bayes dan Random Forest untuk mencari model terbaik dalam prediksi pegawai. Hasil dari penelitian ini diharapkan dapat memberikan kontribusi terhadap PT PGN Tbk mengenai model prediksi untuk dapat membantu mengurangi subjektifitas dalam pemilihan kandidat melalui analisis data pada Aplikasi HRMS PT PGN Tbk. Hasil pada penelitian ini menunjukan bahwa metode Random Forest memiliki tingkat akurasi tertinggi yaitu sebesar 99% melalui uji validasi silang menggunakan 10-fold cross validation. ......PT Perusahaan Gas Negara (PGN) Tbk is a company with 1,383 employees as of January 2020 based on interviews with the Human Capital Management (HCM) division. Employee management at PT PGN Tbk under the Directorate of Human Resources and General Affairs, namely in the HCM division. One of the departments in HCM is Human Capital Business Partner (HCBP) which has the task to mutate, rotation and promotion. The high frequency of mutations and rotation of employees as many as 1,323 people in 2018 and 933 people in 2019, causing difficulty in getting suitable candidates. PT PGN Tbk has an application to manage employee data, namely the HRMS (Human Resources Management System) application. The research aims to determine the predictive model of employee candidates to occupy the vacancy at PT PGN Tbk using 2017-2020 data sourced from the HRMS application. This research uses mixed method approach. Data collectors use interviews with analyst career & succession planning at PT PGN Tbk and observation of PT PGN Tbk HRMS application based on Oracle Version-12. The data were analyzed by comparing K-Nearest Neighbor (KNN), Naïve Bayes and Random Forest methods to find the best model in employee prediction. The results of this research are expected to contribute to PT PGN Tbk regarding prediction model to be able to help reduce subjectivity in candidate selection through data analysis in PT PGN Tbk HRMS Application. The results in this research show that Random Forest method has the highest accuracy rate of 99% through cross validation test using 10-fold cross validation.