{
      "title": "OPTIMASI KLASTERING K-MEANS UNTUK SEGMENTASI\r\nPELANGGAN DENGAN INTEGRASI PCA, DBSCAN DAN KMEANS++",
      "department": "Fakultas Ilmu Komputer",
      "rev_number": 16,
      "divisions": [
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      ],
      "date": "2026-05-09",
      "contributors": [
        {
          "name": {
            "family": "Dhuhita",
            "given": "Windha Mega Pradnya",
            "lineage": null,
            "honourific": null
          }
        }
      ],
      "status_changed": "2026-10-06 03:34:41",
      "full_text_status": "restricted",
      "keywords": "Customer Segmentation, Clustering, K-Means++, PCA, DBSCAN,  Segmentasi Pelanggan, Klasterisasi.",
      "type": "thesis",
      "creators": [
        {
          "nim": "20.11.3652",
          "name": {
            "lineage": null,
            "given": "Tonik",
            "family": "Purwanto",
            "honourific": null
          }
        }
      ],
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      "institution": "Universitas AMIKOM Yogyakarta",
      "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/33167",
      "metadata_visibility": "show",
      "subjects": [
        "000.000.000A",
        "000.000.006"
      ],
      "abstract": "Segmentasi pelanggan merupakan salah satu strategi penting dalam\r\nmemahami perilaku pelanggan dan mendukung pengambilan keputusan bisnis.\r\nNamun, metode klasterisasi konvensional seperti K-Means memiliki kelemahan\r\ndalam menentukan jumlah klaster secara optimal, sensitif terhadap inisialisasi titik\r\npusat (centroid) awal, serta kurang mampu menangani pencilan (outlier) pada data.\r\nPermasalahan tersebut dapat menyebabkan kualitas segmentasi pelanggan menjadi\r\nkurang optimal, sehingga strategi pemasaran, pelayanan pelanggan dan\r\npengambilan keputusan bisnis menjadi kurang tepat sasaran.\r\nPenelitian ini menggunakan metode eksperimen dengan pendekatan CRISPDM\r\nuntuk\r\nmenyelesaikan\r\npermasalahan\r\ntersebut.\r\nTahapan\r\npenelitian\r\ndimulai\r\ndari\r\n\r\nbusiness\r\n\r\nunderstanding, data understanding, data preparation, modeling dan\r\nevaluation. dataset   yang digunakan adalah Online Retail dengan pendekatan RFM\r\n(recency, frequency, monetary). Metode yang diusulkan adalah hybrid dengan\r\nmengintegrasikan PCA untuk menyederhanakan struktur data, mengurangi korelasi\r\nantar fitur, serta membantu visualisasi data klasterisasi, DBSCAN untuk\r\nmengidentifikasi noise yang merepresentasikan outlier dan menentukan jumlah\r\nklaster secara otomatis, serta K-Means++ untuk menghasilkan klaster akhir yang\r\nlebih stabil.\r\nHasil penelitian menunjukkan bahwa metode hybrid menghasilkan\r\nperforma klasterisasi terbaik dengan nilai Silhouette Score sebesar 0.464281, lebih\r\ntinggi dibandingkan K-Means dan K-Means++ sebesar 0.432940. Selain itu,\r\nmetode hybrid menghasilkan Davies-Bouldin Index (DBI) terendah sebesar\r\n0.821916 dan Calinski-Harabasz Index tertinggi sebesar 5103.759768. Kontribusi\r\npenelitian ini adalah memberikan metode segmentasi pelanggan yang lebih optimal\r\nuntuk membantu perusahaan ritel dalam menentukan strategi pemasaran dan\r\npengelolaan pelanggan.",
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