{ "date_type": "completed", "creators": [ { "name": { "lineage": null, "given": "Anita", "family": "Fatmawati", "honourific": null }, "id": "NIM2213030025" } ], "contact_email": "digilib@unpkdr.ac.id", "department": "KODEPRODI57201#Sistem Informasi", "date": "2026-06-25", "divisions": [ "sch_med" ], "ispublished": "unpub", "institution": "Universitas Nusantara PGRI Kediri", "pages": 68, "full_text_status": "restricted", "dir": "disk0\/00\/02\/33\/82", "rev_number": 19, "abstract": "Pasar mobil bekas terus berkembang seiring dengan tingginya kebutuhan masyarakat akan kendaraan dengan harga yang lebih terjangkau. Namun, banyaknya transaksi pada platform jual beli menyebabkan variasi harga antarpenjual, sehingga sulit menentukan harga yang tepat. Sebagian besar penelitian sebelumnya hanya menghasilkan prediksi titik tanpa menyertakan informasi ketidakpastian. Oleh karena itu, penelitian ini mengimplementasikan algoritma QRF untuk membangun model yang mampu menghasilkan prediksi harga sekaligus interval prediksi. \r\nPenelitian ini bertujuan untuk menilai kinerja dari algoritma QRF dalam memprediksi harga mobil Audi bekas serta menilai keandalan interval prediksi yang dihasilkan. Penelitian ini dilakukan dengan mengacu pada kerangka CRISP – DM yang meliputi tahap Business Understanding hingga Deployment. Dataset yang digunakan berasal dari Kaggle dengan judul 100,000 UK Used Car Dataset, khususnya subset mobil Audi sebanyak 10.668 data dengan sembilan variabel. Kinerja model dievaluasi menggunakan metrik R², MAE, dan RMSE untuk menilai prediksi titik, serta CP untuk mengukur keandalan interval prediksi.\r\nHasil penelitian menunjukkan bahwa model tuning lebih unggul dibandingkan dengan model baseline, khususnya pada keandalan interval prediksi yang. Model baseline memiliki R² sebesar 0,9569, MAE sebesar 0,0231, RMSE sebesar 0,0327, dan CP sebesar 0,7495, sedangkan model tuning menghasilkan R² sebesar 0,9480, MAE sebesar 0,0253, RMSE sebesar 0,0359, dan CP sebesar 0,9819 yang melampaui batas kepercayaan 95%. Meskipun akurasi prediksi titik sedikit lebih rendah, model tuning tetap dipilih sebagai model akhir karena mampu menghasilkan interval prediksi yang dapat mencakup 98,19% nilai aktual. \r\nSecara keseluruhan, hasil dari penelitian ini menunjukkan bahwa QRF mampu menghasilkan prediksi harga yang akurat dan interval prediksi yang andal, sehingga dapat mengatasi keterbatasan penelitian terdahulu yang hanya menghasilkan prediksi titik. Model diimplementasikan melalui antarmuka berbasis Streamlit yang dapat diakses secara publik. Namun, penelitian ini masih terbatas pada penggunaan data mobil Audi di Inggris Raya tahun 2002 – 2020, sehingga belum dapat digeneralisasi ke merek atau pasar lain tanpa pelatihan ulang. Pengembangan selanjutnya dapat dilakukan dengan memperluas dataset, menambahkan metrik Interval Width, dan membandingkan QRF dengan metode berbasis kuantil lainnya.", "type": "thesis", "status_changed": "2026-07-12 11:16:38", "subjects": [ 122, 459, 461, 473 ], "metadata_visibility": "show", "documents": [ { "language": "id", "files": [ { "hash_type": "MD5", "datasetid": "document", "fileid": 444413, "filesize": 897034, "mime_type": "application\/pdf", "filename": "RAMA_57201_2213030025.pdf", "mtime": "2026-07-12 10:39:09", "hash": "72f190a81fadc544a69c81c6dbce4d83", "uri": "http:\/\/repository.unpkediri.ac.id\/id\/file\/444413", "objectid": 198287 } ], "pos": 1, "formatdesc": "Full text", "mime_type": "application\/pdf", "placement": 1, "content": "accepted", "format": "text", "rev_number": 2, "eprintid": 23382, "security": "validuser", "main": "RAMA_57201_2213030025.pdf", "docid": 198287, "license": "cc_public_domain", "uri": 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"References", "mime_type": "application\/pdf", "placement": 8, "content": "bibliography", "format": "text", "rev_number": 2, "eprintid": 23382, "security": "public", "main": "RAMA_57201_2213030025_0712108103_0713018402_06_ref.pdf", "docid": 198303, "license": "cc_public_domain", "uri": "http:\/\/repository.unpkediri.ac.id\/id\/document\/198303" }, { "language": "id", "files": [ { "hash_type": "MD5", "datasetid": "document", "fileid": 444450, "filesize": 213487, "mime_type": "application\/pdf", "filename": "RAMA_57201_2213030025_0712108103_0713018402_07_lamp.pdf", "mtime": "2026-07-12 10:49:10", "hash": "f9a183d39bd993b61626987b002a3df4", "uri": "http:\/\/repository.unpkediri.ac.id\/id\/file\/444450", "objectid": 198305 } ], "pos": 9, "formatdesc": "Lampiran", "mime_type": "application\/pdf", "placement": 9, "content": "accepted", "format": "text", "rev_number": 2, "eprintid": 23382, "security": "validuser", "main": "RAMA_57201_2213030025_0712108103_0713018402_07_lamp.pdf", "docid": 198305, "license": "cc_public_domain", "uri": "http:\/\/repository.unpkediri.ac.id\/id\/document\/198305" } ], "uri": "http:\/\/repository.unpkediri.ac.id\/id\/eprint\/23382", "thesis_type": "other", "keywords": "Quantile Regression Forest, Prediksi Harga, Mobil Bekas, Interval Prediksi, Coverage Probability", "eprint_status": "archive", "contributors": [ { "name": { "lineage": null, "given": "Arie", "family": "Nugroho", "honourific": null }, "type": "http:\/\/www.loc.gov\/loc.terms\/relators\/THS", "id": "NIDN0712108103" }, { "name": { "lineage": null, "given": "Anita Sari", "family": "Wardani", "honourific": null }, "type": "http:\/\/www.loc.gov\/loc.terms\/relators\/THS", "id": "NIDN0713018402" } ], "title": "IMPLEMENTASI ALGORITMA QUANTILE REGRESSION FOREST (QRF) UNTUK PREDIKSI HARGA MOBIL AUDI BEKAS BERDASARKAN INTERVAL PREDIKSI DAN TINGKAT KEANDALAN MODEL", "thesis_name": "dphil", "userid": 65152, "eprintid": 23382, "lastmod": "2026-07-12 11:16:38", "referencetext": "Achmad 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