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All Journal Jurnal Media Infotama Pixel : Jurnal Ilmiah Komputer Grafis JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Pseudocode POSITIF Jurnal Informatika Upgris JURIKOM (Jurnal Riset Komputer) Jurnal Tekno Kompak JSAI (Journal Scientific and Applied Informatics) Journal of Technopreneurship and Information System (JTIS) Jusikom: Jurnal Sistem Informasi Ilmu Komputer Journal of Applied Engineering and Technological Science (JAETS) JISA (Jurnal Informatika dan Sains) Journal of Applied Data Sciences Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat ABDIMAS Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Teknologi Sistem Informasi Jurnal Ilmu Komputer Dan Informatika eProceedings of Engineering Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Informatika: Jurnal Pengembangan IT Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) Applied Information Technology and Computer Science (AICOMS) Jurnal Teknokes
Dedy Agung Prabowo
Universitas Telkom
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Journal : Journal of Applied Data Sciences

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Kodein-Penetration: Recommendations of Customer Personalization Level in A CRM using Deep Learning Sudianto, Sudianto; Usman, Muhammad Lulu Latif; Prabowo, Dedy Agung; Gustalika, Muhamad Azrino; Marsally, Silvia Van; Akhmad, Fajar Kamaludin; Rakhma, Nazwa Aulia; Muna, Bunga Laelatul; Wicaksono, Apri Pandu; Rachman, Ari
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.597

Abstract

This study aims to develop a personalization-level recommendation model implemented in the Customer Relationship Management (CRM) system at PT Kodegiri, called KodeinPenetration. Personalization in CRM aims to improve customer interaction by providing more relevant recommendations based on their needs and preferences. To achieve this goal, this study tested several classification models using historical customer interaction data as the basis for analysis. The classification models tested included decision tree-based methods such as Random Forest, Gradient Boosting, and AdaBoosting, as well as deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). In addition, two main feature extraction techniques were applied to process text data, namely TF-IDF (Term Frequency-Inverse Document Frequency) and Tokenizer Padding. TF-IDF is used to represent words as numeric vectors based on their frequency of occurrence. In contrast, Tokenizer Padding is used in deep learning models to convert text into a numeric format that neural networks can process. The test results showed that the decision tree-based method using the TF-IDF feature produced the best accuracy of up to 82%. On the other hand, the deep learning model with GRU architecture utilizing Tokenizer Padding achieved the highest accuracy of 88.23%. This shows that the deep learning model has greater potential in handling sequential data and providing more accurate results compared to traditional methods. This study provides an important contribution to the development of deep learning-based personalized recommendation systems in CRM. By leveraging historical customer interactions, this system can improve user experience by offering more relevant and targeted services.
Co-Authors 12.5202.0161 Daniel Yeri Kristiyanto Abdul Jabbar Robbani Adanti Wido Paramadini Agustianto, Satya Helfi Agustomi, Endri Ajeng Dyah Kurniawati Akhdan Syarif Hidayatullah, Dias Akhmad, Fajar Kamaludin Alon Jala Tirta Segara An-Naayif, Hanief Taqiyuddien Adz-Dzaky Andri Sarpiadi Ardi Susanto Ardi Susanto Aritonang, Sudarsono Azzahra, Fathya Yuanita Briliana, Carlita Wahyu Cahyo Prihantoro Dandi Sunardi Dasril Aldo Dedy Abdullah Dedy Abdullah Dernata, Jaka Dimas Fanny Hebrasianto Permadi Dofiyer, Fernaldo Christofer Dwi Putro Wicaksono, Aditya Eki Agustiawan Faizah Faizah Fauzi Ahmad Muda Fauzian Setiawan, Kelvin Fernaldo Christofer Dofiyer Fiqrian, Muhammad Nafal Firdaus, Ammar Musthofa Gunawan Gunawan Hengki Putra Irawan Jaka Dernata Kirman Kirman, Kirman Kristanto, Joshua Putra Fesha M Yoka Fathoni marhalim, marhalim Marsally , Silvia Van Marsally, Silvia Van Muflih Haura Muhamad Azrino Gustalika Muhammad Husni Rifqo Muna, Bunga Laelatul Nicolaus Euclides Wahyu Nugroho Novian Adi Prasetyo Oktavia, Laksmi Dwi Pangestu, Farhan Aryo Paradise Perdi Leo Ade Candra Pratama, Rendra Agung Putra, Erwin Dwika Rachman, Ari Rakhma, Nazwa Aulia Ramdani, Cepi Rendra Agung Pratama Rendra Agung Pratama Resad Setyadi Rifqo, Muhammad Husni Rona Nisa Sofia Amriza Sa'adah, Aminatus Salsabila, Luciana Sandhy Fernandes Sandhy Fernandez Saputra , Wahyu Andi Sarah Astiti S.Kom., M.MT Silvia Van Marsally Sonita, Anisya Sudianto Sudianto, Sudianto Sulaeman, Gilang Sundari Sundari Syaputri, Yopita Syarif Hidayatullah, Dias Akhdan Tarigan, Emya Ninta Teguh Ramadhani, Dimas Toyib, Rozali Triaji Morgana Kaban Usman, Muhammad Lulu Latif Utami, Annisaa Wicaksono, Apri Pandu Wijiasih, Tsania Maulidia Yasin, Feri Yohani Setiya Rafika Nur Yuza Reswan