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Rusilowati, Umi
Fakultas Ekonomi Dan Bisnis, Universitas Pamulang
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Neuroscience Public Health Social Sciences Other

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Journal : Journal of Applied Data Sciences

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Predictive and Analytics using Data Mining and Machine Learning for Customer Churn Prediction Lukita, Chandra; Bakti, Lalu Darmawan; Rusilowati, Umi; Sutarman, Asep; Rahardja, Untung
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.131

Abstract

This research aims to predict and analyze customer churn using Data Mining and Machine Learning methods. The background of this research is based on the importance of understanding the factors that influence customer decisions to churn, as well as improving the effectiveness of customer retention strategies in a business context. The method used in this research involves the use of a customer bank dataset that includes information about customers who left in the past month, services registered by customers, customer account information, and demographic info about customers. The factors most influential to churn were identified through heatmap analysis, including MonthlyCharges, PaperlessBilling, SeniorCitizen, PaymentMethod, MultipleLines, and PhoneService. This research compares the performance of several machine learning algorithms, including Random Forest, Logistic Regression, Adaboost, and Extreme Gradient Boosting (XGBoost), to predict customer churn. Accuracy metrics and confusion matrix results are used to evaluate the performance of these algorithms. The results showed that XGBoost proved to be the best algorithm in predicting customer churn with high accuracy. The factors that have been correctly identified do not provide missed precision, showing a significant influence on customer churn decisions. The novelty and uniqueness of this research lies in focusing on the factors that have the most influence on customer churn and comparing the performance of machine learning algorithms. This research provides more specific and relevant insights for companies in developing effective customer retention strategies. However, this research has some limitations. One of them is the use of a dataset limited to a customer bank, so the generalizability of the findings of this research may be limited to that business context. In addition, other factors that are not the focus of this research may also contribute to the prediction of customer churn.
Co-Authors Agung Nugroho Agus Budiyana Ahmad Syukri Akhmad Fatkhurohman Al Faruq, Abdullah Jundi Ali Imron Ali Zaenal Abidin Ambar Pratiwi Anandha Fitriani Arshandi, Primastyo Aswadi Jaya, Aswadi Atiawati Atiawati Atmaja, Fitrah Jaya Awaluddin Muchtar Azhar Affandi B., Hamsinah Bagus Kuncoro Indarto Baharuddin, Hamsinah Bangun, Cicilia Sriliasta Bennet, Daniel Beta Mentari Chakim, Heru Riza Daeli, Ora Plane Maria Denok Sunarsi Denok Sunarsi Djati, Leryza Audryna Dwi Andayani, Dwi Dwiyana, Aldi Eka Dian Astuti Ekawati, Anita ENDANG SUGIARTI Evan Anggriawan Fathorrahman Fathorrahman Fatkhurohman, Akhmad Fifth Ernawati Fitrah Jaya Atmaja Fransiska Deci Natalia Gatut Drajad Purwoko Green, Thomas Hadi Supratikta Haedar Akib Hajra Rasmita Ngemba Hamsinah Hamsinah Hamsinah Hamsinah Hamsinah Hamsinah, Hamsinah Harini, Tya Puri Helmawan, Wawan Heri Saputra Hetilaniar Hifny, Muhammad I Nyoman Marayasa Iman Sudirman Irmawanti, Irmawanti Ismail, Farid Bin Jeni Andriani Johnson, David Juhaeri Juhaeri Kusuma, Febby Permatasari Lalu Darmawan Bakti, Lalu Darmawan Lia Herlina Livio Agung Dharmesta, Livio Agung M. Risal, M. Manggala Bela Adi Utama Marayasa, I Nyoman MARDLIYAH, SITI Marifah Marifah Marifah Marifah Marleen Tasidjawa Masno Marjohan Mirdad, Khaizure Moh. Sidik Priadana Mohamad Samsudin Mohamad Sutoro Muhammad Agus Salim Mukrodi, Mukrodi Mulyadi Mulyadi Muslim Naning Oktaviana Nardi Sunardi Nardi Sunardi Nugroho, Agung Nugroho, Arief Novianto Nugroho, Fajar Pandu Nurdin, Abidin Candra Nurmin Arianto, Nurmin Oktrima, Bulan Pasaribu, Veta Lidya Delimah Pernanda, Dody Primastyo Arshandi Pujiati, Tri Rachmawaty Rachmawaty, Rachmawaty Raden Mohamad Herdian Bhakti Raden Roro Ayu Metarini Rahmi Hermawati Rasmadi . Rini Suryani Rio Wahyudin Anugrah Roos, Harjo Nakulo Muhammad Ramadhan Putra Wika Rostikawati, Dian Rr Ayu Metarini Rr. Ayu Metarini RR. Ella Evrita Hestiandari Ruhaedi, Rudi Saputra, Heri Sarwani . Sarwani Sarwani Sarwani Sarwani, Sarwani Senen Senen Senen Senen Septiani, Nanda Setiawan, Sandy Setiyawan, Wasono Sholihin, Dwi Rahmad Siti Mardliyah Sri Handayani, Avivah Styo Budi Utomo Subarto, Subarto Sudaryana, Yayan Sudjatmoko Sudjatmoko Sugiarti, Endang Sugiyanto - Sunardi, Nardi Sunarsi, Denok Susanto, Hengli Sutarman, Asep Sutiman Sutiman Sutiman Sutiman, Sutiman Suwanto Suwanto Syafi’i, Imam Syavera, Arnesha Febra Tasidjawa, Marleen Taswanda Taryo Turnip, Jhon Sutrisno Tya Puri Harini Untung Rahardja Utama, Manggala Bela Adi Utami, Monika Agustia Utomo, Styo Budi Veta Lidya Delimah Pasaribu Wachidin, Muhammad Soffan Wahyu Purnomo Wahyu Purnomo, Wahyu Wahyudi Wahyudi Wakhid Lukas Wang, Rion Widia Astuti Widia Astuti Williams, James Xuan, Alvin Teo Zi Yafi, M Reyhan Yayan Sudaryana Yudha, Fariz Kartika