{ "ispublished": "pub", "lastmod": "2026-10-02 06:53:29", "department": "Fakultas Ilmu Komputer", "title": "ANALISIS PREDIKSI CUSTOMER CHURN PADA LAYANAN \r\nTELEKOMUNIKASI MENGGUNAKAN ALGORITMA \r\nMACHINE LEARNING", "thesis_type": "bachelor", "eprintid": 33122, "documents": [ { "files": [ { "fileid": 1161169, "mime_type": "application\/pdf", "hash": "b6b79858595e2314ac630552d4190060", "filename": "COVER.pdf", "mtime": "2026-10-02 06:44:23", "objectid": 329750, "hash_type": "MD5", "datasetid": "document", "filesize": 707697, "uri": "http:\/\/eprints.amikom.ac.id\/id\/file\/1161169" } ], "uri": "http:\/\/eprints.amikom.ac.id\/id\/document\/329750", "main": "COVER.pdf", "content": "published", "pos": 1, "language": "id", "format": "text", "docid": 329750, "rev_number": 3, "security": "public", "formatdesc": "COVER-ABSTRAK", "placement": 1, "eprintid": 33122, "mime_type": "application\/pdf" }, { "files": [ { "uri": "http:\/\/eprints.amikom.ac.id\/id\/file\/1161171", "datasetid": "document", "filesize": 266820, "filename": "BAB I.pdf", "mtime": "2026-10-02 06:44:34", "objectid": 329751, "hash_type": "MD5", "fileid": 1161171, "mime_type": "application\/pdf", "hash": "79319d3df051171e8cbf3d7d19807f78" } ], "uri": "http:\/\/eprints.amikom.ac.id\/id\/document\/329751", "content": "published", "main": "BAB I.pdf", "format": "text", "language": "id", "pos": 2, "docid": 329751, "rev_number": 2, "security": "public", "formatdesc": "BAB I", "placement": 2, "mime_type": "application\/pdf", "eprintid": 33122 }, { "uri": "http:\/\/eprints.amikom.ac.id\/id\/document\/329752", "files": [ { "hash": "4419d6e5bbd13501c6c6c799e26ee5f1", "mime_type": "application\/pdf", "fileid": 1161173, "hash_type": "MD5", "objectid": 329752, "mtime": "2026-10-02 06:44:37", "filename": "BAB II.pdf", "filesize": 636576, "datasetid": "document", "uri": "http:\/\/eprints.amikom.ac.id\/id\/file\/1161173" } ], "formatdesc": "BAB II", "rev_number": 2, "security": "validuser", "format": "text", "content": "published", "main": "BAB II.pdf", "language": "id", "pos": 3, "docid": 329752, "placement": 3, "mime_type": "application\/pdf", "eprintid": 33122 }, { "content": "published", "docid": 329753, "main": "BAB III.pdf", "language": "id", "format": "text", "pos": 4, "formatdesc": "BAB III", "security": "validuser", "rev_number": 2, "uri": "http:\/\/eprints.amikom.ac.id\/id\/document\/329753", "files": [ { "uri": "http:\/\/eprints.amikom.ac.id\/id\/file\/1161175", "filesize": 282944, "datasetid": "document", "hash_type": "MD5", "mtime": "2026-10-02 06:44:40", "objectid": 329753, "filename": "BAB III.pdf", "hash": "5ee8a2166c8fc6bd43aacceae5d65023", "mime_type": "application\/pdf", "fileid": 1161175 } ], "mime_type": "application\/pdf", "eprintid": 33122, "placement": 4 }, { "files": [ { "hash_type": "MD5", "mtime": "2026-10-02 06:44:44", "objectid": 329754, "filename": "BAB IV.pdf", "hash": "e535e6a1c0fc627e19a1d2d1fa7d2bbb", "mime_type": "application\/pdf", "fileid": 1161177, "uri": "http:\/\/eprints.amikom.ac.id\/id\/file\/1161177", "filesize": 1854624, "datasetid": "document" } ], "uri": "http:\/\/eprints.amikom.ac.id\/id\/document\/329754", "security": "validuser", "rev_number": 2, "formatdesc": "BAB IV", "content": "published", "language": "id", "main": "BAB IV.pdf", "format": "text", "docid": 329754, "pos": 5, "placement": 5, "eprintid": 33122, "mime_type": "application\/pdf" }, { "files": [ { "uri": "http:\/\/eprints.amikom.ac.id\/id\/file\/1161179", "filesize": 76958, "datasetid": "document", "hash_type": "MD5", "objectid": 329755, "mtime": "2026-10-02 06:44:46", "filename": "BAB V.pdf", "hash": "6d11af06560c705b4bafd12cde7639ed", "mime_type": "application\/pdf", "fileid": 1161179 } ], "uri": "http:\/\/eprints.amikom.ac.id\/id\/document\/329755", "security": "public", "rev_number": 2, "formatdesc": "BAB V", "language": "id", "content": "published", "main": "BAB V.pdf", "format": "text", "pos": 6, "docid": 329755, "placement": 6, "mime_type": "application\/pdf", "eprintid": 33122 }, { "eprintid": 33122, "mime_type": "application\/pdf", "placement": 7, "security": "validuser", "rev_number": 2, "formatdesc": "DAFTAR PUSTAKA - LAMPIRAN", "main": "Daftar Pustaka.pdf", "content": "published", "format": "text", "language": "id", "docid": 329756, "pos": 7, "files": [ { "hash": "741a00374518529830235116fc72986d", "fileid": 1161181, "mime_type": "application\/pdf", "mtime": "2026-10-02 06:44:49", "objectid": 329756, "hash_type": "MD5", "filename": "Daftar Pustaka.pdf", "filesize": 78990, "datasetid": "document", "uri": "http:\/\/eprints.amikom.ac.id\/id\/file\/1161181" } ], "uri": "http:\/\/eprints.amikom.ac.id\/id\/document\/329756" }, { "uri": "http:\/\/eprints.amikom.ac.id\/id\/document\/329757", "files": [ { "filename": "Sourcecode - 22.11.5029.zip", "mtime": "2026-10-02 06:44:53", "objectid": 329757, "hash_type": "MD5", "fileid": 1161183, "mime_type": "application\/zip", "hash": "080c1d5a96f16a09fff58c96def89e2d", "uri": "http:\/\/eprints.amikom.ac.id\/id\/file\/1161183", "datasetid": "document", "filesize": 115434 } ], "content": "published", "language": "id", "main": "Sourcecode - 22.11.5029.zip", "docid": 329757, "format": "archive", "pos": 8, "formatdesc": "SOURCE CODE", "security": "staffonly", "rev_number": 2, "placement": 8, "mime_type": "application\/zip", "eprintid": 33122 }, { "placement": 9, "eprintid": 33122, "mime_type": "application\/pdf", "files": [ { "filename": "Publikasi.pdf", "hash_type": "MD5", "objectid": 329758, "mtime": "2026-10-02 06:44:56", "mime_type": "application\/pdf", "fileid": 1161185, "hash": "5a1a53640e560f21cfb5e5d8d056aef7", "uri": "http:\/\/eprints.amikom.ac.id\/id\/file\/1161185", "datasetid": "document", "filesize": 476524 } ], "uri": "http:\/\/eprints.amikom.ac.id\/id\/document\/329758", "content": "published", "main": "Publikasi.pdf", "language": "id", "pos": 9, "format": "text", "docid": 329758, "rev_number": 2, "security": "staffonly", "formatdesc": "PUBLIKASI" } ], "type": "thesis", "thesis_name": "skripsi", "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/33122", "datestamp": "2026-10-02 06:53:29", "full_text_status": "restricted", "divisions": [ "if" ], "institution": "Universitas AMIKOM Yogyakarta", "metadata_visibility": "show", "status_changed": "2026-10-02 06:53:29", "userid": 12, "subjects": [ "000.000.000A" ], "eprint_status": "archive", "date_type": "published", "abstract": "Industri telekomunikasi terus mengamati tingkat churn pelanggan karena\r\npelanggan berdampak pada penurunan pendapatan dan efektivitas upaya retensi\r\npelanggan. Dengan menggunakan algoritma machine learning berbasis data\r\ntabular, penelitian ini bertujuan untuk membangun model yang dapat memprediksi\r\ntingkat penurunan pelanggan. \r\nDataset pelanggan telekomunikasi mencakup fitur dan pola pembayaran\r\nseperti tenure, TotalCharges, MonthlyCharges, jenis kontrak, layanan internet dan\r\nmetode pembayaran, dengan pembagian data 80:20. Algoritma yang digunakan\r\nadalah Logistic Regression, Random Forest, Support Vector Machine (SVM),\r\nGradient Boosting dan XGBoost. Analisis model didukung oleh analisis calibration\r\ncurve, gain dan lift chart, serta ROC-AUC, accuracy, precision, dan recall. Features\r\nimportance dan permutation importance digunakan untuk penyesuaian threshold\r\ndan analisis interpretabilitas. \r\nHasil penelitian menunjukkan bahwa algoritma XGBoost menunjukkan\r\nkinerja terbaik dibandingkan algoritma lainnya dengan akurasi sebesar 80,27% dan\r\nROC-AUC sebesar 0,8415. Gradient Boosting menempati posisi kedua dengan\r\nakurasi sebesar 80,13% dan ROC-AUC sebesar 0,8411. Random Forest dan SVM\r\nmemiliki kinerja yang lebih rendah dibandingkan model Logistic Regression, yang\r\nmemiliki recall tertinggi sebesar 78,07% dan ROC-AUC sebesar 0,8412. Hasil ini\r\nmenunjukkan algoritma berbasis boosting lebih baik menemukan pola kompleks\r\ndalam data churn. \r\nSelain mengidentifikasi pelanggan yang berisiko berhenti berlangganan,\r\nteknik prediksi churn yang dihasilkan ini juga memberikan informasi mengenai\r\nfaktor-faktor yang memengaruhi churn. Untuk meningkatkan retensi pelanggan di\r\nsektor telekomunikasi, model ini dapat digunakan sebagai landasan dalam\r\npengambilan keputusan strategis.", "dir": "disk0\/00\/03\/31\/22", "rev_number": 15, "contributors": [ { "name": { "lineage": null, "honourific": null, "family": "Rahardi", "given": "Majid" } } ], "creators": [ { "nim": "22.11.5029", "name": { "lineage": null, "honourific": null, "family": "Hartono", "given": "Nasywa Febia" } } ], "date": "2026-05-18", "corp_creators": [ "Universitas AMIKOM Yogyakarta" ], "keywords": "churn pelanggan, machine learning, interpretabilitas model,\r\nthreshold, retensi pelanggan, customer churn, model interpretability,\r\ncustomer retention." }