Rancang Bangun Rekomendasi Tempat Wisata Di Kabupaten Rembang Berbasis Website Menggunakan Metode Content Based Filtering
Bibliographic record
Abstract
Kabupaten Rembang merupakan salah satu daerah yang berada di wilayah Jawa Tengah bagian utara Kabuaten Rembang juga memiliki banyak objek wisata, kuliner, rumah adat dan sebagainya. Tempat wisata di kawasan Kabupaten Rembang sangat beragam .Oleh karena itu penulis akan membuat sistem rekomendasi tempat wisata berbasis website di Kabupaten Rembang. Sistem rekomendasi ini bertujuan untuk membantu wisatawan mendapatkan informasi tempat-tempat wisata yang berada di Kabupaten Rembang dan sekitarnya. Dengan menggunakan metode Content-based Filtering, sistem akan melihat tempat wisata yang wisatawan pilih sebelumnya dan memberikan rekomendasi tempat wisata menggunakan metode tersebut. Sistem yang dibuat juga telah dilakukan uji coba menggunakan black box testing dan pengujian usability dari 24 responden dan menghasilkan nilai keseluruhan 89%. Dengan adanya sistem ini diharapkan dapat membantu wisatawan untuk menentukan tempat wisata lebih cepat dan akurat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.026 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".