Model Review Curriculum Vitae Pada Seleksi Administrasi Dosen It Telkom Purwokerto Menggunakan Metode Long Short Term Memory (LSTM)

Risa, Riski Amalia (2023) Model Review Curriculum Vitae Pada Seleksi Administrasi Dosen It Telkom Purwokerto Menggunakan Metode Long Short Term Memory (LSTM). Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

A company will develop human resources by recruiting new employees according to company needs to achieve more profitable company goals and can develop into a larger company. Telkom Purwokerto institute of Technology is one of the universities in Central Java whose track record is experiencing rapid growth from year to year. This college was founded in 2002 which was named the Sandhy Putra Telkom Telecommunications Academy. In an effort to achieve a better university, the Human Resources Department at the Tellkom Purwokerto Institute of Technology conducts a search for new talented and qualified lecturers every year. However, in the process of finding new lecturer candidates who match the qualifications, they must go through a series of selection processes that take a long time. In addition, in the process of finding new lecturers, the staff of the Human Resources section of ITTP still uses manual methods to review the curriculum vitae of new lecturer candidates. The recruitment process, which is still manual, causes a lack of effectiveness and efficiency in the allocation of time for a Human Resource (HR), thus having an impact on slowing down the process of finding new lecturer candidates at the administrative stage. This research aims to build a CV review model that can streamline time in the administrative stage process. The dataset used in this study was sourced from the Human Resources Section at Telkom Institute of Technology Purwokerto with a collection of CV datasets of candidates who passed and CVs have not been declared passed at the administrative stage selection. The method used by converting the CV file from PDF into a text form is then converted the cv dataset from text to vector form using a tokenizer, then implementing the Long Short Term Memory (LSTM)) algorithm model. The result of the LSTM algorithm model testing process. The model is built by creating two experiments between the use of the smallest dataset and the longest dataset. This model can predict whether a candidate's CV passes or not at the administrative stage with 99% testing accuracy with a bias of 0.04%. Keywords: Curiculum Vitae, Human Resource, Telkom Institute of Technology Purwokerto, Recurrent Neural Network

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