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All Journal Jurnal Ekonomi Sosiohumaniora JAM : Jurnal Aplikasi Manajemen AdBispreneur Jurnal Pariwisata Syntax Literate: Jurnal Ilmiah Indonesia JMBI UNSRAT (Jurnal Ilmiah Manajemen Bisnis dan Inovasi Universitas Sam Ratulangi) YUME : Journal of Management Aptisi Transactions on Management Journal of Humanities and Social Studies Aptisi Transactions on Technopreneurship (ATT) Turast: Jurnal Penelitian dan Pengabdian Jurnal Ilmu Manajemen Jurnal Ilmiah Manajemen Kesatuan RESLAJ: RELIGION EDUCATION SOCIAL LAA ROIBA JOURNAL Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences International Journal Of Science, Technology & Management (IJSTM) Jurnal Dharma Bhakti Ekuitas International Journal of Social Service and Research PRIMA : Portal Riset dan Inovasi Pengabdian Masyarakat Prosiding Konferensi Nasional PKM-CSR International Journal of Science and Society (IJSOC) Jurnal Pengabdian Masyarakat Akademisi Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) Return : Study of Management, Economic and Bussines Eduvest - Journal of Universal Studies eProceedings of Management Enrichment: Journal of Multidisciplinary Research and Development Jurnal Kesehatan Pertiwi International Journal of Indonesian Business Review International Journal of Economics, Business and Innovation Research Multidisciplinary Indonesian Center Journal Journal of Multidisciplinary Academic and Practice Studies Global Academy of Business Studies Nucleus Journal
Dian Indiyati
Universitas Telkom
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Talent Performance Analysis Using People Analytics Approach Fahreza Nasril; Dian Indiyati; Gadang Ramantoko
Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences Vol 4, No 1 (2021): Budapest International Research and Critics Institute February
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v4i1.1585

Abstract

The purpose of this study was to answer the research question "How is the prediction of Talent Performance in the following year with the application of People Analytics?" and knowing the description of employees who are potential talents, the resulting performance contributions, to the description of the development and retention efforts needed by Talent in order to be able to maintain their future performance and position as Talents compared to the previous People Analytics method using predictive analysis, namely prediction of Talent Performance in the year next. In this study, data analysis using the Multivariate Logistic Regression method is used to get the Prediction of the Performance of Talents who become the object of research in the form of individual performance quickly and precisely in accordance with the patterns drawn by individual Performance score data in previous years. And can provide insight regarding the projected strategies that need to be done to maintain the improvement of individual talent performance in the years of the assessment period. It also helps management in making decisions about the right Talent development program and determining which Talents are priorities. The population in this study were the talents of employees of PT. Angkasa Pura II (Persero) with a managerial level consisting of: Senior Leader, Middle Leader, and First Line Leader who has a Person Grade (PG) range of 13 to 21. The sample used is Middle Leader level talent with specified criteria and through a process data cleansing. The results of this study indicate that the variable that significantly affects the performance of the following year is the performance of the previous 2 years. Then prediction analysis can be done using these independent variables with the Multinomial Logistic Regression method, and to get prediction results with better accuracy can be done by the Random Forest method.
Talent Acquisition Implementation with People Analytic Approach Atyoko Utomo; Dian Indiyati; Gadang Ramantoko
Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences Vol 4, No 1 (2021): Budapest International Research and Critics Institute February
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v4i1.1584

Abstract

The current human resource (HR) fulfillment conditions in this company are still quite low. This can be seen from the percentage of HR fulfillment of approximately 60% of the total HR needs. The strategy of fulfilling human resources through the recruitment and selection process must be done quickly and optimally. The problem that arises is related to the optimization of the talent acquisition process carried out, so that the results obtained are in accordance with the target and have quality that meets the required. In this study, data analysis was used using the random forest method. The method is used to develop a model that can predict the pass level of participants in recruitment and selection quickly and precisely in accordance with the profile of each participant, and can provide insight on the projected achievement of individual performance on each participant if passed at the company, to assist management in making decisions about the participants accepted in the recruitment and selection process. The data population used is data on recruitment and selection participants in 2018. To carry out the process of predicting the graduation rate of prospective employees, data for prospective employees who register for the recruitment and selection process will be used with a total of 17,294 people. The analytical tool in this study uses a people analytic approach. The conclusion of this study is that making people analytics on the process of talent acquisition can be done using the Random Forest Classification method. This method aims to determine the class of each predicted data. Modeling has been made to predict performance achievements, but the performance of the model is still not showing the level of significance in accordance with the standard level of confidence, which is still below 0.05.
Co-Authors Adelia Farhani Romadhona Adhi Prasetio Aditya Fathur Rohman Agus Supriyadi Aida Fauzia Rahmatika Aldilla Iradianty Alifah, Qattrunnada Amir Nuyman Andi Teguh Imawan Anita Fitriaturrohmah Arien Arianti Gunawan Arif Partono Prasetio, Arif Partono Arissa, Angela Adelia Asep Kurniawan Astri Ghina Atyoko Utomo Carlos Soares Ribeiro Devika Herowijayanti Dian Puteri Ramadhani Dwi Andayani Elbie Yudha Pratama Fahreza Nasril Farida Titik Kristanti Fetty Poerwita Sary Firdausi, Waritsa Gadang Ramantoko Ganjar Mohamad Disastra Hani Gita Ayuningtias Indira Rachmawati Irfan Fitriansyah, Moh. Irwan Mulyawan Izzah, Wafa Nur Januario Soares Kinasih, Florentina Tri Lovei Miftahul Jannah M Oktarifan Agung Nugroho Maria Apsari Sugiat Medina Choirunnisa Meri Mayang Sari Milleniartha Moslem Mohammad Zaldhi Rizkiandi Muhamad Agus Masrukhin Natalia Christy Nidya Dudija Nur Arifyanto, Aziz Nuraini Nuraini Nurikhsan, Yuniarti Paramitha, V Santi Paulus Yustinus Maria Wibowo Ndaruhadi Pratiwi, Tiara Maulidani Putra Pertama, Jibril Putu Nina Madiawati Rahmansyah, Ezra Raisha Ameliza Az’zahra Randy Satria Ratna Komala Putri Ratri Wahyuningtyas Rejeki Amalia Yubairi Restiani, Tunas Rijal Asy'ari, Muhammad Runi, Syifa Salsabila Shanti Ramdhani Suhendrawan Suhendrawan Sujito Sujito Syah Fanissa, Raina Sylvia Meristika Rachman Tresna Nuryandi Triaji Prio Pratomo Triyandhiningsih, Azzahra Tunas Restiani Untung Rahardja Utama, Dana V. Santi Paramitha Veronika Santi Paramita Viya Ermaulandari Wibowo, Bima Argo Widyastuti, Syifa’ Pri Williams, Jack Yao, Goh Yoyoh Sofiah Zahwa, Pandan