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Contact Name
Muhammad Hasanuddin
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cvraskhamediagroup@gmail.com
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+628111261633
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ejodsie@gmail.com
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Jalan Gurilla No. 2 Sidorejo, Kec. Medan Tembung 20222
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INDONESIA
Journal of Data Science and Informatics Engineering
Published by CV. Raskha Media Group
ISSN : -     EISSN : 3123948X     DOI : https://doi.org/10.64803/jodsie
Core Subject : Science,
The Journal of Data Science and Informatics Engineering (JoDSIE) is an open-access, peer-reviewed academic journal that publishes high-quality research articles and reviews in the fields of data science, informatics, and engineering. It aims to bridge the gap between theory and practice by providing a platform for innovative contributions that advance the development, application, and understanding of data science methodologies and informatics engineering solutions. JoDSIE is committed to showcasing interdisciplinary research that addresses real-world challenges across various industries and academia, offering valuable insights into how data-driven approaches can foster technological advancements and improve decision-making processes. The journal focuses on publishing cutting-edge research in the areas of data science, machine learning, artificial intelligence, big data analytics, informatics, and engineering systems. It covers both theoretical developments and practical implementations of data-driven techniques in diverse domains. JoDSIE seeks to highlight advances in data processing, computational models, algorithms, and the engineering of systems that leverage data for decision-making, problem-solving, and optimization. Additionally, the journal is dedicated to fostering a deeper understanding of the ethical, legal, and societal implications of data science and informatics engineering.
Articles 11 Documents
Strategi Customer Intelligence Melalui Klasterisasi K-Means untuk Optimalisasi Retensi Pelanggan pada Industri Telekomunikasi Muhammad Azril Surya Ramadhan; Winda Chariska; Mufidah Karimah
Journal of Data Science and Informatics Engineering Vol. 1 No. 2 (2026): April 2026
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jodsie.v1i2.38

Abstract

Di tengah persaingan ketat industri telekomunikasi, tingginya churn rate pelanggan mengancam stabilitas pendapatan perusahaan. Penelitian ini menerapkan klasterisasi K-Means pada dataset Telco Customer Churn untuk mengidentifikasi segmen pelanggan guna optimalisasi retensi melalui customer intelligence, mengikuti kerangka CRISP-DM. Data diproses dengan imputasi median pada Total Charges, normalisasi StandardScaler, dan penentuan klaster optimal (k=4) via Elbow Method. Hasil mengungkap empat profil: (1) The Newbies  (tenure rendah, biaya rendah); (2) High-Value Loyalists (tenure tinggi, biaya tinggi); (3) At-Risk Big Spenders (tenure rendah, biaya sangat tinggi); dan (4) Budget Veterans (tenure tinggi, biaya rendah), divalidasi Silhouette Score solid. Temuan memungkinkan strategi personalisasi: promo onboarding untuk Newbies, VIP rewards untuk Loyalists, kontrak jangka panjang untuk At-Risk, dan upselling bundling untuk Veterans. Pendekatan ini meningkatkan efisiensi alokasi sumber daya pemasaran berbasis data.

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