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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Informatika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Sains dan Seni ITS IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science Gamatika Prosiding Seminar Matematika dan Pendidikan Matematik JURNAL SOSIAL HUMANIORA (JSH) Semesta Teknika Elkawnie Register: Jurnal Ilmiah Teknologi Sistem Informasi Science and Technology Indonesia Journal of Environment and Sustainability Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control BAREKENG: Jurnal Ilmu Matematika dan Terapan Limits: Journal of Mathematics and Its Applications International Journal of Computing Science and Applied Mathematics JAIM (Jurnal Abdi Masyarakat) Jurnal Teknologi Dan Sistem Informasi Bisnis Indonesian Journal of Electrical Engineering and Computer Science Majalah Ilmiah Matematika dan Statistika (MIMS) Journal of Applied Data Sciences Journal of Research in Social Science and Humanities COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Sewagati Limits: Journal of Mathematics and Its Applications
Mohammad Isa Irawan
Departemen Matematika Institut Teknologi Sepuluh Nopember Surabaya
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PENGKLASTERAN DATA KATEGORIS DENGAN ALGORITMA SHARED NEAREST NEIGHBOR Alvida Mustikarukmi; M. Isa Irawan; Nurul Hidayat
Limits: Journal of Mathematics and Its Applications Vol. 6 No. 1 (2009): Limits: Journal of Mathematics and Its Applications Volume 6 Nomor 1 Edisi Mei
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pengklasteran objek data merupakan salah satu cara untuk mempermudah dalam membaca data, terutama data berdimensi tinggi. Obyek-obyek data berada dalam satu klaster jika mempunyai kesamaan yang tinggi, dan sebaliknya, berada pada klaster berbeda jika menunjukkan ketidaksamaan. Data kategoris merupakan jenis data yang sering digunakan pada database/dataset. Data teks merupakan salah satu data kategoris. Pengklasteran dengan algoritma shared nearest neighbor (SNN) didasarkan pada anggapan bahwa titik-titik akan berada dalam klaster yang sama jika jumlah shared nearest neighbor melebihi ambang batas yang ditentukan. Algoritma SNN mampu memberikan hasil pengklasteran data teks dengan baik, dimana teks dengan tingkat kesamaan yang ditentukan, akan berada pada klaster yang sama.
Customer Churn Prediction Using the RFM Approach and Extreme Gradient Boosting for Company Strategy Recommendation Mohammad Isa Irawan; Nadhifa Afrinia Dwi Putris; Noryanti binti Muhammad
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 2 (2024): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i2.4004

Abstract

Customers are vital assets in the growth and sustainability of business  organizations. However, customers may discontinue their engagement with a company and switch to competitors’ products or services for various reasons. This event referred to as customer churn. Losing customers significantly impacts a company's revenue, often resulting in financial decline. Churn events, which are subject to dynamic monthly changes, are further influenced by intense competition and rapid technological advancements. Analyzing customer characteristics is crucial to understanding customer behavior, with metrics such as recency, frequency, monetary (RFM) serving as key indicators of subscription and transaction patterns. The Extreme Gradient Boosting method is applied to address the challenge of classifying churn and non-churn customers. The prescriptive analytics process is carried out to identify the features most influential in prediction outcomes, enabling the formulation of strategic recommendations to mitigate churn problems. The integration of RFM analysis with the XGBoost method provides optimal results, particularly in the third segmentation, achieving an accuracy of = 0.98833, precession = 0.98768, recall = 0.98899, and f1-score = 0.98833. The prescriptive analytics process highlights three critical features, namely city factor, GMV generation, and total customer transaction generation. This findings demonstrate that the segmentation characteristics, data representation, and behavioral approach with RFM analysis have an effect on improving the performance of the model in churn prediction.
Co-Authors AA. Masroeri Abduh Riski, Abduh Adrianus Bagas Tantyo Dananjaya Akhmad Arif Junaidi Alan Catur Nugraha Alexander Setiawan Alvida Mustika Rukmi Alvida Mustikarukmi Amira, Siti Azza Andreas Handojo Antonio Galileo Tando Arie Dipareza Syafei Arifah, Enny Durratul Auliya Rahmayani Baiq Findiarin Billyan Chyntia Kumalasari Puteri Danang Wahyu Wicaksono Darmaji Darmaji Darmawan, Didiet Edi Satriyanto Ekky Hidma Octia Rahmah Elly Matul Imah Elnora Oktaviyani Gultom Elsen Ronando Erna Apriliani Fendhy Ongko Giandi, Oxsy Ginardi, Raden Venantius Hari Hadi Prasetiya Haloho, Freddi Hartanto Setiawan Hendy Hendy Hendy Hozairi Imam Mukhlash Imam Mukhlash Ira Puspitasari Kadek Eri Mahardika Ketut Buda Artana Khilmy, Akhmad Ku Khalif, Ku Muhammad Naim Mahdiyah, Umi Mardlijah - Mey Lista Tauryawati Mohamad Muhtaromi Mohammad Hamim Zajuli Al Faroby Mohammad Iqbal Mohammad Jamhuri Mohd Aziz, Mohd Khairul Bazli Muchamad Jati Nugroho Muhammad Agung Adi Maulana Muhammad Ahnaf Amrullah Muhammad Athoillah, Muhammad Muhammad Fakhrur Rozi Muhammad Hajarul Aswad Muhammad, Noryanti Mujiono, Edo Priyo Utomo Putro Nadhifa Afrinia Dwi Putris Ni Nyoman Tri Puspaningsih Noryanti binti Muhammad Nugraha, Arma Perwira Nurul Hidayat NURUL HIDAYAT Pratama, Qoria Yudi Putri, Endah R.M. Putri, Endah Rokhmati Merdika Rasyadan Taufiq Probojati Resi Arumin Sani Rita Ambarwati Rita Ambarwati Sukmono Robin Wijaya, Robin Ronando, Elsen Rukmini, Meme Samsul Setumin Santoso Santoso Sepriadi, Robby Setiawan, Muhammad Nanda Shahab, Muhammad Luthfi Siti Maghfiroh Soetrisno Soetrisno Sulastri Sulastri Titin J. Ambarwati Victory Tyas Pambudi Swindiarto YAN ADITYA PRADANA Yongky Ujianto Yuda Dian Harja Zulfa Afiq Fikriya