CLUSTERING NASABAH MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING PADA BANKALTIMTARA

Habibi, Habibi (2024) CLUSTERING NASABAH MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING PADA BANKALTIMTARA. S1 Teknik Informatika thesis, STMIK Widya Cipta Dharma.

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Abstract

ABSTRAK Habibi, 2024, Clustering Nasabah Menggunakan Algoritma K-Means Clustering pada Bankaltimtara. Skripsi Jurusan Teknik Informatika, Sekolah Tinggi Manajemen Informatika dan Komputer Widya Cipta Dharma, Pembimbing (I) Wahyuni, S.Kom., M.Kom., Pembimbing (II) Ita Arfyanti, S.Kom., M.M. Kata Kunci : K-Means, Clustering, Crisp-DM, Data Mining, Perbankan Penelitian ini bertujuan untuk mengelompokkan nasabah Bankaltimtara berdasarkan karakteristik tertentu menggunakan algoritma K-Means Clustering. Dalam proses pengembangan penelitian, metode CRISP-DM (Cross-Industry Standard Process for Data Mining) digunakan sebagai kerangka kerja yang sistematis. Metode ini terdiri dari enam fase utama: pemahaman bisnis, pemahaman data, persiapan data, pemodelan, evaluasi, dan penerapan. Fase pemahaman bisnis dilakukan untuk mengidentifikasi tujuan dan kebutuhan dari Bankaltimtara terkait segmentasi nasabah. Fase pemahaman data melibatkan pengumpulan data nasabah yang relevan, seperti data demografis dan transaksi. Selanjutnya, pada fase persiapan data, dilakukan proses pembersihan dan transformasi data agar siap digunakan dalam analisis. Pada fase pemodelan, algoritma K-Means Clustering diterapkan untuk mengelompokkan nasabah ke dalam beberapa segmen berdasarkan kesamaan karakteristik. Proses evaluasi dilakukan untuk menilai keakuratan dan relevansi model yang dihasilkan. Terakhir, fase penerapan mencakup implementasi model dalam lingkungan operasional Bankaltimtara serta analisis hasil untuk memberikan rekomendasi strategi pemasaran yang lebih tepat sasaran. Hasil dari penelitian ini menunjukkan bahwa algoritma K-Means Clustering mampu mengidentifikasi beberapa segmen nasabah yang berbeda, yang dapat digunakan oleh Bankaltimtara untuk meningkatkan pelayanan dan strategi pemasaran. Dengan segmentasi nasabah yang lebih terstruktur, Bankaltimtara dapat menawarkan produk dan layanan yang lebih sesuai dengan kebutuhan masing-masing segmen nasabah. ============================================================ ABSTRACT Habibi, 2024, Clustering Customers Using K-Means Clustering Algorithm at Bankaltimtara. Thesis Department of Informatics Engineering, College of Information Management and Computer Widya Cipta Dharma, Supervisor (I) Wahyuni, S.Kom., M.Kom., Supervisor (II) Ita Arfyanti, S.Kom., M.M. Keywords: K-Means, Clustering, Crisp-DM, Data Mining, Banking This study aims to cluster customers of Bankaltimtara based on specific characteristics using the K-Means Clustering algorithm. The CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology was employed as a systematic framework in the research development process. This methodology consists of six main phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The business understanding phase was conducted to identify the objectives and requirements of Bankaltimtara regarding customer segmentation. The data understanding phase involved collecting relevant customer data, such as demographic and transaction data. Subsequently, in the data preparation phase, the data was cleaned and transformed to be ready for analysis. In the modeling phase, the K-Means Clustering algorithm was applied to group customers into several segments based on similar characteristics. The evaluation phase assessed the accuracy and relevance of the generated model. Finally, the deployment phase included implementing the model in Bankaltimtara's operational environment and analyzing the results to provide recommendations for more targeted marketing strategies. The results of this study indicate that the K-Means Clustering algorithm is capable of identifying several distinct customer segments, which can be utilized by Bankaltimtara to enhance service and marketing strategies. With more structured customer segmentation, Bankaltimtara can offer products and services that better meet the needs of each customer segment.

Item Type: Thesis (S1 Teknik Informatika)
Additional Information: Pembimbing Utama : Wahyuni, S.Kom., M.Kom Pembimbing Pendamping : Ita Arfyanti, S.Kom., M.M
Uncontrolled Keywords: K-Means, Clustering, Crisp-DM, Data Mining, Perbankan
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Teknik Informatika
Depositing User: Mr Habibi Habibi
Date Deposited: 08 Aug 2024 01:51
Last Modified: 08 Aug 2024 01:51
URI: http://repository.wicida.ac.id/id/eprint/5716

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