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All Journal Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Creative Information Technology Journal JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA BERDIKARI : Jurnal Inovasi dan Penerapan Ipteks JUTIK : Jurnal Teknologi Informasi dan Komputer Jurnal Sisfokom (Sistem Informasi dan Komputer) bit-Tech International Journal of Artificial Intelligence and Robotics (IJAIR) Jurnal Pengabdian kepada Masyarakat Multica Science and Technology Indonesian Journal of Networking and Security - IJNS Malcom: Indonesian Journal of Machine Learning and Computer Science Kapas: Kumpulan Artikel Pengabdian Masyarakat SmartComp E-Amal: Jurnal Pengabdian Kepada Masyarakat
Joang Ipmawati
Universitas Nahdlatul Ulama Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Physics Public Health Social Sciences Other

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Integration of K-Means Clustering, Random Forest, and RFM Analysis for Optimizing Consumer Segmentation in Digital Advertising Strategies Joang Ipmawati; Kusnawi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2548

Abstract

In the era of data-driven marketing, accurate consumer segmentation is essential to improve the precision and impact of digital advertising. This study aims to produce more accurate consumer segmentation to support more targeted digital marketing strategies. The methods used include K-Means Clustering to group users based on digital behavior, RFM Analysis to evaluate user loyalty and interaction value with advertisements, and Random Forest to identify key factors influencing segmentation. The dataset includes demographic and behavioral information such as age, gender, income level, online duration, and interaction with digital ads. The results show that using five clusters (K=5) in K-Means Clustering yields optimal segmentation. RFM Analysis successfully categorizes users based on loyalty and engagement, while Random Forest identifies Click-Through Rate (CTR), Likes and Reactions, and Time Spent Online as the most influential variables in segmentation. This research contributes to improving the effectiveness of digital advertising campaigns and supports data-driven decision-making. The findings are significant for understanding consumer behavior patterns more deeply and for designing more efficient and relevant marketing strategies.
Co-Authors - STMIK AMIKOM Yogyakarta, Emha Taufiq Luthfi Abdulloh, Ferian Fauzi Adelia Octora Pristi Sahida Adelia Octora Pristisahida Adha, Munirah Fatimah Aditya Wahyu Pratama Afrig Aminuddin Ahmad Asyhari Akhmad Fakhurrozi Alif Yudha Suryatama Asadulloh, Bima Pramudya Aulia, Kunni Rofiqotul Azzahro, Marissa Bledug Kusuma Diyon Saputro Elsa Virantika Ferry Wahyu A Hedry Aristianda Heni Pujiastuti Hikmah, Harisna I Nyoman Apraz Ramatryana Ihwan Mualana Ilham Unggara Irwan Novianto Jumanur Rohman Kusnawi Kusnawi Kusrini . Laksana, Feri Febria Lilis Kurniasari M Andika Fadhil Eka Putra M Rifki Ramdani Majid Rahardi Millaty, Marosimy Muhamad Adhitya Saputra Muhamad Nasruddin Manaf Ngulum, Mochamad Bahrul Novan Afrianto Nur Azmi Ainul Bashir NURUL FAIZAH Prasetyo Nur Hidayat Puji Prabowo, Dwi Restiadi Bayu Taruno Rifqi Syarif Nasrulloh Rio Ardiansyah saifulloh Saifulloh, saifulloh Septian Rico Hernawan Taruno, Restiadi Bayu unggara, Ilham Wigar Kumara P Wimar Febrianto Yana Hendriana Yogananda, Amrina Amalia Yudho Bismo Utomo, Yudho Bismo Zulfatun Ruscitasari Zulfatun Ruscitasari Zulkhairi Zulkhairi Sofyan