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Sistem Rekomendasi Program Studi Berdasarkan Preferensi dan Kemampuan Akademik Menggunakan Metode Knowledge Based Filltering Reza Pradana, Areta; Pratiwi, Niken; Kurnia Sari, Vena; Fajrin, Shoffia; Atina, Vihi
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2025
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/7jz6mr78

Abstract

Banyak siswa mengalami kebingungan dalam menentukan jurusan yang sesuai dengan minat dan kemampuan akademik, yang sering kali berujung pada ketidaksesuaian studi dan rendahnya motivasi belajar. Penelitian ini bertujuan untuk membangun sistem rekomendasi program studi yang dapat memberikan saran jurusan berdasarkan nilai rapor dan minat siswa dengan pendekatan knowledge-based recommendation. Data yang digunakan berupa 20 sampel program studi dengan atribut seperti deskripsi, nilai minimum, minat studi. Siswa memberikan input berupa nilai rapor dan minat studi yang kemudian diproses oleh sistem untuk menghasilkan rekomendasi prodi yang paling sesuai. Hasil penelitian menunjukkan bahwa sistem mampu memberikan rekomendasi yang relevan meskipun tanpa riwayat interaksi sebelumnya (cold-start), dan dapat menjadi alat bantu pengambilan keputusan yang bersifat personal dan objektif bagi siswa. Dengan pendekatan ini, diharapkan kesalahan dalam pemilihan jurusan dapat diminimalisir.
Sistem Rekomendasi Resep Masakan Menggunakan Metode Content Based Filtering Berdasarkan Preferensi Pengguna Kurnia Sari, Vena; Hartanti, Dwi; Purwanto, Eko
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30625

Abstract

Food is a fundamental human need that plays a crucial role in supporting health. For housewives, cooking is a daily routine in preparing nutritious meals for their families. In the digital era, technology simplifies recipe searching, but the abundance of available recipes often makes it difficult for users to find ones that match their available ingredients. To address this issue, this study develops a recipe recommendation system based on content-based filtering. The dataset was collected from cookpad.com and includes recipe names, ingredients, cooking duration, and cooking instructions. The system is designed using a content-based filtering approach and cosine similarity to measure the relevance between user input and existing recipe data. Users simply input ingredients, seasonings, or types of dishes they have, and the system provides relevant recommendations. Testing results show that the system can accurately identify and recommend recipes based on keywords, including specific cases such as "Soto Ayam Bening." The recommendations generated are reasonably accurate and assist users in selecting the most suitable recipes according to their needs. This system is expected to offer a practical solution for users in determining appropriate meal choices based on the ingredients available at home