{ "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/31837", "date": "2025-12-19", "creators": [ { "nim": "21.83.0697", "name": { "family": "Muchsin", "honourific": null, "lineage": null, "given": "Muhammad" } } ], "date_type": "published", "keywords": "Kopi, Sistem Rekomendasi, Ekspresi Wajah, Deep Learning,\r\nFER2013, Coffee, Recommendation System, Facial Expression.", "metadata_visibility": "show", "department": "Fakultas Ilmu Komputer", "title": "SISTEM REKOMENDASI MINUMAN KOPI BERDASARKAN\r\nEKSPRESI WAJAH MENGGUNAKAN DEEP LEARNING", "rev_number": 15, "eprint_status": "archive", "abstract": "Kopi merupakan salah satu minuman yang sangat populer dan memiliki\r\ntempat khusus dalam kehidupan sehari-hari masyarakat. Setiap orang memiliki\r\npreferensi berbeda dalam memilih kopi, dan sering kali pilihan tersebut\r\ndipengaruhi oleh suasana hati. Kondisi ini mendorong munculnya gagasan untuk\r\nmembangun sistem rekomendasi kopi yang mampu memberikan saran minuman\r\nberdasarkan emosi pengguna. Pada penelitian ini dikembangkan sebuah prototipe\r\nsistem rekomendasi minuman kopi berbasis ekspresi wajah menggunakan deep\r\nlearning. Sistem ini bekerja dengan memanfaatkan Facial Expression Recognition\r\n(FER) untuk mengenali tiga kategori emosi utama, yaitu senang, sedih, dan netral.\r\nDataset yang digunakan adalah FER2013, dan model dilatih dengan metode\r\nConvolutional Neural Network (CNN). Hasil deteksi emosi dipetakan ke dalam\r\nkategori rasa kopi: manis untuk emosi senang, pahit untuk emosi sedih, dan asam\r\nuntuk emosi netral.\r\nPengujian sistem dilakukan secara terbatas, yakni hanya pada penulis\r\nsendiri sebagai pengguna tunggal, sehingga penelitian ini lebih berfokus pada\r\nvalidasi konsep dan uji coba teknis sistem. Hasil uji coba menunjukkan bahwa\r\nsistem mampu mendeteksi emosi secara real-time dan memberikan rekomendasi\r\nkopi sesuai dengan pemetaan yang telah ditentukan. Penelitian ini diharapkan\r\ndapat menjadi dasar untuk pengembangan lebih lanjut, misalnya dengan pengujian\r\npada responden yang lebih luas agar akurasi dan pengalaman pengguna dapat\r\ndinilai secara menyeluruh.", "lastmod": "2026-07-01 02:03:44", "ispublished": "pub", "thesis_type": "bachelor", "userid": 12, "type": "thesis", "corp_creators": [ "Universitas AMIKOM Yogyakarta" ], "contributors": [ { "name": { "lineage": null, "given": "Dony", "honourific": null, "family": "Ariyus" } } ], "divisions": [ "tk" ], "dir": "disk0\/00\/03\/18\/37", "full_text_status": "restricted", "documents": [ { "formatdesc": "COVER-ABSTRAK", "content": "published", "format": "text", "main": "COVER.pdf", "rev_number": 2, "mime_type": "application\/pdf", "pos": 1, "eprintid": 31837, "docid": 319828, "placement": 1, "language": "id", "files": [ { "filesize": 807516, "objectid": 319828, "uri": "http:\/\/eprints.amikom.ac.id\/id\/file\/1132306", "fileid": 1132306, "hash_type": "MD5", "filename": 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