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All Journal Buletin Peternakan Natural Science: Journal of Science and Technology Jurnal Ilmu Lingkungan Jurnal Natur Indonesia Microbiology Indonesia Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Riset Kimia Jurnal Kimia dan Kemasan Jurnal Litbang Industri IJFAC (Indonesian Journal of Fundamental and Applied Chemistry) Jurnal Zarah Alotrop Jurnal Pendidikan dan Ilmu Kimia Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research JITK (Jurnal Ilmu Pengetahuan dan Komputer) JPT : JURNAL PROTEKSI TANAMAN (JOURNAL OF PLANT PROTECTION) INTECOMS: Journal of Information Technology and Computer Science Jiko (Jurnal Informatika dan komputer) Jurnal Katalisator Jurnal Pascapanen dan Bioteknologi Kelautan dan Perikanan METIK JURNAL Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Analis Farmasi Jurnal Agrotropika Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Kimia dan Kemasan Jurnal Pendidikan dan Teknologi Indonesia JAF (Jurnal Analis Farmasi) Jurnal Litbang Industri Jurnal Algoritma Makara Journal of Science Data Sciences Indonesia (DSI) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Journal of Engineering and Science Application
Abdi Dharma
Universitas Andalas
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Medicine & Pharmacology Physics Veterinary Other

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Journal : International Journal of Artificial Intelligence Research

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Uplift modeling VS conventional predictive model: A reliable machine learning model to solve employee turnover Wijaya, Davin; DS, Jumri Habbeyb; Barus, Samuelta; Pasaribu, Beriman; Sirbu, Loredana Ioana; Dharma, Abdi
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1478.697 KB) | DOI: 10.29099/ijair.v4i2.169

Abstract

Employee turnover is the loss of talent in the workforce that can be costly for a company. Uplift modeling is one of the prescriptive methods in machine learning models that not only predict an outcome but also prescribe a solution. Recent studies are focusing on the conventional predictive models to predict employee turnover rather than uplift modeling. In this research, we analyze whether the uplifting model has better performance than the conventional predictive model in solving employee turnover. Performance comparison between the two methods was carried out by experimentation using two synthetic datasets and one real dataset. The results show that despite the conventional predictive model yields an average prediction accuracy of 84%; it only yields a success rate of 50% to target the right employee with a retention program on the three datasets. By contrast, the uplift model only yields an average accuracy of 67% but yields a consistent success rate of 100% in targeting the right employee with a retention program.
Co-Authors - Afrizal Admin Alief Admin Alif Admin Alif Afriyanti Azhar Alif, Admin Amir Mahmud Husein, Amir Mahmud Andika, Ahmad Zaki Andrian Anggela Marta Tasman Arif Juliari Kusnanda Arjon Turnip Armaini - Baharuddin Shaleh Barus, Samuelta Bayu Afnovandra Perdana Candra, Windy Chandra, Angelia Ayu Chandra, Suwito Christnatalis Chrysia, Celine Crispin, Andrian Reinaldo Dedi Nofiandi Delima Sitanggang, Delima Dewantoro, Rico Wijaya Dicky DS, Jumri Habbeyb Edbert, Edbert Edison Munaf Edison Munaf Elida Mardiah Eri Sulyanti Eti Farda Husin Eti Farda Husin Giawa, Priskila Veronika HAFIL ABBAS Hafil Abbas Harvianti, Yuniar Hazli Nurdin Heyneker, Daniel Husni Mukhtar I PUTU KOMPIANG I. P. Kompiang Indah Indah Indrawati - Indrawati Indrawati IRSAN RYANTO Jabang Nurdin Jamsari Jamsari Jefika, Meliy Kamble, Pratik Bibhisan Kosasi, Hendrick Laoli, Vincensius Yaatulo Lazuardya , Kevin Lee Wah Lim Luis, Matthew MARBUN, ADVENT TORAS Mardi Turnip, Mardi MARIA ENDO MAHATA Marniati Salim Mawaddah Harahap, Mawaddah Melona Siska Musifa, Eva Nasril Nasir Nasril Nasir Nasril Nasir Nasril Nasir Nasril Nasir Nurhamidah Oktaf Rina Oktoriza, Ghifarizka Pasaribu, Beriman Periadnadi - Periadnadi Periadnadi Prayogi, Gali PULUNGAN, JURMIDA PURBA, JOICE ANGELINA Putra, Adya Zizwan Rahmadani Wulandari Rahmatika Yani Rahmiana Zein Randi, Albert Refilda Refilda Riska Hernandi Romaito pane, Jesi Saragi, Yosua Morales Sekatresna, Widiyanti Shaleh, Baharuddin Silaban, Elvin Josafat Simbolon, Billy Sirbu, Loredana Ioana Siregar, Yakob Mulia Siti Aisyah Siti Hajjir, Siti Sulyanti, Eri Sulyanti, Eri Sumaryati Syukur Sunnia, Cecilia Suryani Suryani Syafriza Yanti Syafrizayanti, Syafrizayanti Syafrizayanti, Syafrizayanti Syafrizayanti, Syafrizayanti Syukri Arief Talib, Ramanisa Muliani Tania, Alinda Tarigan, Julio Putra Telaumbanua, Christina Angelina Toyohide Takeuchi TRIMURTI HABAZAR Turnip, Josua Presen Vagga, Cherry Piya Valentino, Bue Vanness, Jeff Veron, Veron Victor Trismanjaya Hulu Wahida Nia Elfiza Warni, Mega Waruwu, Jefrin Widiyanti Sekatresna Wijaya, Davin Wijaya, Eko Bambang Wijaya, Jeremy Wijaya, Kenrick Alvaro Wijaya, Vincent William Wizna (Wizna) Wulandari, Rahmadani Yetria Rilda Yohana Samosir, Devi Yose Rizal Yoserizal Yoserizal Yunazar Manjang Yunazar Manjang Yunazar Manjang Zulkarnain Chaidir